# EEG studies with EEGLAB

**Created:** 11/2/2026 1:40:29  
**Updated:** 11/2/2026 16:52:55  
**Exported:** 11/2/2026 17:20:00  
**Link:** [https://claude.ai/chat/3975a845-90f5-4769-b487-3574fbb52200?artifact=4c442695-075c-4c67-8b6b-d4154af474cd](https://claude.ai/chat/3975a845-90f5-4769-b487-3574fbb52200?artifact=4c442695-075c-4c67-8b6b-d4154af474cd)  

## User:

> 11/2/2026 1:40:29

Hi claude. Let’s try a (first?) session EEG Studies 1.  I always wanted to hack on scott makeig’s(sp?) eeglab in matlab. Can you find one or more public EEG studies and make some ERSPs and ERPs?

## Assistant:

> 11/2/2026 1:41:53

> Checking which data sources the network allows.
>
> - Checking which data sources the network allows.
> - **Done**

I'll pull EEGLAB's own public sample study from GitHub, then compute ERPs and ERSPs on it.

> Checking the EEG signal patterns against expected responses.
>
> - Command failed
> - Ran a command
> - Setting up a Python tool to read the EEG files.
> - Ran a command
> - Ran a command
> - Checking raw event fields for epoch position info.
> - Ran a command
> - Created a file
> - Command failed
> - Ran a command
> - Ran a command
> - Read a file
> - Read a file
> - Checking the EEG signal patterns against expected responses.
> - Shared files
> - **Done**

Session 1 is done. The data is Makeig's own: EEGLAB's tutorial recording (`sample_data/eeglab_data.set` in the sccn/eeglab GitHub repo). It has 32 channels. A square flashes at one of two positions, 40 trials each, and the subject responds to targets.

There's no MATLAB or Octave in this sandbox, so I read the `.set` file with Python/MNE. The math is the same as EEGLAB's `pop_erpimage` and `newtimef`.

What showed up, from looking at the plots:
- **ERP** (`erp_by_position.png`): a big positivity around 350–400 ms at Fz and Cz, with a matching negativity at Oz. Position 2 runs a little later.
- **ERSP** (`ersp_by_position.png`):
  - At Cz, beta power (~22 Hz) drops after the stimulus, then rebounds around 1 s. That pattern usually means the motor system is preparing the button press and then resetting.
  - Theta/alpha power rises at about 300–600 ms.
- I didn't run significance testing, so treat these as patterns to probe, not findings. The script has a comment where EEGLAB's bootstrap mask (`'alpha', 0.01`) would go.

For when you're in MATLAB, here are the matching calls to diff against:
```matlab
EEG = pop_loadset('eeglab_data.set');           % same file
EEG = pop_epoch(EEG, {'square'}, [-1 2]);
EEG = pop_rmbase(EEG, [-1000 0]);
pop_newtimef(EEG, 1, 14, [-1000 1990], [3 0.5], 'freqs', [3 40]);  % chan 14 = Cz, cycles [3 0.5] like mine
```

If you want a session 2, OpenNeuro has full multi-subject BIDS studies. ds002718 is Wakeman–Henson faces, which is also the dataset the EEGLAB STUDY tutorial uses.

## User:

> 11/2/2026 1:53:07

By studies 1 i mean this whole conversation.

* can you reference the architecture copypasta conversation per library-first design here? The initial layout gives the impression of there being a lot of code in classes oriented to this use rather than reuse. I’m interested in generalizing across the use of erp, ersp, and ica alongside different kinds of events, kind of, but you may not know what i really want in a situation yet, so try to be agile
* Can you make it clear in the charts: how many recordings there are and all the dimensions of that (how many participants, how many channels and which ones, how many behavior classes, etc), what the participants were told to do, what events are being summed, and show all the processed recordings alongside each other in sorted order like in makeig’s old papers, so human intuition can combine them too?
* Has this data passed through ica for artefact removal or any other purpose?
* Yeah let’s add another data source

## Assistant:

> 11/2/2026 2:04:00

> Checking an ICA component's blink pattern.
>
> - Searched past chats
> - Read memory
> - Designing the event selection and measure modules.
> - Ran a command
> - Checking PyPI packages that might bundle EEG data.
> - Command failed
> - Ran a command
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> - Sorting the marker labels against the image file names.
> - Ran a command
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> - Checking how the script's markers map to trial types.
> - Ran a command
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> - Planning the study design and the Muse loader.
> - Ran a command
> - Sorting out the electrode positions for the library.
> - Added a task
> - Added a task
> - Added a task
> - Created a file
> - Created a file
> - Created a file
> - Weighing whether to rebuild the ICA mixing matrix.
> - Edited a file
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> - Ran a command
> - Planning the three sheet layouts for the figures.
> - Edited a file
> - Edited a file
> - Created a file
> - Created a file
> - Adding an equations line to the card.
> - Edited a file
> - Created a file
> - Adding a stored-ICA loader method for continuous data.
> - Edited a file
> - Checking the epoch data against the continuous recording.
> - Ran a command
> - Ran a command
> - Created a file
> - Replacing an awkward display line and a clumsy class-copying hack.
> - Edited a file
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> - Ran a command
> - Deciding how to snap the sample rate to nominal.
> - Ran a command
> - Edited a file
> - Read a file
> - Checking whether the common reference explains the noise.
> - Edited a file
> - Read a file
> - Comparing ERP components across the two conditions.
> - Ran a command
> - Read a file
> - Read a file
> - Checking the ICA sheet's card overlap and height.
> - Ran a command
> - Read a file
> - Read a file
> - Splitting the card and plot into subfigures.
> - Ran a command
> - Read a file
> - Read a file
> - Recalculating the per-line spacing for the card subfigure.
> - Ran a command
> - Read a file
> - Checking the ICA metadata and wrapping up the repository.
> - Ran a command
> - Updated a task
> - Updated a task
> - Updated a task
> - Shared files
> - Checking an ICA component's blink pattern.
> - **Done**

I applied the "library-first" rule from the Gridstar architecture chat. All the general code now lives in an `eegkit/` library. The two studies are just data: each one is a loader plus a `StudyDesign` (conditions as event queries, time windows, sort fields). Both datasets run through the same pipeline, and neither has any study-specific code paths.

**How it generalizes**
- Events are rows with arbitrary fields, and a condition is just a query on them (`type=square & position=1`). A new kind of event therefore needs no new code.
- ICA is treated as a change of signal space. `epochs.in_space(ica)` swaps channels for components, so ERP, ERSP and ERP-image all run on components unchanged.
- There are 5 tests on synthetic data with known answers. They check that:
  - the ERP recovers the peak I planted;
  - the ERSP sees a random-phase burst that the ERP averages away;
  - ICA unmixes known sources.
- 1128 lines in total, in two commits (library, then studies), in `eeg_studies_1.bundle`.

**Charts.** Every sheet starts with a header card that is generated from the data itself. It shows:
- the source and what participants were told to do;
- the number of recordings, participants and sessions, plus total minutes;
- every channel by name;
- each condition with its event query and trial counts;
- each processing step and the ICA status;
- the equations written as code.

Below the card are Makeig-style ERP images: one row per trial, sorted by response time with the RT curve drawn on top. At Fz, the positivity rides just ahead of the button press. For Muse there is also one row per recording, grouped by participant.

**Has the tutorial data been through ICA?** Nothing has been removed from it. I checked rather than assumed:
- The continuous file stores no ICA.
- `eeglab_data_epochs_ica.set` does hold Infomax weights computed on the same trials (the samples are identical up to a baseline offset), but they were never applied.
- Both files are full rank, which is what you'd expect if no components had been subtracted.

I attached those weights for the ICA sheet. My guess is that IC3, with its frontal-pole scalp map and sporadic big deflections, is a blink component worth removing. That's an inference from the plots, not something the dataset states.

**New data source.** I added the NeuroTechX visual P300 oddball: 5 participants, 45 recordings, a 4-channel Muse headband, 89 minutes. It came from GitHub, which is the only host this sandbox can reach.
- The data quality is honest-to-consumer-hardware. Rejecting trials above 100 µV peak-to-peak dropped 1791 of 8610 trials, and participant P4 is left with only 8 target trials.
- The target effect shows up as a sharper ~330 ms negativity at TP9/TP10 rather than a textbook parietal P300, since the Muse has no parietal electrodes.

**Spaghetti watch.** Two places bend the design:
1. The per-recording stack only renders when there is more than one recording.
2. The tutorial's ICA comes from a sibling file.

Both are flagged in the code. ICA for Muse isn't fitted yet: `IcaDecomposition.fitted()` exists, but with only 4 channels it would yield just 4 components, so I left it for you to decide.

## User:

> 11/2/2026 2:07:37

Ok hmmm it’s nice to see more data but pretty busy and quickly makes it clear that interactivity would be helpful. Can you make it so i can click around in your interface to open up what information i want to see and focus on it?
When i saw the data was bandpass preprocessed i wanted to see what it would like without the bandpass, although that might not usually be available, maybe add a note to that effect

## Assistant:

> 11/2/2026 2:26:33

> Starting the preprocessing module implementation.
>
> - Shrinking the embedded data to fit the size limit.
> - Loaded a skill
> - Starting the preprocessing module implementation.
> - Created a file
> - **Done**

Now update the design to take a preprocessing variant.

