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توضیحات

py for BI

ویژگی	مقدار
سیستم عامل	-
نام فایل	BIpy-1
نام	BIpy
نسخه کتابخانه	1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Adrien Schurger-Foy
ایمیل نویسنده	-
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/BIpy/
مجوز	BSD 3-Clause License

# BIpy package Python package to help build experiments at the Brain Institute. It’s main purpose is to help using the real-time BCI. ## Module contents 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 bci.stims.NeuroFeedbackStim bci.training_session.TraininSession 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 Session: session.Session Other: electrode_info.csv electrode_info.json ### Example usage of Session ```python from BIpy.sesssion import Session import random import numpy as np # [OMITTED]: Psychopy stuff such as making window, stims... def instructions(logger): # display some instructions text_stim.draw() win.flip() event.waitKeys() # hide this trial so it doesn't show up in the saved csv file logger.hide_trial() def discrimination_RT_trial(logger): # wait for keypress to start trial event.waitKeys() # present fixation cross (random amount of time) cross.draw() win.flip() core.wait(random.uniform(1,2)) # present l/r arrow at random is_left = random.choice([0,1]) if is_left: left_arrow.draw() else: right_arrow.draw() # get reaction time win.flip() key_pressed, RT = event.waitKeys(keyList=['0', '1'], timeStamped=core.Clock())[0] # log key pressed, RT, and which arrow was displayed to a csv file info = {'RT': RT, 'key_pressed': key_pressed, 'is_left': is_left} logger.log(info) # get current block and trial number current_block_num, current_trial_num = logger.get_current() # get all past trials this block past_trials = logger.log_history[current_block_num][:current_trial_num] # get average past correct reaction time for the current block mean_rt = np.mean([ trial['rt'] for trial past_trials if str(trial['is_left']) == trial['key_pressed'] ]) # display feedback to the participant if str(is_left) != key_pressed: text_stim.text = 'Wrong side!' elif RT > mean_rt: text_stim.text = 'Slower than average' else: text_stim.text = 'Faster than average' text_stim.draw() win.flip() core.wait(1) # set up trials and blocks # first block will just be instructions instruction_block = [instructions] # second block will be 4 discrimination RT trials trial_block = [discrimination_RT_trial, discrimination_RT_trial, discrimination_RT_trial, discrimination_RT_trial] blocks = [instruction_block, trial_block] # some info about the experiment we might want saved refresh_rate = win.monitorFramePeriod sess_id = input('Enter session id:') info = {'sess_id': sess_id, 'refresh_rate': refresh_rate} session = Session(info, blocks) session.run() # exit Psychopy win.close() core.quit() ``` ### Example usage of BCI ```python import BYpy.bci as bci import numpy as np from Psychopy import event, core, visual from pylsl import resolve_stream, StreamInlet # do Psychopy stuff win = visual.Window(monitor='testMonitor', fullscr=True) text_stim = visual.TextStim(win, color='grey') # train classifier on previously recorded data data = np.load('some_data.npy') labels = np.load('some_labels.npy') clf = bci.models.get_trained_CSP_LDA(data, labels) # start a classifier process that runs in the background, # in_source_id='myuid323457' corresponds to the stream of data from dry eeg that will be classified via CSP + LDA # window_size=500 means the classifier will use the past 500 samples from the dry eeg as input clfproc = bci.classifier_process.ClassifierProcess(clf, in_source_id='myuid323457', out_source_id='classifier_output', window_size=500) # listen to the output of the classifier inlet = bci.inlets.ClassifierInlet(source_id='classifier_output') for _ in range(100): # get a new sample from the classifer every second for 100 seconds core.wait(1) sample, timestamp = inlet.pull_sample() # display the classifier's prediction on screen text_stim.text = str(sample) text_stim.draw() win.flip() # stop the classifier process, if not it could keep running in the background indefinetly clfproc.kill() clfproc.join() clfproc.close() # exit Psychopy win.close() core.quit() ``` ### Example usage of TrainingSession ```python from training_session import TrainingSession from psychopy import visual, core win = visual.Window(monitor='testMonitor', fullscr=True) sess = TrainingSession(win, iterations=5, trials_per_iteration=20) sess.run() win.close() core.quit() ``` ## 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, step=1) Splits data into several windows. Deprecated (waste of memory) 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 step Step size of the sliding window windowed_data Array of shape (windows, channels, time) windowed_labels 1d array of labels corresponding to each window ### BIpy.data_processing.get_windows(data, labels, window_size, step_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 step Step size of the sliding window 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] ### BIpy.data_processing.sliding_window_iter(total_size, window_size) Helper function to get a sliding window view of indexes ## BIpy.session module ### class BIpy.session.Session(info, blocks, use_json=False, hide_info=False, suppress_save=False) Bases: `object` A class that handles the execution and data collection of a Psychopy experiment info Information about the session, ex: {‘session_id’: 1234} blocks List of blocks, each block a list of trials, each trial a function that takes