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=45.,
        type=float
    )
    return parser.parse_args()


args = parse_args()

subj_name = args.subj

data_dir = f'./data/{subj_name}/'

with open(os.path.join(data_dir, 'train_info.yml'), 'r') as f:
    info = yaml.safe_load(f)
sessions = info['sessions']
event_id = {'rest': 0}
for f in sessions.keys():
    event_id[f] = neo.FINGERMODEL_IDS[f]

trial_duration = config_info['buffer_length']
# preprocess raw
raw = neo.raw_loader(data_dir, sessions, do_rereference=False, unify_label=True, upsampled_epoch_length=trial_duration, ori_epoch_length=5, mov_trial_ind=[2], rest_trial_ind=[1])

###############################################################################
# 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]
sub_band_width = 5.
sub_band_step = 5.
alpha = 0.4

# 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, freq_band, sub_band_width,
                             sub_band_step, alpha,
                             tmin=0., tmax=0.5,
                             cv=cv,
                             return_class_dis=True, verbose=False)

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

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

subband_fmin = np.arange(freq_band[0],
                              freq_band[1] - sub_band_width + 1.,
                              sub_band_step)
subband_fmax = np.arange(freq_band[0] + sub_band_width,
                              freq_band[1] + 1., sub_band_step)
n_subband = len(subband_fmin)

subband_fmean = (subband_fmin + subband_fmax) / 2

x = subband_fmean

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

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'))


print(f'Optimal frequency band for this subject is '
      f'{best_freq[0][0]} - {best_freq[0][1]} Hz')

plt.show()