> - Ran a command
> - Checking the raw recording source and its baseline offset.
> - Ran a command
> - Restructuring the card so its details come back as structured sections.
> - Read a file
> - Ran a command
> - **Done**

Now the exporter that precomputes everything the viewer shows.

> - Created a file
> - Adding a with_extra_signals method to Epochs.
> - Ran a command
> - Restructuring the power computation to reuse per-condition results.
> - Ran a command
> - Loaded tools
> - Stopped a task
> - Timing the eeglab ERSP computation.
> - Ran a command
> - Estimating the ERSP computation cost across conditions.
> - Ran a command
> - Laying out the dataset card and component cards.
> - Created a file
> - Checking electrode positions and labels in the study picker.
> - Ran a command
> - Checking whether node is available for a syntax check.
> - Ran a command
> - Ran a command
> - Read a file
> - Reviewing the component label ordering next.
> - Ran a command
> - Used artifacts
> - Ran a command
> - **Done**

The **EEG Studies 1** page lets you click through everything:

- **Dataset and preprocessing:** pick either dataset, then switch between **band-pass** and **as distributed**.
- **Channels:** each one is drawn on a scalp map with its ERP trace. Click a channel to focus it.
- **ICA components:** for the EEGLAB data these appear as clickable scalp-map tiles.
- **One focused plot:** choose ERP, ERP image, ERSP, or per-recording rows, then filter by condition and participant. "Overlay each participant" draws thin per-person ERPs on top of the average.
- **Hover:** read exact values, including the trial row, the participant, and the response time on the sorted ERP image.
- **Dataset card:** collapsed to a one-line summary until you open it.

**About "without the bandpass":** the page puts a caveat right under that toggle, because "unfiltered here" isn't the same as raw.
- **EEGLAB tutorial:** the file is 128 Hz, so it was already low-pass filtered and downsampled before release. Its trials were also cut out and spliced together. The original acquisition data isn't published, which is common.
- **Muse:** close to raw. The CSVs are what the headset streamed. Any firmware-side filtering isn't documented, and this view rejects no trials, so drift and movement artifacts stay in.

**Code changes:**
- Preprocessing is now a swappable `Preprocessing` variant rather than baked into each source, so any study can be viewed under any number of variants.
- The viewer is generic: `StudyExport` precomputes everything into packed arrays and `ViewerPage` embeds it. A new dataset shows up in the viewer without touching the page.
- `eeg_studies_1.bundle` is updated with a third commit.

The page is about 10 MB because every view is precomputed, so it may take a moment to load on a phone.

## User:

> 11/2/2026 2:42:17

Glancing through last results:

* it’s not that interesting if it’s sorted by participant id or such, it should be sorted by something that produces visual similarity or has inherent meaning, like time of highest peak, or average time*energy, or response time. We’re of course interested in aligning events and performing erp/ersp/ica as well as sorting by them
* Generalization: it should be fine to stack epochs or events rather than recordings
* Data: i guess i might be interested at looking at a dataset with highest samplerate and total recordings*channels*time duration (total number of samples). Maybe just highest total bits of uncompressed signal. I understand it’s hard to find datasets just on github — is there a bundle easy for me to download that would help, or a plugin i could enable? But probably it’s fine to just look closer at the biggest one you have.
* Data summary field: it would be nice to have grand total sample count in the data precalculated in this field
* I’m not sure what “per participant” means in conditions
* Working with muse data, if you can access the original recordings, the headband protocol has different hardware bandpass and signal density modes that would be good to document

Moving on to your interactive creation:

* Initial impression, it’s cool to see the electrode positions and an erp for each, but i’d like to have a more generalization-oriented view where scalp location is just one of many properties i can spread things over, and i can combine signals across different dimensions
* I’m on an ipad this time so the overview is initially a small window on the right, might be useful for ui design choices.
* I’m experiencing a scrolling bug where when i scroll up to change dataset it rubberbands back down hiding the header when i let go. Is there a standard workaround for that ios safari bug?
* Ica waveforms should be visible adjacent to each other in the same interface as the scalp position erps, just spread over (eventually choosable) property like variance rather than position
* The erp image view is cool! Thats the stuff science needed 2 decades ago
* The erp and ersp charts could benefit from a difference plot to show the contribution of the condition
* The ersp would ideally have an image view like erp, maybe by removing time or giving a time control
* hovering could benefit from axis coordinate locators, low priority

Reading your comments:

* if the muse data looks high value it might be useful to see if the sample depth and rate can be discerned from the csv
* if it makes sense to precompute some things with python before showing the renderer feel free here, or alternatively to load it all on my end; this is an ipad pro 4th gen model number MXAT2LL/A running safari on ipados 18.7.7

## Assistant:

> 11/2/2026 3:04:35

> Deciding how to cite search sources.
>
> - Planning the viewer's sorting, stacking and layout features.
> - Ran a command
> - Stopped a task
> - Working out how to fetch the data folder.
> - Ran a command
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> - Checking the trial and cue counts in the recording.
> - Ran a command
> - Aligning trials to markers using codes and timing.
> - Ran a command
> - Matching the EEG markers to behavior trials by offset.
> - Command failed
> - Searched the web
> - Working out the ADC scaling for the EEG signal.
> - Read a page
> - Laying out the overview and selection controls.
> - Loaded tools
> - Searched connector directory
> - Writing the Python alignment and event code first.
> - Created a file
> - Ran a command
> - Ran a command
> - Deciding which Muse channels to keep.
> - Ran a command
> - Ran a command
> - Designing the sorting and rows for the viewer.
> - Couldn't create the file
> - Ran a command
> - Ran a command
> - Created a file
> - Ran a command
> - Checking whether the test suite passes.
> - Ran a command
> - Deciding to compute the overview live from loaded signals.
> - Couldn't create the file
> - Ran a command
> - Read a file
> - Created a file
> - Removing stray fill lines and rebuilding the site template.
> - Ran a command
> - Planning a parity test between node and python.
> - Ran a command
> - Checking the speed of the timing-analysis run.
> - Ran a command
> - Read a file
> - Deciding whether to exclude fields used in conditions.
> - Ran a command
> - Gathering the file list for the Artifact call.
> - Ran a command
> - Deciding whether to publish a new artifact or update the old one.
> - Couldn't use artifacts
> - Weighing PNG packing against canvas color handling.
> - Ran a command
> - Weighing a leaner file layout for the paste.
> - Couldn't use artifacts
> - Settling on a multichannel WAV layout for the signals.
> - Ran a command
> - Ran a command
> - Packing the signal files into multichannel audio.
> - Ran a command
> - Ran a command
> - Used artifacts
> - Used artifacts
> - Used artifacts
> - Used artifacts
> - Sorting out the dataset hosting options.
> - Ran a command
> - Shared files
> - Deciding how to cite search sources.
> - **Done**

I updated the viewer in place. It now ships single trials, and the browser does all the sorting, grouping, filtering and time-frequency math itself.

**Your science points**
- **Sorting.** Rows can be sorted by:
  - response or overlay time;
  - latency of the largest positive or negative peak;
  - energy-weighted mean time, Σt·x²/Σx²;
  - mean amplitude, peak-to-peak, or band power;
  - recording order.

  All of these are measured inside an editable window. When the sort key is a latency, that latency is drawn as a curve over the image.
- **Time zero.** You can choose what the epochs are locked to: stimulus onset, button press (EEGLAB, so you can see Makeig's response-locked view), or cue onset (cueing study).
- **Stacking.** Rows can be single trials, or averages per recording, per session or per participant. Trial fields like *tilt*, *correct* and *target side* appear as filters automatically. Artifact rejection is now a live threshold rather than a fixed preprocessing step.
- **Biggest dataset.** I added a Muse spatial-cueing (Posner) task: 129 recordings, 65 participants, 31.4M samples. That's about 6× the P300 set, and it has per-trial reaction times.
  - Its EEG markers usually start partway into the session, so I matched them to the behaviour log. Marker codes must match exactly, and the gaps between markers predicted from the logged reaction times line up to within about 156 ms RMS. That's about what the task's random timing jitter alone would produce, which supports the match.
- **"Per participant."** That wording was unclear. The card now says, for example, "each participant contributed 0–83 of these (median 39)". It also shows grand totals of samples and uncompressed bits.
- **Muse hardware.** The CSVs don't record a firmware preset or the hardware filter settings. What I could measure from the files:
  - every value sits on a 1000/2048 µV grid spanning ±1000 µV, which means a 12-bit ADC;
  - the sample rate is 256 Hz;
  - there's a fifth "Right AUX" input, usually unconnected, which I left out.

