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webbci

📊 EEG signal processing and machine learning in JavaScript

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This project has been renamed to bcijs. Install bcijs for latest updates.

Readme

Brain Computer Interfaces (BCIs) with JavaScript

Version Downloads CDN License


Getting Started

Latest release is v1.8.0. You can view the release notes at releases

Documentation is available at https://bci.js.org/docs/

Node.js

npm install bcijs

Browser

<script src="https://cdn.jsdelivr.net/npm/bcijs@1.8.0/dist/bci.min.js"></script>

Feature Overview

For a complete list of methods, see the docs.

Signal ProcessingMachine LearningData Management
BandpowerFeature extractionLoad and save CSVs (Node.js only)
Welch's methodLinear discriminant analysisLoad from EDF (Node.js only)
PeriodogramConfusion matricesEpoch / window data
Independent component analysisMetrics (precision, recall, F1, MCC, etc.)Partition datasets
Common spatial patternArray subscripting (colon notation)
Signal generation

Tutorials

Examples

More examples can be found in the examples directory

Bandpower

const bci = require('bcijs');

// Generate 1 second of sample data at 512 Hz
// Contains 8 μV / 8 Hz and 4 μV / 17 Hz
let samplerate = 512;
let signal = bci.generateSignal([8, 4], [8, 17], samplerate, 1);

// Compute relative power in each frequency band
let bandpowers = bci.bandpower(signal, samplerate, ['alpha', 'beta'], {relative: true});

console.log(bandpowers); // [ 0.6661457715567836, 0.199999684787573 ]

Epoch data

let samples = [[1,2], [3,4], ...] // 2D array where rows are samples and columns are channels
let samplerate = 256; // 256 Hz

// Epoch data into epochs of 256 samples with a step of 64 (75% overlap)
// Then find the average alpha and beta powers in each epoch.
let powers = bci.windowApply(
    samples,
    epoch => bci.bandpower(epoch, samplerate, ['alpha', 'beta'], {average: true}),
    256,
    64
);

Subscript

const bci = require('bcijs');

// 5 samples of data from 3 channels 
let signal = [[1,2,3], [5,3,4], [4,5,6], [7,5,8], [4,4,2]];

// Select the first 3 samples from channels 1 and 3
let subset = bci.subscript(signal, '1:3', '1 3'); // [ [ 1, 3 ], [ 5, 4 ], [ 4, 6 ] ]

Linear discriminant analysis

const bci = require('bcijs');

// Training set
let class1 = [[0, 0], [1, 2], [2, 2], [1.5, 0.5]];
let class2 = [[8, 8], [9, 10], [7, 8], [9, 9]];

// Testing set
let unknownPoints = [[-1, 0], [1.5, 2], [7, 9], [10, 12]];

// Learn an LDA classifier
let ldaParams = bci.ldaLearn(class1, class2);

// Test classifier
let predictions = bci.ldaClassify(ldaParams, unknownPoints);

console.log(predictions); // [ 0, 0, 1, 1 ]

Check out https://bci.js.org/examples/lda for a visual demo of how LDA works

Usage in the web

BCI.js can be loaded from the jsDelivr CDN with

<script src="https://cdn.jsdelivr.net/npm/bcijs@1.8.0/dist/bci.min.js"></script>

You can also find bci.js and bci.min.js at releases.

BCI.js methods are accessible via the global object bci.

If building a web distributable using a tool such as browserify or webpack, require bcijs/browser.js to load only methods that are browser compatible. Node.js specific methods such as networking and file system methods will not be included.

const bci = require('bcijs/browser.js');

Requiring specific methods

You can require specific methods as well. For example, if you only need fastICA, you can use

const fastICA = require('bcijs/lib/math/fastICA.js');

BCI.js methods can be found in the src/ directory.

Files are transpiled from ES6 import/export (in src/) to CommonJS (generated lib/) on npm install.

Documentation

Documentation can be found at https://bci.js.org/docs or by viewing api.md

Deprecated methods can be found at deprecated.md

Building

See dev.md for info on how to modify and build BCI.js

Reference

BCI.js began as WebBCI, a library developed to aid in my research at the Human Technology Interaction Lab at the University of Alabama Department of Computer Science. If you use BCI.js in a published work, please reference this paper

P. Stegman, C. Crawford, and J. Gray, "WebBCI: An Electroencephalography Toolkit Built on Modern Web Technologies," in Augmented Cognition: Intelligent Technologies, 2018, pp. 212–221.

Logo uses icon from Font Awesome.

Contact

If you have a commercial use case for BCI.js and would like to discuss working together, contact me at pwstegman@gmail.com

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