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


npm install bcijs


<script src=""></script>

Feature Overview

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 ]

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(
	epoch => bci.bandpower(epoch, samplerate, ['alpha', 'beta'], {average: true}),


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 for a visual demo of how LDA works

Usage in the web

BCI.js can be loaded from the jsDelivr CDN with

<script src=""></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 can be found at or by viewing

Deprecated methods can be found at


See 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.

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