Brain Computer Interfaces (BCIs) with JavaScript
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 bcijsBrowser
<script src="https://cdn.jsdelivr.net/npm/bcijs@1.8.0/dist/bci.min.js"></script>For a complete list of methods, see the docs.
| Signal Processing | Machine Learning | Data Management |
|---|---|---|
| Bandpower | Feature extraction | Load and save CSVs (Node.js only) |
| Welch's method | Linear discriminant analysis | Load from EDF (Node.js only) |
| Periodogram | Confusion matrices | Epoch / window data |
| Independent component analysis | Metrics (precision, recall, F1, MCC, etc.) | Partition datasets |
| Common spatial pattern | Array subscripting (colon notation) | |
| Signal generation |
More examples can be found in the examples directory
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 ]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
);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 ] ]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
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');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 can be found at https://bci.js.org/docs or by viewing api.md
Deprecated methods can be found at deprecated.md
See dev.md for info on how to modify and build BCI.js
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.
If you have a commercial use case for BCI.js and would like to discuss working together, contact me at pwstegman@gmail.com