BIpy 0.1.1

py for BI

BIpy package

Submodules

BIpy.data_processing module

class BIpy.data_processing.LowpassWrapper(lowcut=70, sf=500, order=6, axis=2)

Bases: sklearn.base.TransformerMixin

Wrapper class for using lowpass_filter in an sklearn pipeline

fit_transform(data)

Low pass filters data

transform(data) = fit_transform

Also low pass filters data

_init_(lowcut=70, sf=500, order=6, axis=2)

lowcut

Upper limit in hz, above which frequencies are filtered out.
Default 70

sf

Sampling frequency, defualt 500

order

Passed to scipy.signal.butter, default 6

axis

Axis along which the filter is performed, with axis=2 it can filter the entire data cube at once
Default 2, and default should always be used with input data shape (trials, channels, time)

BIpy.data_processing.get_sliding_window_partition(data, labels, window_size)

Splits data into several windows

data

EEG data with shape (trials, channels, time)

labels

1d array of integer labels corresponding to left/right

window_size

Size in samples (not time) of the windows the data will be split into
If window_size corresponds to the number of recorded samples per trial, the function returns the input data and labels unchaged

windowed_data

Array of shape (windows, channels, time)

windowed_labels

1d array of labels corresponding to each window

BIpy.data_processing.lowpass_filter(data, lowcut=70, sf=500, order=6, axis=2)

Low pass filter

data

Shape (trials, channels, time)

lowcut

Upper limit in hz, above which frequencies are filtered out.
Default 70

sf

Sampling frequency, defualt 500

order

Passed to scipy.signal.butter, default 6

axis

Axis along which the filter is performed, with axis=2 it can filter the entire data cube at once
Default 2, and default should always be used with input data shape (trials, channels, time)

y

Filtered data, of same shape as input

BIpy.data_processing.organize_xdf(xdf_filename, trial_duration, gelled_indeces=[5, 6, 7, 10, 11, 21, 22, 24, 27, 28, 38, 39, 40, 42, 53, 55, 56, 57], stim_channel=67, instructed_trigger_map={'instructed_left': 12, 'instructed_right': 13})

Function to organize motor imagery xdf data into labeled epochs. Does not support free trials

Free tip: avoid using xdf data wherever possible

xdf_filename

The file location of the xdf data to load and organize

trial_duration

The duration in seconds of each motor imagery trial

gelled_indeces

The indices of relevant electrodes, the data from all other electrodes will be discarded.
By default fc_c_cp_1through6, the indeces corresponding to electrodes fc, c, and cp 1 through 6, found to be most useful for BCI
Index <=> electrode mappings can be found in BIpy.electrode_info.csv

stim_channel

The channel used for triggers/events, by default 67

instructed_trigger_map

Trigger/event values for instructed left/right motor imagery trials, with keys instructed_left, instructed_right
and int or list of int values

organized_data

Numpy array of shape (trials, channels, time) containing extracted epochs

labels

Numpy array of shape (trials,) where labels[trial_num] corresponds to organized_data[trial_num]
The labels are integers corresponing to instructed_trigger_map[instructed_left] and instructed_trigger_map[instructed_right]

Module contents

Python package to help build experiments at the Brain Institute. Currently it’s use is to help using the BCI.

Data Processing: data_processing.default_instructed_trigmap data_processing.default_free_trigmap data_processing.fc_c_cp_1through6 data_processing.organize_xdf data_processing.get_sliding_window_partition data_processing.lowpass_filter data_processing.LowpassWrapper

BCI: bci.classifier_process.run_classifier bci.classifier_process.ClassifierProcess

bci.inlets.WindowInlet bci.inlets.ClassifierInlet

bci.models.DummyClassifier bci.models.get_trained_CSP_LDA

Other: electrode_info.csv electrode_info.json