exactly one input: ‘logger’ each trial function should execute the intended trial use_json Flag to indicate whether or not to save data to json on calling save() to_hide List of trials to be ignored when saving to csv log_history Used to store trial data on call of log() _iq Index queue, list of tuples corresponding to the indeces of each trial function in blocks run Iterates through and runs all trial functions of blocks in order log(to_log: dict, save_to_file=True) Stores to_log in log_history[current_block_num][current_trial_num] hide_trial Marks current trial to be ignored when saving to csv save Attempts to find an apropriate filename, and calls save_to_csv(filename) if use_json is True, also calls save_to_json(filename) save_to_csv(filename) Saves the Session’s info, and log_history to filename in csv format get_current Returns the current block and trial nuber save_to_json(filename) Saves as much information about the current Session object as possible to filename in json format #### \__init__(info, blocks, use_json=False, hide_info=False, suppress_save=False) info Information about the session, ex: {‘session_id’: 1234} blocks List of blocks, each block a list of trials, each trial a function that takes exactly one input: ‘logger’ each trial function should execute the intended trial use_json Flag to indicate whether or not to save data to json on calling save() suppress_save Flag to indicate log() calls should never save to file save to file hide_info Flag to indicate if ‘info’ should be saved to csv #### get_current() Returns the current block and trial nuber: tuple (block, trial) #### hide_trial() Marks current trial to be ignored when saving to csv #### log(to_log, save_to_file=True) Stores to_log in log_history[current_block_num][current_trial_num] to_log: dict Python dict with information to save from the current trial, ex: {‘RT’: .129486} save_to_file: bool, defaul=True Flag to save data to file this can take time so set to False if you want to log some data but the current trial needs strict timing, then call Session.save() a the end of the experiment #### run() Iterates through and runs all trial functions of blocks in order #### save() Attempts to find an apropriate filename, and calls save_to_csv(filename) If self.use_json is True, also calls save_to_json(filename) #### save_to_csv(filename) Saves the Session’s info, and log_history to filename in csv format filename : str #### save_to_json(filename) Saves as much information about the current Session object as possible to filename in json format filename : str ### BIpy.session.is_jsonable(x) ## BIpy.bci.classifier_process module ### BIpy.bci.classifier_process.ClassifierProcess(clf, in_source_id='myuid323457', out_source_id='classifier_output', stream_no=0, window_size=500) Returns a multiprocessing.process of run_classifier clf Classifier to run in_source_id Pylsl stream source_id of incoming data to be fed to the classifier Default myuid323457 - dry EEG, for ActiChamp use 17010768 out_source_id Pylsl stream source_id for output of the classifier Default ‘classifier_output’ strem_no Index of the stream. Should be 0 or 1, ask Tian for help on this Default 0 window_size Number of samples required as input to the provided classifier clf If None, the function will attenpt to get this from clf.window_size Default None multiprocessing.process() of BIpy.classifier_process.run_classifier() ### BIpy.bci.classifier_process.run_classifier(clf, in_source_id='myuid323457', out_source_id='classifier_output', stream_no=0, window_size=None) Runs a real-time classifier untill killed clf Classifier to run. Must be able to take the output of WindowInlet.pull_window() as input to clf.predict_proba(), and clf.predict_proba(data) must output a single float in_source_id Pylsl stream source_id of incoming data to be fed to the classifier Default myuid323457 - dry EEG, for ActiChamp use 17010768 out_source_id Pylsl stream source_id for output of the classifier strem_no Index of the stream. Should be 0 or 1, ask Tian for help on this Default 0 window_size Number of samples required as input to the provided classifier clf If None, the function will attenpt to get this from clf.window_size For every input recieved, immediately pushes the provided classifiers output (predict_proba(data)) to lsl ## BIpy.bci.inlets module ### class BIpy.bci.inlets.ClassifierInlet(source_id='classifier_output') Bases: `object` A class used like a pylsl inlet, but made for getting the output of a real-time classifier inlet Pylsl inlet from which the output of a classifier is pulled pull_sample returns inlet.pull_sample() #### \__init__(source_id='classifier_output') source_id Pylsl stream source_id for incoming data Default ‘classifier_output’ #### pull_sample() returns inlet.pull_sample() ### class BIpy.bci.inlets.WindowInlet(source_id='myuid323457', stream_no=0, window_size=500) Bases: `object` A class used like a pylsl inlet, but that returns window_size past samples on each pull_window Attributes ———- inlet : pylsl.StreamInlet > Pylsl inlet from which real-time EEG data is pulled window_size Width in samples (not seconds) of the sliding window window List of window_size last samples pulled from inlet pull_window Pulls the last window_size from inlet #### \__init__(source_id='myuid323457', stream_no=0, window_size=500) > source_id > Pylsl stream source_id of incoming data buffered for sliding window > Default myuid323457 - dry EEG, for ActiChamp use 17010768 > stream_no > Index of the stream. Should be 0 or 1, ask Tian for help on