  The loader now checks the 12-bit grid on every file. Two files with broken timestamps (24 Hz and 37 Hz) are skipped, and the reason is logged.
- **Getting bigger data.** This sandbox can only reach GitHub and package registries. OpenNeuro, PhysioNet and Zenodo are all blocked. A Hugging Face connector exists, but downloads would still hit that block. Practically, a GitHub repo containing the data is the easiest path in.

**Interface**
- **Signal array.** Channels and ICA components share one array. You can spread them by scalp position, variance rank, peak latency, peak amplitude, RMS, name, or a latency × amplitude scatter. "Combine signals" averages whichever cells you tap into one region of interest.
- **Comparisons.** ERP shows an A − B difference wave with its error band. ERSP shows A, B, or A − B. There's a new ERSP image: single-trial power at one frequency, sorted the same way as the ERP image.
- **Crosshair.** It now has value tags on both axes. On touch screens, drag sideways to scrub.
- **iPad scroll bug.** The common fixes for this iOS behaviour are:
  - `overscroll-behavior: none` on html and body;
  - not setting `touch-action: none` on large elements, since that stops you scrolling when you start a drag on them;
  - avoiding `vh` heights, which change as the Safari toolbar slides in and out;
  - not re-rendering on height-only resizes.

  I applied all four. I can't test on your iPad, so tell me if it still snaps back.
- **Python ↔ browser check.** The page recomputes Python's reference values every time it loads data. On every dataset and view, the ERP agrees within 0.001 µV and the ERSP within 0.002 dB. ERSP over about 5,000 trials takes a few seconds and shows its progress.
- **Your iPad split view.** The layout drops to one column below 900 px.

**Caveats**
- Muse cueing ERPs at target onset are only about 1 µV across these four sites, so sorting is where any structure would show up.
- Signal files are 16-bit WAV, because that's the binary format the artifact host will serve.
- `eeg_studies_1.bundle` has the code.

## User:

> 11/2/2026 3:14:34

I got through the rubberbanding problem by scrolling in the outer dom element and have more comments on the first interactive view:

* why is there ica data for one set but not the other? Also what ica algorithm was used and what are the general processing parameters of each algorithm? Oh i see these are documented in matlab? notation at the bottom? Could each keyword and operator (what is | here?) show a definition or explanation if tapped?
* It’s cool to see the nose-up scalp view from other works
* Muse task: maybe document if the images do not move between trials (compared to the other task where they do)
* Unfiltered data shows high AC noise. The interface should let the user remove (or investigate) this with ICA in a way generalizable to other things
* Parameters of ersp should provide for seeing AC noise if it’s visible on an ersp (don’t remember if they remove independent signals)
* generally, i’d like to be able to construct processed data via erp/ersp/ica/labels&measures, possibly remove signals from the data they were identified in to look at weaker ones, then explore that processed data across all the dimensions. Eventually i imagine a large graph of processes and signals being automatically generated based on guessed utility, everything labeled so that multiple datasets could be processed in together.
* I’d also like to perform correlation of the data to propose novel events

Next version:
Well I really appreciate that it shows a clear error message when something goes wrong: Could not load data/catalog.json: data/catalog.json: HTTP 403 .  I think it might need help with its sandbox.
That’s exciting you found a big dataset! Could you ensure the code is flexible to load or add other popular formats from large repos?
Cool also you analyzed the muse protocol and identified the adc.  I would include the aux lead as it could aid noise separation.  Did you identify the preset? Is there accelerometer data or anything too?

## Assistant:

> 11/2/2026 3:27:44

> Wiring the component scalp maps and removal chips into the viewer.
>
> - Checking whether relative file URLs resolve differently in published artifacts.
> - Loaded a skill
> - Working out how the page's URL changes relative paths.
> - Searched files
> - Read a file
> - Specifying how the ICA sphering, the proposal step and the viewer controls fit together.
> - Ran a command
> - Planning the display of spectra and component signals.
> - Ran a command
> - Checking the default settings against the EEGLAB recipe.
> - Ran a command
> - Checking how AUX channels map to 2D positions.
> - Ran a command
> - Updating the source positions and rejection channels.
> - Ran a command
> - Finishing the export step for the tutorial.
> - Ran a command
> - Checking warnings about empty condition averages.
> - Ran a command
> - Wiring the component scalp maps and removal chips into the viewer.
> - Ran a command
> - **Done**

Now the viewer: data access fallback, derived components, component removal, spectrum view, ERSP options, glossary.

> Checking where BIDS stores electrode positions.
>
> - Ran a command
> - Cleaning up the sort and removing leftover hacks.
> - Ran a command
> - Ran a command
> - Checking which identifier matches first in the pattern.
> - Ran a command
> - Checking how annotation times line up with the recording start.
> - Ran a command
> - Ran a command
> - Checking where BIDS stores electrode positions.
> - Ran a command
> - **Done**

Now a full export, the browser-math parity check, and one look at the page.

> Comparing noise views and processing graph plans.
>
> - Ran a command
> - Checking the infomax convergence criterion against the iteration cap.
> - Ran a command
> - Deciding whether to keep iterating or report the stopping outcome.
> - Command failed
> - Ran a command
> - Ran a command
> - Read a file
> - Judging the scale of the component values.
> - Ran a command
> - Checking whether leftover old files fit the size limit.
> - Used artifacts
> - Used artifacts
> - Used artifacts
> - Used artifacts
> - Ran a command
> - Shared files
> - Comparing noise views and processing graph plans.
> - **Done**

The viewer is updated in place.

**The 403.** I couldn't reproduce it here: the page and its data load fine when I serve them. So I made two changes rather than one guessed fix:
- If the page can't fetch its data files, it now retries two other ways.
- If those fail too, it falls back to the EEGLAB tutorial, which is now embedded in the page itself, and shows a banner with the exact URL and the response for each attempt.

If you see that banner, its text tells me what the sandbox is blocking.

**Why only one dataset had ICA.** Only the EEGLAB file shipped with stored weights, computed by the dataset's authors. Now every dataset has a decomposition:
- **EEGLAB:** Infomax (`runica`). Its settings weren't saved in the file, and the card says so.
- **Muse:** extended Infomax (the algorithm behind runica, via MNE's implementation), fitted once across all recordings so components are comparable between people.
  - Data is sphered with `2·inv(sqrtm(cov))` and fitted on about 200k evenly spaced samples.
  - It stops when the weight change drops below 1e‑7, or after 512 iterations. It hit the 512 cap without meeting the stopping rule. The card shows all of these settings.

**Equation box.** Every underlined term can be tapped for a definition. The `|…|` bars are absolute value, so `|…|^2` is power. I removed the separate `|` that was being used between conditions, since it was ambiguous.

**Muse stimulus position.**
- **P300:** the photos always appear at screen centre.
- **Cueing:** the grating appears 10° left or right of fixation, so it moves between trials.

**Line noise.**
- **Why the ERSP hid it:** the ERSP divides by baseline power, so steady noise like mains hum cancels out.
- **Where to see it:** there's now a **Spectrum** view with 50 and 60 Hz marked, and an absolute-power ERSP option. Both can run up to about 115 Hz.
- **Removing it:** tap a component chip to subtract that component from the channels. Every view then recomputes on the cleaned data. It's the same mechanism for blinks, AUX noise, or anything else ICA isolates.
- **Unfiltered fit:** the "as distributed" variant fits its ICA on the unfiltered data, so mains noise is available to be separated out.

**Toward the processing graph and novel events.** Today's pieces:
- **Steps over a whole study:** a preprocessing variant can now include steps that need every recording at once, such as a shared ICA or event proposal.
- **Proposed events:** large excursions of the most frontal component become "proposed" events, with their own time zero, filters and sorting.
- **Template sort:** a new sort key ranks rows by correlation with the ERP.

Two things are missing:
- **Continuous-data search:** scanning the full recordings for template matches needs continuous data shipped to the browser, not just epochs.
- **Labelled graph:** a general graph of operations with labels that carry across datasets doesn't exist yet. The study steps above would become its nodes.

**Other formats.** Two new loaders, each tested by writing a synthetic dataset and reading it back in BrainVision and EDF:
- `MneRawLoader` reads anything MNE can open: EDF, BDF, BrainVision, FIF, CNT, and more.
- `BidsDatasetLoader` reads the BIDS layout that OpenNeuro and NEMAR use. Every column of `events.tsv` becomes an event field, and it reads task instructions and electrode positions.

**Muse hardware.**
- **AUX:** now included.
- **Preset:** I couldn't identify it; the CSVs don't record it.
- **Other sensors:** the files contain only EEG, AUX and markers: no accelerometer, gyroscope or PPG.

`eeg_studies_1.bundle` has 5 commits.

