kraken/backend/band_selection.py

import os
import argparse
import matplotlib.pyplot as plt
import yaml
import numpy as np
from bci_core.frequencybandselection_helpers import freq_selection_class_dis
from dataloaders import neo
from sklearn.model_selection import ShuffleSplit
from settings.config import settings


config_info = settings.CONFIG_INFO


def parse_args():
    parser = argparse.ArgumentParser(
        description='Model validation'
    )
    parser.add_argument(
        '--subj',
        dest='subj',
        help='Subject name',
        default=None,
        type=str
    )
    parser.add_argument(
        '--band-min',
        dest='b_min',
        help='Band lower range',
        default=5.,
        type=float
    )
    parser.add_argument(
        '--band-max',
        dest='b_max',
        help='Band upper range',
        default=200.,
        type=float
    )
    parser.add_argument(
        '--band-div',
        help='Dividing point of low frequency bands and high frequency bands',
        default=50.,
        type=float
    )
    return parser.parse_args()


args = parse_args()

subj_name = args.subj

data_dir = os.path.join(settings.DATA_PATH, subj_name)

with open(os.path.join(data_dir, 'train_info.yml'), 'r') as f:
    info = yaml.safe_load(f)
sessions = info['sessions']

trial_duration = config_info['buffer_length']
ori_epoch_length = info.get('ori_epoch_length', 5.)
# preprocess raw
raw, event_id = neo.raw_loader(data_dir, sessions, reref_method=config_info['reref'], upsampled_epoch_length=trial_duration, ori_epoch_length=ori_epoch_length)

###############################################################################
# Pipeline with a frequency band selection based on the class distinctiveness
# ----------------------------------------------------------------------------
#
# Step1: Select frequency band maximizing class distinctiveness on
# training set.
#
# Define parameters for frequency band selection
freq_band = [args.b_min, args.b_max]
alpha = 0.4

subband_fmean = np.logspace(np.log10(args.b_min), np.log10(args.b_max), 30)
f_step = (np.log10(args.b_max) - np.log10(args.b_min)) / 30
subband_fmin = subband_fmean * (10 ** (-f_step))
subband_fmax = subband_fmean * (10 ** f_step)

# cross validation
cv = ShuffleSplit(n_splits=1, test_size=None, random_state=42)

# Select frequency band using training set
best_freq, all_class_dis = \
    freq_selection_class_dis(raw, subband_fmin, subband_fmax, alpha, args.band_div,
                             tmin=0., tmax=0.5,
                             cv=cv,
                             return_class_dis=True, verbose=False)

print(f'Selected frequency band (low) : {best_freq[0][0][0]} - {best_freq[0][0][1]} Hz')
print(f'Selected frequency band (high) : {best_freq[0][1][0]} - {best_freq[0][1][1]} Hz')

# split high frequency band
n_bands = np.round((best_freq[0][1][1] - best_freq[0][1][0]) / 50 + 1).astype(int)
split_high_freqs = np.logspace(np.log10(best_freq[0][1][0]), np.log10(best_freq[0][1][1]), n_bands)
print('Splited high frequency bands: ', split_high_freqs)


###############################################################################
# Plot selected frequency bands
# ----------------------------------
#
# Plot the class distinctiveness values for each sub_band,
# along with the highlight of the finally selected frequency band.

fig, ax = plt.subplots(1, 1, figsize=(8, 5))
ax.plot(subband_fmean, all_class_dis[0], marker='o')

ax.axvspan(best_freq[0][0][0], best_freq[0][0][1], color='orange', alpha=0.5)
ax.axvspan(best_freq[0][1][0], best_freq[0][1][1], color='orange', alpha=0.5)

ax.set_ylabel('Class distinctiveness')
ax.set_xlabel('Filter bank [Hz]')
ax.set_title('Class distinctiveness value of each subband')
ax.tick_params(labelsize='large')

fig.tight_layout()
fig.savefig(os.path.join(data_dir, 'freq_selection.pdf'))

plt.show()