An easy-to-use platform for EEG experimentation in the classroom
moving files
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app/utils/jupyter/cells.ts
app/utils/pyodide/cell.js
app/utils/jupyter/cells.ts
app/utils/pyodide/cell.js
app/utils/jupyter/functions.ts
app/utils/pyodide/functions.js
app/utils/jupyter/functions.ts
app/utils/pyodide/functions.js
app/utils/jupyter/pipes.ts
app/utils/pyodide/pipes.js
app/utils/jupyter/pipes.ts
app/utils/pyodide/pipes.js
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app/utils/jupyter/utils.py
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app/utils/jupyter/utils.py
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from glob import glob
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import os
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from collections import OrderedDict
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from mne import create_info, concatenate_raws, viz
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from mne.io import RawArray
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from mne.channels import read_montage
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import pandas as pd
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import numpy as np
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import seaborn as sns
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from matplotlib import pyplot as plt
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sns.set_context('talk')
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sns.set_style('white')
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def load_data(fnames, sfreq=128., replace_ch_names=None):
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"""Load CSV files from the /data directory into a Raw object.
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Args:
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fnames (array): CSV filepaths from which to load data
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Keyword Args:
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sfreq (float): EEG sampling frequency
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replace_ch_names (dict or None): dictionary containing a mapping to
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rename channels. Useful when an external electrode was used.
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Returns:
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(mne.io.array.array.RawArray): loaded EEG
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"""
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raw = []
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print(fnames)
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for fname in fnames:
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# read the file
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data = pd.read_csv(fname, index_col=0)
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data = data.dropna()
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# get estimation of sampling rate and use to determine sfreq
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# yes, this could probably be improved
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srate = 1000 / (data.index.values[1] - data.index.values[0])
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if srate >= 200:
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sfreq = 256
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else:
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sfreq = 128
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# name of each channel
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ch_names = list(data.columns)
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# indices of each channel
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ch_ind = list(range(len(ch_names)))
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if replace_ch_names is not None:
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ch_names = [c if c not in replace_ch_names.keys()
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else replace_ch_names[c] for c in ch_names]
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# type of each channels
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ch_types = ['eeg'] * (len(ch_ind) - 1) + ['stim']
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montage = read_montage('standard_1005')
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# get data and exclude Aux channel
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data = data.values[:, ch_ind].T
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# create MNE object
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info = create_info(ch_names=ch_names, ch_types=ch_types,
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sfreq=sfreq, montage=montage)
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raw.append(RawArray(data=data, info=info))
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# concatenate all raw objects
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raws = concatenate_raws(raw)
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return raws
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def plot_topo(epochs, conditions=OrderedDict()):
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palette = sns.color_palette("hls", len(conditions) + 1)
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evokeds = [epochs[name].average() for name in (conditions)]
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evoked_topo = viz.plot_evoked_topo(
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evokeds, vline=None, color=palette[0:len(conditions)], show=False)
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evoked_topo.patch.set_alpha(0)
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evoked_topo.set_size_inches(10, 8)
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for axis in evoked_topo.axes:
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for line in axis.lines:
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line.set_linewidth(2)
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legend_loc = 0
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labels = [e.comment if e.comment else 'Unknown' for e in evokeds]
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legend = plt.legend(labels, loc=legend_loc, prop={'size': 20})
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txts = legend.get_texts()
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for txt, col in zip(txts, palette):
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txt.set_color(col)
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return evoked_topo
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def plot_conditions(epochs, ch_ind=0, conditions=OrderedDict(), ci=97.5, n_boot=1000,
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title='', palette=None,
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diff_waveform=(4, 3)):
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"""Plot Averaged Epochs with ERP conditions.
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Args:
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epochs (mne.epochs): EEG epochs
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Keyword Args:
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conditions (OrderedDict): dictionary that contains the names of the
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conditions to plot as keys, and the list of corresponding marker
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numbers as value. E.g.,
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conditions = {'Non-target': [0, 1],
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'Target': [2, 3, 4]}
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ch_ind (int): index of channel to plot data from
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ci (float): confidence interval in range [0, 100]
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n_boot (int): number of bootstrap samples
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title (str): title of the figure
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palette (list): color palette to use for conditions
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ylim (tuple): (ymin, ymax)
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diff_waveform (tuple or None): tuple of ints indicating which
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conditions to subtract for producing the difference waveform.
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If None, do not plot a difference waveform
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Returns:
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(matplotlib.figure.Figure): figure object
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(list of matplotlib.axes._subplots.AxesSubplot): list of axes
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"""
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if isinstance(conditions, dict):
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conditions = OrderedDict(conditions)
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if palette is None:
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palette = sns.color_palette("hls", len(conditions) + 1)
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X = epochs.get_data()
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times = epochs.times
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y = pd.Series(epochs.events[:, -1])
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fig, ax = plt.subplots()
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for cond, color in zip(conditions.values(), palette):
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sns.tsplot(X[y.isin(cond), ch_ind], time=times, color=color,
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n_boot=n_boot, ci=ci)
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if diff_waveform:
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diff = (np.nanmean(X[y == diff_waveform[1], ch_ind], axis=0) -
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np.nanmean(X[y == diff_waveform[0], ch_ind], axis=0))
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ax.plot(times, diff, color='k', lw=1)
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ax.set_title(epochs.ch_names[ch_ind])
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ax.axvline(x=0, color='k', lw=1, label='_nolegend_')
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ax.set_xlabel('Time (s)')
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ax.set_ylabel('Amplitude (uV)')
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ax.set_xlabel('Time (s)')
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ax.set_ylabel('Amplitude (uV)')
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# Round y axis tick labels to 2 decimal places
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# ax.yaxis.set_major_formatter(FormatStrFormatter('%.2f'))
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if diff_waveform:
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legend = (['{} - {}'.format(diff_waveform[1], diff_waveform[0])] +
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list(conditions.keys()))
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else:
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legend = conditions.keys()
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ax.legend(legend)
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sns.despine()
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plt.tight_layout()
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if title:
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fig.suptitle(title, fontsize=20)
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fig.set_size_inches(10, 8)
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return fig, ax
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def get_epochs_info(epochs):
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return [*[{x: len(epochs[x])} for x in epochs.event_id], {"Drop Percentage": round((1 - len(epochs.events)/len(epochs.drop_log)) * 100, 2)}, {"Total Epochs": len(epochs.events)}]