## User:

> 11/2/2026 3:46:53

Muse: did you find the model of the headset and the preset details for that model?
task: wowwww this data has a lot of noise on learning and belief formation :scary:
conditions: it doesn’t classify if the task was right or left??
ICA: i am still reading and exploring, but in your chat you said ICA is entirely preprocessed. That doesn’t sound very interesting. Would more options open up if the app had a background worker with progress output?
What does “combine signals” do?
It’s confusing that signal array shows mixed information ordered by sensor alongside mixed information ordered by guassian variance. This is two arrays that contain the same information but separated in different manners,  note: erps and ersps formed from the ica components have scalp information extractable with the mixing matrix (i think?) is that obvious or should we spend a few turns discussing it?
it’s a little useful that it lets one remove ica components from the channels but, just like the ica and scalp arrays, we want _generalization_ here

hmmmm it’s a little confusing. I may have spoken too quickly with some of the above. Process what i’ve send and maybe if we’re lucky i could try describing some example use experiences i’d like

## Assistant:

> 11/2/2026 3:47:32

> Working out which AUX stream the recorded data came from.
>
> - Checking muse-lsl source for the preset setting.
> - Ran a command
> - Ran a command
> - Working out which AUX stream the recorded data came from.
> - **Done**

Short answers first, then the unifying idea.

**Muse model and preset.**
- **Model:** the eeg-notebooks README says the recordings were made with a **Muse 2016**.
- **Preset:** none was recorded. The CSVs were captured with muse-lsl. For the 2016 headband, its code lists `p21` as the default (4 EEG channels) and `p20` as the alternative, and muse-lsl only sends a preset command if you ask it to.
  - The AUX column here holds live, varying values (std ≈ 32 µV). If muse-lsl's linked notes are right that `p20` is the preset that adds AUX, these sessions likely ran in `p20`. That's an inference: I couldn't open the notes from here.
- **Other sensors:** the headband has an accelerometer and gyroscope on separate Bluetooth channels. eeg-notebooks never saved them.

**Left/right isn't a condition.** In cueing it's currently a filter ("target side"), not a contrast. That's a design flaw: conditions are hard-coded per study. The general fix is to let you split on any trial field, so validity, side, tilt, correct, or their combinations can all act as A and B.

**ICA being preprocessed.** Yes, it's fitted in Python before the data ships. A worker running ICA in the browser, with progress, would open real options:
- fit on any subset you pick: one participant, one condition, already-cleaned data, or only high-noise trials;
- compare algorithms or seeds side by side;
- refit after removing components.

The data here are small enough to fit in seconds: 5 Muse channels, or 32 EEGLAB channels × 40 trials.

**"Combine signals."** It averages the selected signals trial by trial into one virtual signal, i.e. a region of interest.

**Your mixing-matrix intuition is right, and it's the unifying idea.** Everything here is linear, so:
- **One idea behind three features.** A component, an averaged region, and "channels minus IC3" are all the same thing: a weight vector applied to the channels.
- **Scalp maps for any component result.** Because the math is linear, `ERP_channels = mixing @ ERP_components`, and the same holds for any linear measure. A component's ERP times its mixing column is exactly its contribution to every channel's ERP. So any component view can be shown on the scalp, and any channel view can be broken into components.
- **ERSP is the exception.** Power isn't linear, so per-component ERSPs don't add up to channel ERSPs.

That suggests replacing the mixed array with **bases**: channels, ICA fits, your own filters. Each basis is laid out by its own properties. Any signal can be projected into any other basis. Removing components becomes one transform among many, and transforms chain, which is the start of your process graph.

I'd rather hear your example use experiences before building that, so the bases and graph match how you actually want to move through the data.

## User:

> 11/2/2026 4:10:48

* 40 trials? It’s showing 510 mins of data in the task dropdown (it is indeed strange that the scalp chart shows tiny waveforms, what are they? It should be clear what they are)
* combine signals: it should say it is averaging the selection across trials (although that parameter to average over should be controllable) — where does this data go and how is it different from the erp,ersp — or is that button what fills the other view?
* the scalp remixing sounds a little unintuitive but also a core concept and could use a learning view or such to understand. We want to do this across other labels than sensor id too.

The bases idea sounds great and is much closer to what i’m trying to consider.

Example use, removing line noise:

* shows data with line noise
* user selects ica transform
* user identifies component with line noise
* user transforms data with a subtraction of that component in either time or frequency space. This produces a further mode of interacting with the data where that noise is filtered out but is available as an additional channel.

Example use, investigating event:

* User selects a property time label in a visual way, such as in an erp (time domain mean) image over all trials or epochs
* user can time align the property and see its mean signal in either time (erp) or frequency (ersp)
* user considers the erp/ersp and decides to take ica over another property, such as participant id
* this produces another view of the data where each participant’s contribution to the erp or ersp is recombined into nongaussian separations
* user goes back to previous view and instead decides they want to consider a subevent that is near the original event but not quite time-aligned
* they remove the time-aligned erp from the recordings, producing a different array, and then either click the visible sign or use correlation to automatically realign the data on a large peak
* each time the user engages the data, they choose whether to work in frequency, time, or both together
* the user now has two erps, one labeled, and another correlated after removal of the labelled one. They plot a difference in scalp energy. The interface shows the equation used to calculate the plot next to each electrode.

something i’m thinking if here is a part slightly similar to ica in that it uses a feedback loop, where signals are correlated with the data with other signals subtracted from it, this is used to find likely time points the signals might be present, and then the signals are refined further with the new data, in parallel to break the data into instances of many waveforms at different times. Is this a technique with a name?

## Assistant:

> 11/2/2026 4:11:12

> Revising the scalp chart labels and averaging signals.
>
> - Revising the scalp chart labels and averaging signals.
> - **Done**

**The 40 trials** were the EEGLAB tutorial: 80 epochs, 40 per condition. The Muse cueing set is the 510-minute one.

**The tiny waveforms** on the scalp are each channel's ERP for condition A (or A − B if you pick that trace), drawn at the electrode's position on one shared µV scale. You're right that the chart should say that on itself, not in a hint line underneath.

**"Combine signals"** averages across *signals*, not trials. For each trial, sample by sample, it takes the mean of the channels you tapped. That one virtual signal then becomes the focused signal that ERP, ERSP and the images are computed from. So yes, it's what fills the other view.

You're right that "average over what" should be a choice. In the bases model, averaging is just collapsing one axis, and that axis could be signals, trials, participants or time.

**Your two use cases** map onto one rule: every transform produces both what it keeps and what it removes, and both stay explorable.
- **Line noise.** ICA isolates the noise component. You subtract it either in time (back-project and subtract) or in frequency (a notch, or spectral subtraction of that component). The noise becomes its own channel alongside the cleaned data. You've effectively reinvented "keep the residual"; that should be built in, not a special case.
- **Event investigation.** The steps you list are each one transform:
  - tapping a time on an ERP image creates a label;
  - realigning on it creates new epochs;
  - "ICA over participants" means unmixing along a different axis, such as a participants × time matrix of ERPs instead of channels × time;
  - subtracting the aligned ERP gives a residual;
  - correlating that residual creates new event times;
  - the per-electrode difference, with its equation shown, is a projection of the result back onto the scalp.

**Your feedback loop has names.**
- **Woody filter** (Woody 1967): correlate single trials with a template, realign each trial to its best lag, re-average, repeat. This is the "realign on a large peak by correlation" step.
- **RIDE**, residue iteration decomposition (Ouyang et al.): iteratively separates stimulus-locked, response-locked and latency-varying parts by subtracting each estimate from the data and re-estimating the others. It's very close to your second scenario.
- **Matching pursuit** (Mallat & Zhang 1993): repeatedly find the best-matching waveform at its best time, subtract it, repeat.
- **Convolutional sparse coding / shift-invariant dictionary learning**: matching pursuit plus learning the waveforms themselves, in parallel, which is exactly your "refine the signals with the new data." For EEG see adaptive waveform learning (Hitziger et al. 2017) and multivariate CSC (Dupré la Tour et al. 2018, the `alphacsc` package).
- **Regression deconvolution** (`unfold`, Ehinger & Dimigen 2019) does the same for known events that overlap in time.
- **Spike sorting** (e.g. Kilosort) is the same loop applied to neurons.

Before building, I'd sketch the core model in a few lines of code: a **signal set** has axes such as signals, trials and time; a **basis** is a set of weights on one axis; a **transform** maps a signal set to its kept and removed parts. You can then check it against both scenarios on paper.

## User:

> 11/2/2026 4:19:47

Build something; let me see, or move toward letting me see, a cutting edge approach for breaking this data into a description of component wavelet events spread across the scalps of the participants at different times.

would it make sense to hash out how spectral components might be removed? it will take me a little learning to understand the inner workings of your wavelet spectra. is removing a time-aligned spectral response a reasonable thing to do with existing approaches, or would it make sense to skip for now?

