Brain Computer Interfaces (BCIs) with JavaScript

## Getting Started Latest release is v1.8.0. You can view the release notes at [releases](https://github.com/pwstegman/bci.js/releases) Documentation is available at [https://bci.js.org/docs/](https://bci.js.org/docs/) **Node.js** ```bash npm install bcijs ``` **Browser** ```html ``` ## Feature Overview For a complete list of methods, see the [docs](https://bci.js.org/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 | | | ## Tutorials - [EEG Motor Imagery Classification in Node.js with BCI.js](https://medium.com/@pwstegman/eeg-motor-imagery-classification-in-node-js-with-bci-js-d21f29cf165) ## Examples More examples can be found in the [examples](https://github.com/pwstegman/bci.js/tree/master/examples) directory ### Bandpower ```javascript 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 ```javascript 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 ```javascript 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 ```javascript 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](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 ```html ``` You can also find `bci.js` and `bci.min.js` at [releases](https://github.com/pwstegman/bci.js/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. ```javascript 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 ```javascript const fastICA = require('bcijs/lib/math/fastICA.js'); ``` BCI.js methods can be found in the [src/](https://github.com/pwstegman/bci.js/tree/master/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](https://bci.js.org/docs) or by viewing [api.md](https://github.com/pwstegman/bci.js/blob/master/docs/docs/api.md) Deprecated methods can be found at [deprecated.md](https://github.com/pwstegman/bci.js/blob/master/docs/docs/deprecated.md) ## Building See [dev.md](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](https://fontawesome.com/license/free). ## Contact If you have a commercial use case for BCI.js and would like to discuss working together, contact me at [pwstegman@gmail.com](mailto:pwstegman@gmail.com)