brainaccess.connect package

Submodules

brainaccess.connect.P300 module

class brainaccess.connect.P300.P300(repetitions: int)

Bases: object

P300 BCI library

Initialize P300 model as per selected number of repetitions.

Parameters:

repetitions (int) – number of repetitions to consider on each stimuli, can be either 1 or 3.

Raises:

Exception – An error is raised if initializing failed

destroy() → None

predict(x: ndarray) → float

Predict P300

Parameters:

x (np.ndarray) – data for classifier

Returns:

probability that data was P300 event

Return type:

float

Raises:

Exception – An error is raised if prediction failed

Warning

Data sampled at 250 Hz must have these properties:

• standardized with ewma and filtered with 1-40 Hz filter

• (8, 176 * repetitions) shape (channels x samples),

• each repetition 200 ms prior to stimulus onset up to 500 ms after stimulus onset

• Channels must be in exactly this order: F3, F4, C3, C4, P3, P4, O1, O2

brainaccess.connect.SSVEP module

class brainaccess.connect.SSVEP.SSVEP(frequencies: list = [], sample_rate: float = 250)

Bases: object

SSVEP BCI library

Initialize SSVEP model

Parameters:

• frequencies (list) – list of stimulation frequencies

• sample_rate (float) – data sampling rate

Raises:

Exception – An error is raised if initializing failed

predict(x: ndarray, frequencies: Optional[list] = None, sample_rate: Optional[float] = None) → tuple

Classify EEG SSVEP (steady state visually evoked potentials) given a set of class frequencies

Parameters:

• x (np.ndarray) – EEG data (channels x samples) for classifier

• frequencies (list) – list of stimulation frequencies

• sample_rate (float) – data sampling rate

Returns:

• float – target frequency

• float – target threshold value

Raises:

Exception – An error is raised if prediction failed

Warning

Data must have these properties:

• filtered with 1-90 Hz filter

• selected channels must be from ocipital region

brainaccess.connect.processor module

brainaccess.connect.processor.demean(x: ndarray) → ndarray

Subtract mean from each channel

Parameters:

x (np.ndarray) – data array, shape (channels, time)

Returns:

data array, shape (channels, time)

Return type:

np.ndarray

brainaccess.connect.processor.ewma(x: ndarray, alpha: float = 0.001) → ndarray

Exponential weighed moving average helper_function

Parameters:

• x (np.ndarray) – data array, shape (channels, time)

• alpha (float) – new factor

Returns:

data array, shape (channels, time)

Return type:

np.ndarray

brainaccess.connect.processor.ewma_standardize(x: ndarray, alpha: float = 0.001, epsilon: float = 0.0001) → ndarray

Exponential weighed moving average standardization

First-order infinite impulse response filter that applies weighting factors which decrease exponentially

Parameters:

• x (np.ndarray) – data array, shape (channels, time)

• alpha (float) – Represents the degree of weighting decrease, a constant smoothing factor between 0 and 1. A higher alpha discounts older observations faster.

• epsilon (float) – Stabilizer for division by zero variance

Returns:

data array, shape (channels, time)

Return type:

np.ndarray

brainaccess.connect.processor.fft(x: ndarray, sampling_freq: float) → dict

Compute the discrete Fourier Transform (DFT) with the efficient Fast Fourier Transform (FFT) algorithm

Parameters:

• x (np.ndarray) – data array, shape (channels, time)

• sampling_freq (float) – data sampling rate

Returns:

dictionary (key: value) - freq: frequencies - mag: amplitudes - phase: phases

Return type:

dict

brainaccess.connect.processor.filter_bandpass(x: ndarray, sampling_freq: float, freq_low: float, freq_high: float) → ndarray

Bandpass filter

Butterworth 5th order zero phase bandpass filter

Parameters:

• x (np.ndarray) – data array, shape (channels, time)

• sampling_freq (float) – data sampling rate

• freq_low (float) – frequency to filter from

• freq_high (float) – frequency to filter to

Returns:

filtered data, shape (channels, time)

Return type:

np.ndarray

Warning

Data must be detrended or passed through ba_bci_connect_ewma_standartize before applying notch filter

brainaccess.connect.processor.filter_highpass(x: ndarray, sampling_freq: float, freq: float) → ndarray

High-pass filter

Butterworth 5th order zero phase high-pass filter

Parameters:

• x (np.ndarray) – data array, shape (channels, time)

• sampling_freq (float) – data sampling rate

• freq (float) – edge frequency

Returns:

filtered data, shape (channels, time)

Return type:

np.ndarray

Warning

Data must be detrended or passed through ba_bci_connect_ewma_standartize before applying notch filter

brainaccess.connect.processor.filter_lowpass(x: ndarray, sampling_freq: float, freq: float) → ndarray

Low-pass filter

Butterworth 5th order zero phase low-pass filter

Parameters:

• x (np.ndarray) – data array, shape (channels, time)

• sampling_freq (float) – data sampling rate

• freq (float) – edge frequency

Returns:

filtered data, shape (channels, time)

Return type:

np.ndarray

Warning

Data must be detrended or passed through ba_bci_connect_ewma_standartize before applying notch filter

brainaccess.connect.processor.filter_notch(x: ndarray, sampling_freq: float, center_freq: float, width_freq: float) → ndarray

Notch filter at desired frequency

Butterworth 4th order zero phase bandpass filter

Parameters:

• x (np.ndarray) – data array, shape (channels, time)

• sampling_freq (float) – data sampling rate

• center_freq (float) – notch filter center frequency

• width_freq (float) – notch filter width

Returns:

data array, shape (channels, time)

Return type:

np.ndarray

Warning

Data must be detrended or passed through ba_bci_connect_ewma_standartize before applying notch filter

brainaccess.connect.processor.mean(x: ndarray) → ndarray

Calculate mean for each channel in the data

Parameters:

x (np.ndarray) – data array, shape (channels, time)

Returns:

means for each channel in the same order as x

Return type:

np.ndarray

brainaccess.connect.processor.st_deviation(x: ndarray) → ndarray

Calculate standard deviation for each channel in the data

Parameters:

x (np.ndarray) – data array, shape (channels, time)

Returns:

standard deviation for each channel in the same order as x

Return type:

np.ndarray

brainaccess.connect.processor.standartize(x: ndarray) → ndarray

Data standardization

Parameters:

x (np.ndarray) – data array, shape (channels, time)

Returns:

data array, shape (channels, time)

Return type:

np.ndarray

Module contents