## Assistant:

> 11/2/2026 4:30:34

> - Writing the rank-one sparse coding learner and its test.
> - Created a file
> - Created a file
> - Ran a command
> - Revising the atom initialization to pick dissimilar windows.
> - Ran a command
> - Checking whether a shifted atom loses its edges.
> - Ran a command
> - Checking a sign error in the correlation score.
> - Ran a command
> - Ran a command
> - Planning how to add discovered waveforms to the study pipeline.
> - Ran a command
> - Checking how empty trial sets are handled in the ERSP computation.
> - Ran a command
> - Checking the zero-length epoch handling.
> - Ran a command
> - Checking the reset logic for the kinds filter.
> - Ran a command
> - Trimming the largest per-atom event sets.
> - Ran a command
> - Listing stale artifact files for removal.
> - Command failed
> - Ran a command
> - Ran a command
> - Read a file
> - Checking the histogram's peak timing against the target.
> - Used artifacts
> - Ran a command
> - Tallying file sizes across batches to stay under the limit.
> - Used artifacts
> - Used artifacts
> - Used artifacts
> - Used artifacts
> - Used artifacts
> - Ran a command
> - Shared files
> - **Done**

The viewer now has a **Discovered waveforms** panel. It's built on the cutting-edge version of your feedback loop: **multivariate convolutional sparse coding**, the same model as `alphacsc`. Each atom is one scalp pattern times one 500 ms waveform, and the recordings are explained as many timed, scaled copies of those atoms.

The fit alternates two steps, ten rounds each:
1. Matching pursuit finds copies and subtracts them from the data.
2. Each atom is re-learned from its own copies with the rest subtracted (a K-SVD update).

**Checked on synthetic data first.** I planted two atoms in noise. The learner recovered both shapes at 0.99 correlation, and found more than 80% of the planted times.

**What each atom card shows:**
- its scalp pattern, including AUX;
- its waveform, with its peak marked;
- how many copies were found, and the rate per minute for each participant;
- when copies occur relative to the nearest task event.

"Lock time to W3" turns that atom's copies into epochs. Every existing view (ERP image, ERSP, spectrum, filters) then works on them. "Use its scalp pattern" adds the pattern as a signal. ICA components and waveform patterns are now both treated as **bases** (sets of channel weights), and each basis gets its own array instead of being mixed with the channels.

**First look, as patterns to probe rather than findings:**
- **Muse cueing W1:** temporal sites opposite to frontal, slow, about 33 copies a minute. Its copies cluster roughly 0.3–0.5 s after target onset, which looks like an eye or movement response.
- **Frontal atoms:** several load on AF7, AF8 and AUX together. That suggests shared environmental noise, which is the reason to keep AUX.
- **P300 as distributed:** W1 and W2 are strongly locked to image onset.

**Known limits:**
- Each dictionary is learned from 600 s spread across all recordings.
- Some waveforms peak at the window edge, meaning the atom is shifted and partly cut off. Re-centering atoms after each update would fix this.
- Only the strongest 800 copies per atom become epochs, to stay under the host's 256 MB limit.

**Removing time-aligned spectral responses.** I'd skip it for now. Power has no phase, so it can't be subtracted from the signal itself. The existing approaches all do something adjacent instead:
- **Subtract the phase-locked part.** That's the ERP, or RIDE's per-trial-shifted version, and it's exact.
- **Rescale time-frequency amplitudes while keeping each trial's phase, then reconstruct.** This is Wiener-style masking. The reconstruction is approximate.
- **Remove stationary noise.** ZapLine (de Cheveigné 2020) is the current best tool for line noise: a spatial filter plus spectral removal.

Oscillatory bursts already appear as atoms here, so subtracting atom copies removes them in the time domain, exactly. That keeps the "kept vs removed" idea linear. The next step for that is a **Subtract this atom** action that keeps the removed part as its own signal.

`eeg_studies_1.bundle` has 6 commits.

## User:

> 11/2/2026 4:43:45

You said “Each dictionary is learned from 600 s spread across all recordings” — is this a limit of the approach (are there more general approaches that aren’t quite as new?) or a choice to manage resources?
You said “Only the strongest 800 copies per atom become epochs, to stay under the host’s 256 MB limit” — why do copies need memory? Subwaveforms should be referenced as data, offset, length. Is it because of how the algorithm functions?
Would it be helpful to add the background worker concept with a control panel to configure work?

Can you walk me through finding something that’s possibly eye motion (i’ve seen muse eye motion and it would be interesting to see both of an extracted waveform with spectrum, and original raw waveforms or spectra focused on the event time (i could ideally select the region and label it); and also finding something that’s possibly from the brain?

## Assistant:

> 11/2/2026 4:47:22

> Checking when the artifacts peak against the response timing.
>
> - Comparing eye-movement and brain-activity candidates for the cueing task.
> - Ran a command
> - Matching polarity across frontal and temporal sites to blink artifacts.
> - Ran a command
> - Checking the brain signal's timing and symmetry.
> - Created a file
> - Ran a command
> - Read a file
> - Checking when the artifacts peak against the response timing.
> - Shared files
> - Ran a command
> - **Done**

**600 s of learning data.** That was my choice, to keep the run quick. The method itself has no limit. Online dictionary learning (Mairal et al. 2009, and alphacsc's online mode) streams through all of the data in batches.

Older, more general relatives:
- **ICA / PCA:** spatial only, no time shape.
- **Matching pursuit over a fixed Gabor dictionary:** Durka's EEG work around 2001. It finds wavelet-like events but doesn't learn their shapes.
- **Template matching / k-means on segments:** shapes without scalp patterns.
- **NMF on spectrograms:** spectral patterns without phase.

**The 800-copy cap.** You're right; this isn't the algorithm's doing. It's my export design. Each event set ships its own epochs, so every alignment duplicates the same samples.

The better design is to ship each recording's continuous data once, about 78 MB for all of cueing at 16 bits. Then events are just (recording, offset, length) references, and any number of copies, alignments or labels costs almost nothing.

**Background worker with a control panel.** Yes, and it pairs naturally with continuous data. Once the browser holds the raw recordings, a worker can run ICA or waveform learning with settings you choose and show progress. It also makes "select a region and label it" possible, because a label is just a new event on the continuous timeline.

**The walkthrough** (`walkthrough_eye_brain.png`, one row each):

- **Eye candidate, cueing W1.** The scalp pattern has AF7 and AF8 the same sign and TP9/TP10 opposite, the usual blink or vertical eye pattern. It's a slow wave peaking near 2 Hz. Copies cluster 0.5–0.7 s after the target, right around the median reaction time of 0.52 s, so it's plausibly a blink after responding.
  - The strongest raw copies hit the ±1000 µV limit of the ADC. Those are headband movement, mixed into the same atom.
  - Horizontal saccades (AF7 and AF8 opposite in sign) barely appear. Left versus right targets differ by under 1 µV, so people mostly held fixation.
- **Brain candidate, P300 W2.** The pattern is symmetric at TP9/TP10 with a ~2 Hz positive peak, which is shaped like a late ERP component.
  - Its occurrence after targets versus non-targets isn't convincingly different, so I wouldn't claim a P300.
- **Mains noise.** The raw spectra show peaks at **both 50 and 60 Hz**. These recordings came from different mains regions, which matters if you plan to remove line noise per recording.

**Doing the same in the viewer today:** Dataset → *Muse spatial cueing* → Discovered waveforms → **Lock time to W1** → ERP image → Spectrum → Preprocessing *As distributed* for the raw view. Region selection and labelling are the missing piece.

**The figure picks the strongest copies, which favours artifacts.** Choosing *typical* copies (median amplitude), with mains removed, would give a fairer look at brain-like atoms.

I can build the continuous-data export with the worker panel next, and the label tool falls out of it.

## User:

> 11/2/2026 4:52:11

Oh discovered waveforms is a separate panel with simple summaries rather than an integrated algorithm option in a powerful suite :(

I still don’t see something familiar. Can you show me a time plot of a 4-channel stacked single recording with the eye blink event or events hilighted?

## Assistant:

> 11/2/2026 4:54:24

> Checking which events went unlabelled.
>
> - Choosing recording windows with clear blink copies.
> - Created a file
> - Ran a command
> - Read a file
> - Adjusting trace spacing to stop overlapping plots.
> - Ran a command
> - Read a file
> - Checking which events went unlabelled.
> - Shared files
> - Ran a command
> - **Done**

Here's 20 seconds of one recording, `stacked_W1.png`. W1 copies are in gold, and cue, target and response are dashed lines. This should look familiar:
- **The deflection:** about 300 µV, lasting a few hundred ms, steep onset with a slower return.
- **The timing:** it lands just after almost every **response** (green), and again near some cues.
- **The pattern:** that's blink behaviour. People hold still through the trial and blink once they've answered.
- **Blinks in between:** a few at 189.5, 192.8, 199.8 and 203.7 fall between task events.
- **Missed blinks:** at least one same-looking blink near 196 s isn't gold. The detector's threshold misses some.

**Why it's biggest at TP9/TP10, upside down.** The Muse's reference electrode is at FPz, right above the eyes. Every channel is "site minus FPz", so a blink at FPz shows up inverted on the far sites, TP9 and TP10. AF7 and AF8 sit close to FPz and see less of it. The AUX trace stays flat, which confirms the blink is on the head.

**On the separate panel:** you're right, it's bolted on. Waveform learning should be one more algorithm in the same suite, alongside ICA, filtering and labelling. Each would take a configurable worker job, produce a basis plus events, and feed every view.

