- MNE version: 1.6.1
- Platform - Kaggle
i tried extracting the bands result values are coming zero
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.baseline import rescale
from mne.datasets import somato
from mne.stats import bootstrap_confidence_interval
iter_freqs = [("Theta", 4, 7), ("Alpha", 8, 12), ("Beta", 13, 25), ("Gamma", 30, 45)]
# set epoching parameters
event_id, tmin, tmax = 7, -1.0, 3.0
baseline = None
raw_fname="/kaggle/input/deap-datasetraw-data/s01.bdf"
# get the header to extract events
raw1 = mne.io.read_raw_bdf(raw_fname)
del_channels =['EXG1', 'EXG2', 'EXG3', 'EXG4', 'EXG5', 'EXG6', 'EXG7', 'EXG8', 'GSR1', 'GSR2', 'Erg1', 'Erg2', 'Resp', 'Plet', 'Temp']
raw = raw1.drop_channels(del_channels)
print(raw.info['ch_names'])
events = mne.find_events(raw)
print(events)
print(events,"apple")
frequency_map = list()
for band, fmin, fmax in iter_freqs:
# (re)load the data to save memory
raw = mne.io.read_raw_bdf(raw_fname)# we just look at gradiometers
raw.load_data()
# bandpass filter
raw.filter(
fmin,
fmax,
n_jobs=None, # use more jobs to speed up.
l_trans_bandwidth=1, # make sure filter params are the same
h_trans_bandwidth=1,
) # in each band and skip "auto" option.
# epoch
epochs = mne.Epochs(
raw,
events,
event_id,
tmin,
tmax,
baseline=baseline,
reject=None,
preload=True,
)
# remove evoked response
epochs.subtract_evoked()
# get analytic signal (envelope)
epochs.apply_hilbert(envelope=True)
frequency_map.append(((band, fmin, fmax), epochs.average()))
del epochs
del raw
# Helper function for plotting spread
def stat_fun(x):
"""Return sum of squares."""
return np.sum(x**2, axis=0)
# Plot
fig, axes = plt.subplots(4, 1, figsize=(10, 7), sharex=True, sharey=True)
colors = plt.colormaps["winter_r"](np.linspace(0, 1, 4))
for ((freq_name, fmin, fmax), average), color, ax in zip(
frequency_map, colors, axes.ravel()[::-1]
):
times = average.times * 1e3
gfp = np.sum(average.data**2, axis=0)
gfp = mne.baseline.rescale(gfp, times, baseline=(None, 0))
ax.plot(times, gfp, label=freq_name, color=color, linewidth=2.5)
print(gfp)
ax.axhline(0, linestyle="--", color="grey", linewidth=2)
ci_low, ci_up = bootstrap_confidence_interval(
average.data, random_state=0, stat_fun=stat_fun
)
ci_low = rescale(ci_low, average.times, baseline=(None, 0))
ci_up = rescale(ci_up, average.times, baseline=(None, 0))
ax.fill_between(times, gfp + ci_up, gfp - ci_low, color=color, alpha=0.3)
ax.grid(True)
ax.set_ylabel("GFP")
ax.annotate(
"%s (%d-%dHz)" % (freq_name, fmin, fmax),
xy=(0.95, 0.8),
horizontalalignment="right",
xycoords="axes fraction",
)
ax.set_xlim(-1000, 3000)
axes.ravel()[-1].set_xlabel("Time [ms]")
output
