EEG Pipes

By Neurosity

Blazing fast EEG transformers implemented as "Pipeable" RxJS operators for Node and the Browser.

Features include:

FFT
PSD and Power Bands
Buffering and Epoching
IIR Filters
Signal Quality (new)
and more.

Get started by installing the library:

npm install @neurosity/pipes

Usage

An Observable of EEG data is required to work with pipes. This can be done by using fromEvent from RxJS in order to push callback events into an Observable stream.

Given a callback-driven API such as:

bci.on("data", () => { ... });

Then...

import { fromEvent } from "rxjs";

const eeg$ = fromEvent(bci, "data");

Now we have an Observable of EEG data that support Pipeable operators.

eeg$
  .pipe
  // ...
  ()
  .subscribe();

The following are some libraries that provide EEG as RxJS observables out of the box:

Pipes can be added to an EEG observable of EEG data samples with the following data structure:

{
  data: [Number, Number, Number, Number], // channels
  timestamp: Date,
  info?: {
      samplingRate?: Number,
      channelNames?: [String, String, String, String],
      ..
  }
};

Individual samples of EEG data contain an array of values for each EEG channel as well as a timestamp. An additional info object containing metadata about the EEG stream such as sampling rate and channel names can also be included or added with the addInfo operator.

Import the pipes from the module:

import { epoch, fft, alphaPower } from "@neurosity/pipes";

Add to RxJS observable pipe:

eeg$
  .pipe(
    epoch({ duration: 256, interval: 100 }),
    fft({ bins: 256 }),
    alphaPower()
  )
  .subscribe(alphaPower => console.log(alphaPower));

Pipes

Filtering (IIR)

Filter pipes can be applied to both samples or buffers of samples. Filters are linear IIR filters using a digital biquad implementation.

lowpassFilter({ nbChannels, cutoffFrequency })
highpassFilter({ nbChannels, cutoffFrequency })
bandpassFilter({ nbChannels, cutoffFrequencies: [lowBound, highBound] })
notchFilter({ nbChannels, cutoffFrequency })

Optional Parameters:
characteristic: 'butterworth' or 'bessel'. Default is butterworth characteristic because of its steeper cutoff
order: the number of 2nd order biquad filters applied to the signal. Default is 2.
samplingRate: should match the samplingRate of your EEG device. Default is 250

Frequency

bufferFFT({ bins, window, samplingRate })
alphaPower()
betaPower()
deltaPower()
gammaPower()
thetaPower()
averagePower()
sliceFFT([ min, max ])
powerByBand()

Unit conversion

voltToMicrovolts({ useLog })

Utility

epoch({ duration, interval, samplingRate })
bufferCount()
bufferTime()
bufferToEpoch({ samplingRate })
pickChannels({ channels: [c1, c2, c3] })
removeChannels({ channels: [c1, c2, c3] })
addInfo()
addSignalQuality()
- signal quality is represented as standard deviation value for each channel

Coming soon

Filtering

vertScaleFilter()
vertAgoFilter()
smoothFilter()
polarityFilter()
maxFrequencyFilter()

Data Structures

Sample

This is the simplest, core data structure for individual samples of EEG data. Samples have a data array containing a single reading from a number of different EEG electrodes along with a single timestamp. Samples can also contain optional other parameters added by the addInfo operator. The design for this comes directly from the discussion on the EEG stream data models repo.

{
    data: [Number, ..., Number], // length == nbChannels
    timestamp: <Number>,
    info?: {
        samplingRate?: Number,
        channelNames?: [String, String, String, String],
      ...
  }
}

Epoch

An Epoch represents the EEG data that has been collected over a specific period of time. They can be produced by using either the epoch operator or by using a standard RxJS buffering operator such as bufferTime followed by the bufferToEpoch operator. Collecting data in this way is necessary for performing frequency-based analyses and, in many cases, will improve performance of filtering and other downstream operations. Epochs contain a 2D data array (channels x samples) and an info object that always includes samplingRate and startTime data so that the timestamps of individual samples can be derived.

{
    data: [
        [Number, ... , Number], // length == duration
        [Number, ... , Number]
    ], // length == nbChannels
    info: {
        samplingRate: Number,
        startTime: Number,
        channelNames?: [String, ..., String ]
    }
}

Power Spectral Density or PSD: Proposed Work in Progress

A PSD represents the absolute power of different frequency bins in an Epoch of EEG data. PSDs are produced by applying the fft operator to Epochs or using the bufferFFT operator directly on a stream of EEG Samples. PSDs contain an array of frequencies and a corresponding array of spectral power at each of those frequencies, as well as an info object that contains samplingRate and startTime info similarly to Epochs.

{
    psd: [
        [Number, ... , Number] // spectral power; length = freqs.length
    ], // length = numChannels
    freqs: [Number, ... , Number], // length = fftLength / 2
    info: {
        samplingRate: Number,
        startTime: Number,
        channelNames?: [String, ..., String ]
    }
}

Generating documentation

To generate the docs, run yarn esdoc