Doing that properly means one redesign. Each recording's continuous data ships once, and events become (recording, offset, length) references. A worker then runs ICA, waveform learning, or your own region labels, with settings and progress, and a stacked view like this one becomes the place you select and label regions.

## User:

> 11/2/2026 5:00:08

It hilights a weak signal prior to the blink, that’s pretty cool.
so, i’d imagine using time aligning to then break this signal into smaller signals to analyze blink muscles.

work on building up the app. I’m not sure the panels approach is best, something with multiple desktop-like windows and a tree explorer like many design suites might give a good compromise between ease of building and use.  Is there a reason not to go multiple windows? I feel like it could make an  issue in the conversation i’m not predicting

## Assistant:

> 11/2/2026 5:22:58

> - Sizing the export and window-manager plan.
> - Ran a command
> - Designing the epoch pipeline and job runners.
> - Created a file
> - Passing raw recordings alongside processed ones.
> - Ran a command
> - Designing the window manager and app windows.
> - Created a file
> - Reviewing the bench file for errors.
> - Edited a file
> - Ran a command
> - Ran a command
> - Ran a command
> - Ran a command
> - Ran a command
> - Ran a command
> - **Done**

Exact parity on all three datasets. Checking the screenshots.

> - Read a file
> - Adjusting the atom span to use the peak offset.
> - Command failed
> - Ran a command
> - Checking whether the Run click actually fired.
> - Ran a command
> - Ran a command
> - Read a file
> - Read a file
> - Read a file
> - Ran a command
> - Ran a command
> - Ran a command
> - Fixing the select's width inside the flex row.
> - Ran a command
> - Ran a command
> - Ran a command
> - **Done**

Nothing else uses the old single-trial viewer, so I'm removing it, so there's only one app to maintain.

> - Ran a command
> - Ran a command
> - Couldn't use artifacts
> - Used artifacts
> - Sequencing the publish step after the data batches.
> - Ran a command
> - Ran a command
> - Used artifacts
> - Ran a command
> - Used artifacts
> - **Done**

I've rebuilt the app as a workbench: an explorer tree on the left and windows you can move, resize, maximise and tile on a desktop. It's published at the same link. It now loads the continuous recordings and does all the epoching, filtering and analysis in your browser. I checked the browser's numbers against Python on all three datasets: the trial counts match and the ERP and ERSP agree to four decimal places.

**What's in the tree**
- **Recordings, by participant.** Opens a stacked-trace window with task events as dashed lines and discovered waveform copies as gold spans (now the atom's true extent). Drag sideways to select a span, name it (e.g. "blink") and save it. Labels go under *Event sets → Your labels* and can be used as time zero like any other event. "Find similar spans" searches every recording for spans that correlate with your selection across all channels at r > 0.8, which starts the realign-on-a-template loop you described.
- **Event sets.** Task events, discovered waveforms, your labels and job outputs all open the same window, with ERP / ERP image / ERSP / ERSP image / Spectrum tabs.
  - Comparing A against B gives a difference trace.
  - Rows can be single events, recordings or participants, sorted by RT, peak latency, energy-weighted time, correlation with the average, band power, and more.
  - Double-tapping a row opens that recording at that moment.
- **Bases.** These are signal sets: channels, the Python ICA, the Python waveform patterns, and anything your jobs produce. Any member of any basis can be the "Signal" in an event window.
- **Jobs.** FastICA and waveform learning run in a background worker on whatever recordings you choose, with progress and a log. Their results appear in the tree as a new basis and, for waveforms, new event sets. In testing, ICA on 5 recordings converged in about 3 seconds; waveform learning on 10 recordings took about 35 seconds.
- **The filter** (none, band-pass, 50/60 Hz notch) is chosen at the top and applies to every window, so you can drop the band-pass and look at mains noise anywhere.

**Is there a reason not to go multiple windows?**

Nothing that rules it out. The costs you might not be predicting:
- **Talking about what you see is harder.** "The ERP looks odd" could mean any of several windows. That's why every window title states what it shows (for example "Events · valid cue · TP9"); quoting the title tells me exactly what you mean.
- **Screenshots.** Overlapping windows hide each other, so press *Tile windows* before taking one.
- **Touch and narrow screens.** Dragging title bars is fiddly with a finger, and in the iPad side panel there's no room for a desktop. Below about 860 px wide, the windows become a vertical stack of cards and the tree folds away. I checked that nothing scrolls sideways at phone width.
- **Lost work.** Open windows, job results and saved spans are lost on reload. Labels are kept in your browser.

To break the blink into smaller parts, as you suggested: label a few blinks, run "Find similar spans", then open an ERP image locked to the result and sort by "Correlation with the mean". A natural next step is a "subtract this average and keep the residual as a new signal" action, so the weak lead-in before the blink becomes something you can analyse on its own.

The old single-panel viewer code is deleted, since nothing else used it. The work is committed and the bundle at `/home/claude/eeg_studies_1.bundle` is refreshed.

## User:

> 11/2/2026 5:30:03

Could not load data/catalog.json: data/catalog.json: HTTP 403   Can you bundle the data or use the workaround from before?

## Assistant:

> 11/2/2026 5:33:54

> - Planning a fallback that bundles a data subset.
> - Ran a command
> - Ran a command
> - Ran a command
> - Ran a command
> - Ran a command
> - Read a file
> - Deciding how to skip overlapping timeline labels.
> - Ran a command
> - Used artifacts
> - Shared files
> - **Done**

I've done both: the workbench now retries the data download, and if that's still refused it uses a subset of the data built into the page.

- **Retries.** When fetching `data/catalog.json` fails, the page tries again with credentials and then with the page's own query string.
- **Built-in data.** If every attempt gets a 403, the page switches to data embedded in itself and a banner says so. The embedded set is:
  - EEGLAB tutorial: complete (1 recording).
  - Muse cueing: 10 of 129 recordings.
  - Muse P300: 10 of 45 recordings.
- **What I tested.** I forced every data request to return 403. All three datasets still loaded and drew, with no page errors.
- **What's lost on the subset.** For the two cut-down Muse datasets, the "check against Python" button does nothing, because the Python reference values were computed over all recordings. The EEGLAB check still works.

I also attached the page as a standalone `index.html` (about 14 MB), which needs no data files.

If the published link still gives a 403, the banner shows the exact failure for each attempt; please send me that text.

## User:

> 11/2/2026 5:40:14

Loading … Console messages Uncaught Error: Script error.

## Assistant:

> 11/2/2026 5:43:09

> - Comparing browser features that could break the viewer.
> - Ran a command
> - Looking into whether page size is breaking the script.
> - Ran a command
> - Checking how an undeclared assignment in the worker script behaves.
> - Ran a command
> - Checking how the worker's message handler gets wired up.
> - Ran a command
> - Ran a command
> - Ran a command
> - Read a file
> - **Done**

Works with both workers and data refused. Committing and publishing.

> Used 4 tools
>
> - Ran a command
> - Couldn't use artifacts
> - Used artifacts
> - Shared files
> - **Done**

I couldn't reproduce the startup error here, so I fixed the two most likely causes, made errors show on the page, and republished. The same link has the new version, and the attached `index.html` is the same 3.9 MB page.

- **Page size.** I cut the page from 14 MB to 3.9 MB. The earlier viewer started in your viewer at about 3 MB, so size is my main suspect. The built-in data is now smaller:
  - EEGLAB tutorial: complete (1 recording).
  - Muse cueing: 2 recordings.
  - Muse P300: 1 recording.
- **Background worker.** If your viewer refuses it, the analysis code now runs on the page itself. Everything still works, but long jobs will freeze the page until they finish.
- **Errors.** Any error now appears in the line under the title, with its message and line number.

Here I tested with the data downloads refused and the worker refused, both at once. All three datasets loaded and drew. With normal access, the browser still matches Python exactly on all three datasets.

If it still gets stuck at "Loading…", send me the text in that line under the title. A bare "Script error." means the browser is hiding the real error, so that line is the best clue to the actual cause.

## User:

> 11/2/2026 5:48:26

Grumph: Warning: Background worker unavailable, running jobs on the page: SecurityError: The operation is insecure. . Does that mean workers have to be implemented using foreground callbacks or timets?

Notes: from @https://claude.ai/artifact/8AyZrwnPvfpDrowazxBDVF i’m remembering that one use-case here is to find an evoked erp next to its ersp, subtract the erp, look at the remaining induced response in the ersp, and then hunt signals nearby the erp aligned time to work on finding those that underly the induced response. Each signal would get associated with the ersp and the event if subtracting it would reduce power.  A user and optionally an automated algorithm could then do that again for another erp or signal to build further data.

