关于
DEAP数据集是一个常用的情绪分类公共数据，在日常研究中经常被使用到。如何合理地预处理DEAP数据集，对于后端任务的成功与否，非常重要。本文主要介绍DEAP数据集的预处理流程。
工具
图片来源：DEAP: A Dataset for Emotion Analysis using Physiological and Audiovisual Signals
DEAP数据集详细描述：https://www.eecs.qmul.ac.uk/mmv/datasets/deap/doc/tac_special_issue_2011.pdf
方法实现
加载.bdf文件+去除非脑电通道
bdf_file_name = 's{:02d}.bdf'.format(subject_id)
bdf_file_path = os.path.join(root_folder, bdf_file_name)
print('Loading .bdf file {}'.format(bdf_file_path))
raw = mne.io.read_raw_bdf(bdf_file_path, preload=True, verbose=False).load_data()
ch_names = raw.ch_names
eeg_channels = ch_names[:N_EEG_electrodes]
non_eeg_channels = ch_names[N_EEG_electrodes:]
stim_ch_name = ch_names[-1]
stim_channels = [ stim_ch_name ]设置脑电电极规范
raw_copy = raw.copy()
raw_stim = raw_copy.pick_channels(stim_channels)
raw.pick_channels(eeg_channels)
print('Setting montage with BioSemi32 electrode locations')
biosemi_montage = mne.channels.make_standard_montage(kind='biosemi32', head_size=0.095)
raw.set_montage(biosemi_montage)带通滤波器（4-45Hz），陷波滤波器（50Hz）
print('Applying notch filter (50Hz) and bandpass filter (4-45Hz)')
raw.notch_filter(np.arange(50, 251, 50), n_jobs=1, fir_design='firwin')
raw.filter(4, 45, fir_design='firwin')脑电通道相对于均值重标定
# Average reference. This is normally added by default, but can also be added explicitly.
print('Re-referencing all electrodes to the common average reference')
raw.set_eeg_reference()获取事件标签
print('Getting events from the status channel')
events = mne.find_events(raw_stim, stim_channel=stim_ch_name, verbose=True)
if subject_id<=23:
# Subject 1-22 and Subjects 23-28 have 48 channels.
# Subjects 29-32 have 49 channels.
# For Subjects 1-22 and Subject 23, the stimuli channel has the name 'Status'
# For Subjects 24-28, the stimuli channel has the name ''
# For Subjects 29-32, the stimuli channels have the names '-0' and '-1'
pass
else:
# The values of the stimuli channel have to be changed for Subjects 24-32
# Trigger channel has a non-zero initial value of 1703680 (consider using initial_event=True to detect this event)
events[:,2] -= 1703680 # subtracting initial value
events[:,2] = events[:,2] % 65536 # getting modulo with 65536
print('')
event_IDs = np.unique(events[:,2])
for event_id in event_IDs:
col = events[:,2]
print('Event ID {} : {:05}'.format(event_id, np.sum( 1.0*(col==event_id) ) ) )获取事件对应信号
inds_new_trial = np.where(events[:,2] == 4)[0]
events_new_trial = events[inds_new_trial,:]
baseline = (0, 0)
print('Epoching the data, into [-5sec, +60sec] epochs')
epochs = mne.Epochs(raw, events_new_trial, event_id=4, tmin=-5.0, tmax=60.0, picks=eeg_channels, baseline=baseline, preload=True)ICA去除伪迹
print('Fitting ICA to the epoched data, using {} ICA components'.format(N_ICA))
ica = ICA(n_components=N_ICA, method='fastica', random_state=23)
ica.fit(epochs)
ICA_model_file = os.path.join(ICA_models_folder, 's{:02}_ICA_model.pkl'.format(subject_id))
with open(ICA_model_file, 'wb') as pkl_file:
pickle.dump(ica, pkl_file)
# ica.plot_sources(epochs)
print('Plotting ICA components')
fig = ica.plot_components()
cnt = 1
for fig_x in fig:
print(fig_x)
fig_ICA_path = os.path.join(ICA_components_folder, 's{:02}_ICA_components_{}.png'.format(subject_id, cnt))
fig_x.savefig(fig_ICA_path)
cnt += 1
# Inspect frontal channels to check artifact removal
# ica.plot_overlay(raw, picks=['Fp1'])
# ica.plot_overlay(raw, picks=['Fp2'])
# ica.plot_overlay(raw, picks=['AF3'])
# ica.plot_overlay(raw, picks=['AF4'])
N_excluded_channels = len(ica.exclude)