this > Default 0 > window_size > Number of past samples returned on call of pull_window > Default 500 > transpose_output > If True transposes the output of the window so that it has the shape expected by mne’s CSP #### pull_window(window_size=None, timeout=0.25) Pulls the last window_size samples from inlet window_size Width in samples (not seconds) of the sliding window If None, uses self.window_siz, Default None timeout Timeout in seconds passed to pylsl.inlet.pull_chunk Default .25 window list of window_size last samples pulled from inlet ## BIpy.bci.models module ### class BIpy.bci.models.DummyClassifier() Bases: `object` Dummy classifier for testing purpose #### \__init__() #### predict_proba(data) returns input[0] ### class BIpy.bci.models.WrappedCSPLDAClassifier(data_channels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], window_size=1000, preprocessing=<BIpy.data_processing.LowpassWrapper object>) Bases: `object` Wrapper class for using an sklearn csp+lda pipeline in a BIpy.bci.ClassifierProcess predict_proba(self, window: np.array) takes the output form a WindowInlet and returns the probability (according to the csp+lda classifier) that the right hand was imagined def fit(self, data, labels): calls fit(data, labels) on the csp+lda classifier #### \__init__(data_channels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], window_size=1000, preprocessing=<BIpy.data_processing.LowpassWrapper object>) data_channels Channels that the classifier should use as input window_size number of samples of eeg data the classifier should use as input preprocessing Step added to the start of the csp+lda sklearn pipeline #### fit(data, labels) calls fit(data, labels) on the csp+lda classifier #### predict_proba(window) takes the output form a WindowInlet and returns the probability (according to the csp+lda classifier) that the right hand was imagined ### BIpy.bci.models.get_trained_CSP_LDA(data, labels, window_size=None, preprocessing=<BIpy.data_processing.LowpassWrapper object>, step_size=None) Returns a trained sklearn pipeline of [csp, lda] data Data to train the classifier on Shape (trials, channels, time) labels 1d array of labels to the training data window_size Size in samples (not seconds) the classifier should be trained on If None, the function will trian with each entire trial as input Default None preprocessing Preprocessing step to add at the beggining of the sklearn pipeline Default BIpy.preprocessing.LowpassWraspper() step_size Stride/step size passed to BIpy.data_processing.get_windows() If None, classifier will be trained on raw data and get_windows() is never used clf A trained csp + lda Pipeline ## BIpy.bci.stims module ### class BIpy.bci.stims.NeuroFeedbackStim(win, resolution) Bases: `object` Progress bar style stim for displaying classifier output to the subject draw(self, porportion=None) draws the stim #### \__init__(win, resolution) win window in which the stim will be drawn resolution number of segments in on each side of the “progress bar” #### draw(proportion=None) draws the stim proportion value between 0 and 1. At 0 the “progress bar” reaches all the way to the left, and at 1 it reaches the right ## BIpy.bci.training_session module ### BIpy.bci.training_session.TrainingSession(win, iterations, trials_per_iteration, clf=<BIpy.bci.models.WrappedCSPLDAClassifier object>, trial_duration=4, sampling_rate=500, data_channels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19], window_size=500, step_size=100, eeg_source_id='myuid323457', eeg_stream_no=0, resolution=6) Returns a Session object that runs an iterative training session. The returned Session performs a block of neurofeedback trials, then re-trains the classifier on the newly collected data and runs a new block of neurofeedback trials. The EEG data, classifier predictions and true labels are saved to files. The defaults of this function are suitable for dry EEG win window in which the session will occur iterations number of blocks/ number of times the classifier is re-trained on new data trials_per_iteration number of trials per block clf the classifier used to predict left/right motor imagery trial_duration duration in seconds of each trial sampling_rate sampling rate of data sent to the EEG lsl stream data_channels indeces of the electrode channels the classifier should use as input window_size: int, default 500 number of samples the given classifier should take as input at once step_size the given classifier is trained on multiple windows spanning the data collected in one trial. step_size is the gap (in samples) between the start of each window. if one trial contains 800 samples of eeg data, the window size is 400 and the step size is 200, then the classifier will be trained using 3 windows for each trial: samples 0-400, 200-600, and 400-800 the window size, step size and samples per trial need not line up perfectly, all data will be used for training regardless (but the last step size will differ from the rest) eeg_source_id Pylsl stream source_id of incoming eeg data to be fed to the classifier Default myuid323457 - dry EEG, for ActiChamp use 17010768 eeg_stream_no Index of the stream. Should be 0 or 1, ask Tian for help on this resolution number of segments in on each side of the BIpy.bci.stims.NeuroFeedbackStim displayed on screen

نیازمندی

مقدار	نام
==1.20.3	numpy
==1.13.6	pylsl
==2021.2.0	psychopy
==0.23.0	mne
==1.16.3	pyxdf
==0.24.2	scikit-learn

نحوه نصب

نصب پکیج whl BIpy-1:

pip install BIpy-1.whl

نصب پکیج tar.gz BIpy-1:

pip install BIpy-1.tar.gz