## Assistant:

> 11/2/2026 12:38:26

> - Comparing a same-origin worker against chunked main-thread yielding.
> - Ran a command
> - Read a file
> - Read a file
> - Converting the remaining functions to generators with yields.
> - Ran a command
> - Checking how the worker posts messages back.
> - Ran a command
> - Ran a command
> - Ran a command
> - Checking whether the server is still responding.
> - Ran a command
> - Ran a command
> - Checking how the worker receives its messages.
> - Command failed
> - Ran a command
> - Ran a command
> - Read a file
> - Ran a command
> - Ran a command
> - Used artifacts
> - **Done**

Yes. Your viewer doesn't allow pages to start background workers, so the jobs have to run in the page's own thread. To stop that freezing the page, every job now pauses itself in small steps:

- **How it works.** Each long routine (wavelet power, FastICA, waveform learning, template search) marks points where it can safely pause. In a browser that allows workers, it runs straight through in the background as before. In yours, it runs about 12 ms at a time and then hands the page back, so it keeps drawing and taking taps. Several jobs can share the page this way. It's the same code either way, so there's only one copy to maintain. The new version is published to the same link.
- **What I measured.** With workers blocked, the worst pause during an ERSP was 283 ms, and during a FastICA job 167 ms. Those pauses come from the page cutting epochs and filtering before the job starts. That part isn't split into steps yet, and it's the next thing to slice.
- **Accuracy.** The browser still matches Python exactly on all three datasets.

Your evoked/induced idea maps well onto this, and I've added its first step. The ERSP and ERSP image views now have a **Power of** choice:
- **Total:** the power of each trial as recorded.
- **Induced:** each trial minus its set's ERP before taking power. That removes the evoked (phase-locked) part and leaves what you called the remaining induced response.

The rest of the loop could fit the app's existing pieces:
1. **Find nearby signals.** Take candidate signals near the event, such as waveform-atom copies, ICA components or template-search matches.
2. **Score each one.** Subtract it from the trials and measure how much induced power drops in the time and frequency region you've selected. That drop is what would associate the signal with the ERSP and the event.
3. **Record and repeat.** Save signals that reduce power as a new event set or basis linked to that ERSP region. Then repeat on the remainder, either by hand or with an automatic greedy pass.

Step 2 is the new piece. Say if you want it built next. Separately: the last commit holds two changes (the step-by-step jobs and the induced-power option), not one commit each as you prefer.

> - **Done**

Nothing here changes what you have. The local web server I used to test the page has stopped; it isn't needed until the next round of changes, and I'll restart it then. The published page doesn't depend on it.

## User:

> 11/2/2026 12:45:08

claude it's the following day and i'm popping in briefly. thanks for trying to engage this a little bit with me. it hasn't reached the point yet where it's clear to me that it's doing some of the things i've struggled to pursue on my own for years. it would help to generalize the interface more.

you seem to be working in small steps, so it might make some sense to check in around the generalization of the backends. i'm imagining something where all concepts are factored out to make it easy to renavigate them, and very OO similar to maybe gnuradio/phobos in the underlying pluggable architecture, or to arrow where everything has properties

when it loads it looks like panes which is frustrating then i see i can move and resize them :) how useful!

i don't see anything happen when i click "explorer" -- what's it do?

should events and basis maybe be the same kind of window as recording, just with different data, and the features merged?

in the recording window, it would be nice for the signal interactivity to be more intuitive and featureful, more like working with say audacity or scrubbing a video; the meaning of the "window s" spacing and arrows should be clear -- but plying with them i see these controls are similar to the ones i'm describing, just not as normatively placed without as normative responses

## Assistant:

> 11/2/2026 12:45:53

> - Fixing the explorer toggle for wide screens.
> - Ran a command
> - Couldn't use artifacts
> - Used artifacts
> - **Done**

The **Explorer** button was meant to hide the tree, but it only worked on narrow screens. That's fixed and published: on any width it now hides the tree and the desk takes the space.

Here's the backend design to check before I build it:

- **Everything is a table with properties**, as in Arrow. Recordings, events, labels, channels and basis members all become one `Table` type whose columns carry units and where they came from. An "event set" is just a filtered table, and any column can be used to sort, group, average over or split by. That covers splitting by left/right, by participant, and so on.
- **Data is one `Signal` type**: channels × time, with a property table for the channels (name, position, which basis produced it).
- **Processing is pluggable blocks**, as in GNU Radio flowgraphs or Phobos ranges. Each `Block` has typed inputs and outputs:
  - band-pass, project onto a basis, epoch at events, average over a property, wavelet power;
  - subtract, keeping the removed part as a second output;
  - decompose with ICA or waveform learning, giving a basis plus an events table;
  - template search.
  Blocks connect into a `Graph`. The tree becomes that graph, so every intermediate result can be found and reopened.
- **One window type, `View`.** It plots any node's output by assigning dimensions to axes: time, frequency, rows sorted by a property, channels stacked or placed on the scalp. So yes, recording, events and basis become the same window:
  - recording view: time × stacked channels;
  - ERP image: time × rows;
  - basis maps: channels on the scalp.
  Every feature then works everywhere.
- **Time navigation like Audacity** in every view:
  - an overview strip with a draggable viewport;
  - pinch or scroll to zoom, drag to pan, drag on the time ruler to select, a playhead;
  - visible-span and zoom controls in the usual places, replacing the current "window s" box and arrow buttons.

One decision changes everything after it: where the backend lives.

1. **Python in the browser (Pyodide).** The OO `eegkit` library runs on the page, so there is one codebase and the tests run in both places. It costs roughly 30 MB on first load, and Python-side slowness that's hard to break into small steps.
2. **A JavaScript port of the same object model.** Faster and lighter, but two implementations kept in step by the Python reference values, as now.

I lean toward 2, with the Python library staying the reference and test oracle. Which do you want?

## User:

> 11/2/2026 12:48:37

i'm heading off now but feel free to work on it more to make it something i'd respond better to, and maybe add a tutorial mode to help me understand that i'm seeing what i want.  if you're unsure of a decision i'd implement both really :s   i'm likely to only be using this in your interface now but eventually want to use it CLI far far in the future without node, and at that time i'd probably ask for a parallel structure port. if speed is a concern it might make sense to try a test and see how it lands with me (letting me know it's a slowness test in case i'm feeling impatient)

## Assistant:

> 11/2/2026 16:27:20

> - Designing the tutorial steps as chained blocks.
> - Ran a command
> - Converting latency columns to epoch-relative milliseconds.
> - Ran a command
> - Read a file
> - Writing the workbench viewer's HTML and worker files.
> - Created a file
> - Restructuring the recordings view to avoid redundant redraws.
> - Ran a command
> - Checking how the navigator handles the recording's start point.
> - Ran a command
> - Ran a command
> - Checking how the app escapes script closing tags.
> - Ran a command
> - Ran a command
> - Checking timing of the epoch and ERSP runs.
> - Read a file
> - Read a file
> - Read a file
> - Read a file
> - Arranging open windows side by side in a layout.
> - Ran a command
> - Tracing a shared phase-locked artifact in frontal and AUX channels.
> - Ran a command
> - Rewriting tutorial step run logic and caching nodes.
> - Ran a command
> - Read a file
> - Read a file
> - Read a file
> - Checking why two outputs come out identical.
> - Ran a command
> - Ran a command
> - Read a file
> - Read a file
> - Adjusting box sizes so overlapping channel layouts fit the scalp.
> - Ran a command
> - Ran a command
> - Checking the narrow-width layout.
> - Ran a command
> - Read a file
> - Weighing a horizontal pan setting for narrow screens.
> - Ran a command
> - Used artifacts
> - Used artifacts
> - **Done**

I rebuilt the workbench around a processing graph, added an 8-step tutorial that opens when the page loads, and made a separate slowness test for Python-in-the-browser. The new workbench is published to the same link.

**How it's organised now**
- **Everything is a node in the explorer.** Recordings, events, bases (sets of signals made by weighting the channels, such as ICA components), epochs, averages and spectra are each a node. Each step you run adds a child node, so the explorer is the history of what you made.
- **Steps are blocks** that work on whatever data has the right shape:
  - filter, cut epochs, average, difference between groups;
  - project onto a basis, or remove basis members (the removed part is kept as its own node);
  - subtract the average to get induced power, ERSP, band power, spectrum;
  - per-trial measures (peak latency, energy-weighted time, correlation with the average) that become sortable columns;
  - ICA and waveform learning.
  Each window's **Add a step** menu lists the blocks that apply. Filtering is a step now, not a global setting.
- **One window type for everything.** Events and bases are now the same kind of window as recordings, as you suggested. Recordings show stacked traces, events and bases show sortable tables (bases also show scalp maps), and results are drawn as lines, an image or small plots placed on the scalp. Each window also shows the chain of equations from the raw data.
- **Recording navigation like Audacity:**
  - a whole-recording strip you can drag;
  - drag to pan, pinch or Ctrl+scroll to zoom, tap to place a cursor;
  - a **Select** mode for marking a span to label or template-search;
  - a readout like "0–10 s of 0–239".