print('Excluding {:02} ICA component(s): {}'.format(N_excluded_channels, ica.exclude))
epochs_clean = ica.apply(epochs.copy())
#############################
print('Plotting PSD of epoched data')
fig = epochs_clean.plot_psd(fmin=4, fmax=45, area_mode='range', average=False, picks=eeg_channels, spatial_colors=True)
fig_PSD_path = os.path.join(PSD_folder, 's{:02}_PSD.png'.format(subject_id))
fig.savefig(fig_PSD_path)
print('Saving ICA epoched data as .pkl file')
mneraw_pkl_path = os.path.join(mneraw_as_pkl_folder, 's{:02}.pkl'.format(subject_id))
with open(mneraw_pkl_path, 'wb') as pkl_file:
pickle.dump(epochs_clean, pkl_file)
epochs_clean_copy = epochs_clean.copy()数据降采样和保存
print('Downsampling epoched data to 128Hz')
epochs_clean_downsampled = epochs_clean_copy.resample(sfreq=128.0)
print('Plotting PSD of epoched downsampled data')
fig = epochs_clean_downsampled.plot_psd(fmin=4, fmax=45, area_mode='range', average=False, picks=eeg_channels, spatial_colors=True)
fig_PSD_path = os.path.join(PSD_folder, 's{:02}_PSD_downsampled.png'.format(subject_id))
fig.savefig(fig_PSD_path)
data = epochs_clean.get_data()
data_downsampled = epochs_clean_downsampled.get_data()
print('Original epoched data shape: {}'.format(data.shape))
print('Downsampled epoched data shape: {}'.format(data_downsampled.shape))
###########################################
EEG_channels_table = pd.read_excel(DEAP_EEG_channels_xlsx_path)
EEG_channels_geneva = EEG_channels_table['Channel_name_Geneva'].values
channel_pick_indices = []
print('\nPreparing EEG channel reordering to comply with the Geneva order')
for (geneva_ch_index, geneva_ch_name) in zip(range(N_EEG_electrodes), EEG_channels_geneva):
bdf_ch_index = eeg_channels.index(geneva_ch_name)
channel_pick_indices.append(bdf_ch_index)
print('Picking source (raw) channel #{:02} to fill target (npy) channel #{:02} | Electrode position: {}'.format(bdf_ch_index + 1, geneva_ch_index + 1, geneva_ch_name))
ratings = pd.read_csv(ratings_csv_path)
is_subject = (ratings['Participant_id'] == subject_id)
ratings_subj = ratings[is_subject]
trial_pick_indices = []
print('\nPreparing EEG trial reordering, from presentation order, to video (Experiment_id) order')
for i in range(N_trials):
exp_id = i+1
is_exp = (ratings['Experiment_id'] == exp_id)
trial_id = ratings_subj[is_exp]['Trial'].values[0]
trial_pick_indices.append(trial_id - 1)
print('Picking source (raw) trial #{:02} to fill target (npy) trial #{:02} | Experiment_id: {:02}'.format(trial_id, exp_id, exp_id))
# Store clean and reordered data to numpy array
epoch_duration = data_downsampled.shape[-1]
data_npy = np.zeros((N_trials, N_EEG_electrodes, epoch_duration))
print('\nStoring the final EEG data in a numpy array of shape {}'.format(data_npy.shape))
for trial_source, trial_target in zip(trial_pick_indices, range(N_trials)):
data_trial = data_downsampled[trial_source]
data_trial_reordered_channels = data_trial[channel_pick_indices,:]
data_npy[trial_target,:,:] = data_trial_reordered_channels.copy()
print('Saving the final EEG data in a .npy file')
np.save(npy_path, data_npy)
print('Raw EEG has been filtered, common average referenced, epoched, artifact-rejected, downsampled, trial-reordered and channel-reordered.')
print('Finished.')
图片来源： Blog - Ayan's Blog
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