PyBCI brain-computer interface development toolkit

PyBCI

BCI Development Reimagined

One AI-powered editor for the full BCI workflow, from streams and markers to preprocessing, training, and review.

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Preview
PyBCI Builder Mode with a pipeline graph, live EEG preview, model output, and confidence readout.

Why PyBCI

BCI tooling is fragmented.

Developers juggle LSL, BRAIFLOW, PsychoPy, OpenBCI, event markers, and preprocessing scripts just to get one experiment running.

PyBCI brings the full pipeline into one editor so you can build, validate, and iterate faster with AI assistance.

Fragmented toolsLSL, PsychoPy, OpenBCI, and markers in one flow.

No more bouncing between disconnected apps to complete one experiment.

One workspaceBuild, preprocess, train, and review in one place.

Keep the pipeline visible so iteration stays fast.

AI poweredUse AI to reduce friction across the workflow.

Get guidance as you configure experiments and move toward deployment.

pyBCI in Action

01 / Data

Import data and map channels.

Upload EEG files, concatenate sessions, assign electrodes, and inspect the signal before analysis.

01 / Data

Import data and map channels.

Upload EEG files, concatenate sessions, assign electrodes, and inspect the signal before analysis.

Data management
PyBCI Data Manager with selected EEG files, frequency spectrum, and electrode position assignment dialog.
  • Selected EEG files
  • Electrode assignment
  • Frequency spectrum
02 / Record

Record with live board context.

Monitor connection status, sample rate, channel controls, scaling, and session actions beside the live trace.

Recording console
PyBCI Record screen showing metadata, channel controls, and recording controls.
  • Recording status
  • Board metadata
  • Scale controls
03 / Preprocess

Build and validate preprocessing.

Queue filters, artifact steps, epoching, and scripts, then compare raw and processed EEG.

Preprocessing
PyBCI Preprocessing Pipeline with filtering controls and before-after EEG waveform plots.
  • Filter builder
  • Pipeline queue
  • Before/after EEG
04 / Train

Train classical and neural models.

Configure algorithms, queue model candidates, and review evaluation outputs in one workflow.

ML pipeline
PyBCI ML Pipeline screen with model builder, queued models, and visualization actions.
  • Algorithm settings
  • Model queue
  • Evaluation actions
05 / Tune

Tune deep learning visually.

Set input shape, optimizer, batch size, epochs, gradient clipping, and layer architecture.

Deep learning config
Configure Deep Learning Model dialog with architecture settings and available layers.
  • Layer palette
  • Training controls
  • Architecture canvas
06 / Visualize

Review brain playback and activation.

Use 3D playback and regional activation charts to review model behavior over time.

Visualization
PyBCI Visualization screen showing 3D brain playback and estimated regional activation.
  • 3D model tab
  • Playback controls
  • Activation chart

Access

Sign up for early PyBCI access.