**The tutorial** builds each step with the same blocks the menus use, then says what you should be seeing. It goes: raw data → filter → events table → ERP image sorted by response time → ERP on the scalp → total vs induced ERSP → bases. The induced step prints how much the two power maps actually differ. That's about 2.8 dB on the EEGLAB tutorial data, where the evoked response holds the early low-frequency power. On Muse TP9 the difference is small.

**What I checked**
- I ran the whole tutorial on the Muse cueing and EEGLAB datasets, including with workers blocked the way your viewer blocks them, plus a phone-width check. There were no errors.
- The Python comparison now runs through the blocks themselves and still matches exactly.
- **Slow steps:** on Muse cueing, cutting epochs from all 129 recordings takes about 7–13 s, and the two ERSPs about 11 s. Progress shows in the banner while they run.
- **A finding in the Muse ERPs:** AF7, AF8 and the AUX lead have jagged, fast activity locked to the stimulus, and the Python pipeline shows it too. It could be screen or electrical interference; I haven't checked.

**Slowness test.** Your viewer may well refuse to load Python, so this is a separate page. It times the same wavelet and filtering math in Pyodide (numpy, scipy) and in plain JavaScript, and shows any failure. I couldn't run the Python half here because the download site is blocked from my workspace; the JavaScript half took about 1 s. How it lands for you will decide between running the Python library in the page and keeping the JavaScript port. The parallel port for command-line use can come later either way.

The work is committed in separate commits and `/home/claude/eeg_studies_1.bundle` is refreshed.

> - **Done**

The local test server I was using for browser checks hit its time limit and stopped. Nothing depends on it, and I'll start it again before the next round of tests. Your workbench and slowness-test pages are already published, so they aren't affected.

## User:

> 11/2/2026 16:45:32

* python test gave this output, suspecting unlogged error:

JavaScript: 400-trial wavelet power, 27 frequencies	0.45 s	plain JS, the workbench's current engine
Download the Pyodide loader	0.04 s	

* audacity-like view seems so nice! scroll-wheel should zoom without ctrl
* it's not clear yet (i'm at step 1 paragraph 2) why some areas are hilighted in different shades of yellow. clicking them doesn't seem to give information
* the tutorial says the data is in uV but i don't see a vertical axis
* might be clearest to refuse to zoom in so far that data is upsampled, or to use a stepped view to make it clear what a sample is
* ok now i see that gold spans are copies of waveforms, having gotten to paragraph 4. a little confused/frustrated that they're only identified in a python pass. also not yet clear how to see or link them
* it says "W1 is the blink-like one in the Muse sets" but i'm not sure how to find a W1. i found the dataset dropdown; there are 2 muse sets, and it has spatial cueing open.
* oh! i see that some of the gold spans have labels at the very top and say W1. i see that that's a blink.
* seems to be some autoscale bug, as i scroll by the blink (it moves to the left) it gets taller and overlaps adjacent graphs and goes off-graph
* be good to see the gold spans and dashed lines on the strip above, hopefully they are also in the tree view somewhere
* i het next on the tutorial and a window opened named #23; not clear how i could have opened or created this myself

i like that #23 is the same as #1. i'll keep reading the tutorial, but i'm a little worried that the hilighted data was a python pass, and i guess _that this wasn't clear in the interface_.

hmmm we got really far but i'm starting to have challenges engaging it.
tutorial says every window has how this was computed and add a step, but where? i don't see these. i did find the events shown dropdown, that seems cool

## Assistant:

> 11/2/2026 16:52:55

> Diagnosing why the Pyodide loader hangs without logging.
>
> - Diagnosing why the Pyodide loader hangs without logging.
> - Added a task
> - Added a task
> - Added a task
> - Added a task
> - **Done**

Thanks, this is very usable feedback. I'm working through all of it now. I'll fix the slowness test first so it logs what's going wrong, then make the recording view changes (wheel zoom, µV axis, fixed scale, sample steps, spans on the strip), then make it clear what was computed in Python and where "how this was computed" and **Add a step** are.

> - Ran a command
> - Read a file
> - Planning a fixed vertical scale and zoom limits.
> - Ran a command
> - Read a file
> - Planning the navigator wheel and zoom changes.
> - Ran a command
> - **Done**

Now the navigator: wheel zooms by default, tap reports a time, plus a wheel-mode toggle.

> Arranging the step and equation controls into a top row.
>
> - Edited a file
> - Edited a file
> - Edited a file
> - Edited a file
> - Arranging the step and equation controls into a top row.
> - **Done**

Now the window layout: put "How this was computed" and "Add a step" at the top as buttons.

> - Edited a file
> - Edited a file
> - Edited a file
> - Tracing how shipped data gets its origin tags.
> - Ran a command
> - Checking how span lookup matches waveform atom names.
> - Edited a file
> - Read a file
> - Edited a file
> - Edited a file
> - Ran a command
> - **Done**

Now mark the shipped waveform event sets in `App.load`, and tag Python nodes in the tree.

> Planning the recordings view controls and drawing logic.
>
> - Ran a command
> - Planning the recordings view controls and drawing logic.
> - **Done**

Now the recordings view itself: fixed scale, clipping, sample steps, zoom limit, µV scale bar, legend, tap to get details, and events on the strip.

> Adding step-two text and a pulse highlight.
>
> - Ran a command
> - Tracing how redraw and drawRecordings call each other.
> - Ran a command
> - Adding step-two text and a pulse highlight.
> - **Done**

Now the tutorial text, and having it point at the button that made each window.

> Adding a timeout and progress ticker to the loading step.
>
> - Ran a command
> - Clearing the pointed class on tutorial show.
> - Ran a command
> - Adding a timeout and progress ticker to the loading step.
> - **Done**

Now the slowness test: a timeout on every step, a live timer, and logging of CSP blocks, console errors and Python's own output.

> - Ran a command
> - Ran a command
> - Checking whether the viewer's fetch succeeds.
> - Ran a command
> - Ran a command
> - Checking which folder the local server serves from.
> - Ran a command
> - **Done**

Tutorial passes and still matches Python. Now an interaction check on the Muse set: scroll-zoom, tapping a gold span, the sample steps and a pan.

> Adding a minimize toggle to the tutorial card.
>
> - Ran a command
> - Read a file
> - Read a file
> - Checking a trace drifting past the plot's edge.
> - Ran a command
> - Read a file
> - Adding a minimize toggle to the tutorial card.
> - **Done**

The scale holds steady while panning (5.10 px per 10 µV at every position). The tutorial card covers part of the plot, so I'm adding a button to shrink it.

> - Ran a command
> - Ran a command
> - Read a file
> - Checking the scale of the raw recording window.
> - Ran a command
> - Used artifacts
> - Used artifacts
> - Updated a task
> - Updated a task
> - Updated a task
> - Updated a task
> - **Done**

I've updated both pages from your notes. The workbench passes the full tutorial and the Python comparison on both datasets, including with workers blocked. The slowness test still can't load Python from my workspace, so I couldn't run its Python half.

**Slowness test**
- You were right: a failure was going unlogged. It now times out every step and keeps a log of blocked loads, console errors and Python's own output.
- When it stops it says which step and why. If it stops for you, please paste the log back.

**Recording window**
- **Scroll wheel** now zooms without Ctrl, and Shift+scroll pans. There's also a "Wheel: zoom/pan" button to swap them.
- **Scale:** the µV scale is now fixed for the whole recording, so the blink no longer grows as you pan. I checked that the scale stays the same while panning. A "refit to what's shown" option is still there.
- **Overlap:** traces are clipped at the plot edge, and you can choose whether big swings cross into the neighbouring trace or are clipped to their own row.
- **Vertical axis:** each trace now has a faint 0 µV line, there's an orange µV scale bar at bottom left, and hovering shows the value and sample number.
- **Zooming in:** samples are drawn as flat steps with dots, and zoom stops at 24 pixels per sample. The other "Closest zoom" option stops at 1 pixel per sample instead.
- **Coloured spans:** a legend under the plot says what each colour means. Tapping a span or line gives:
  - what it is and where it came from (Python or this page);
  - buttons to open its table, show its waveform's shape, and jump to the previous or next copy.
- The strip above the plot now shows the spans and event lines too.

**Where things came from**
- Each window has a line under its title saying where its data came from:
  - **#1:** "as recorded".
  - **#23:** "made here: ＋ Add a step → Filter on #1".
  - **The waveform basis and its copies:** "computed by the Python pass before this page was built". You were right that this wasn't clear before.
- The explorer marks nodes "from Python" or "made here".
- You can learn waveforms in the page itself: ＋ Add a step → Decompose: waveform learning.

**Finding the steps:** **＋ Add a step** and **ƒ How this was computed** were folded away at the bottom of each window. They're now buttons at the top. Tutorial step 2 highlights the button that made #23, and the tutorial card has a **Shrink** button so it stops covering the plot.

One thing I noticed: at about 38.9 s in the first Muse recording there's a blink-shaped swing with no gold span. The Python pass's list of copies isn't complete.

I committed the slowness test separately, but the recording-view and labelling changes went into one combined commit because they touch the same code. I can split it if you'd like. The bundle is refreshed.

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