Mouse_EEG_ChronicSleepRestriction_Kim_et_al

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README.md

Nine-day Continuous Recording of Mouse EEG under Chronic Sleep Restriction

version 1.0.2

https://gin.g-node.org/hiobeen/Mouse_EEG_ChronicSleepRestriction_Kim_et_al

A set of screw (2+1 channels of EEG & Accelerometer, 24-hour x 9-day recordings) and high-density (36 channels, intermittent 2-hour recordings) EEG (electroencephalogram) data obtained from freely-moving mice (mus musculus) (n = 9). Details of experimental method are described in the original research articles using the same dataset.

High density EEG: 36 channels of EEG: intermittent 2-hour recordings (Total 9 mice, 2x).

Figure 1

Screw EEG: 2+1 channels (EEG & accelerometer): ceaseless 9-day recording (Total 9 mice, 9x24 hours per mouse).

Figure 2

1. Publication information

Kim, B., Kocsis, B., Hwang, E., Kim, Y., Strecker, R. E., McCarley, R. W., & Choi, J. H. (2017). Differential modulation of global and local neural oscillations in REM sleep by homeostatic sleep regulation. Proceedings of the National Academy of Sciences, 114(9), E1727-E1736 [https://www.pnas.org/content/114/9/E1727].

Kim, B., Hwang, E., Strecker, R. E., Choi, J. H., & Kim, Y. (2020). Differential modulation of NREM sleep regulation and EEG topography by chronic sleep restriction in mice. Scientific reports, 10(1), 1-13 [https://www.nature.com/articles/s41598-019-54790-y].

See also: Hwang, E., Han, H. B., Kim, J. Y., & Choi, J. H. (2020). High-density EEG of auditory steady-state responses during stimulation of basal forebrain parvalbumin neurons. Scientific Data, 7(1), 1-9 [https://www.nature.com/articles/s41598-019-54790-y].

Visit our website [https://www.jeelab.net/] for full puiblication list.

JEELAB_logo

2. How to start

download: https://gin.g-node.org/hiobeen/Mouse_EEG_ChronicSleepRestriction_Kim_et_al

Large dataset can be downloaded through gin-cli tool.

After installation of gin-cli, run three commands below in your command line window.

 gin get hiobeen/Mouse_EEG_ChronicSleepRestriction_Kim_et_al
 
 cd Mouse_EEG_ChronicSleepRestriction_Kim_et_al/
 
 gin download --content

Reference: https://gin.g-node.org/G-Node/Info/wiki/GIN+CLI+Usage+Tutorial

3. Analysis tutorial

3-1) Loading EEG data

This tutorial document includes several examples of EEG data analysis using the dataset of [Mouse_EEG_ChronicSleepRestriction_Kim_et_al]. First, here, we need to import/define some functions that make data handling easier.

""" Define handy functions """
import warnings; warnings.filterwarnings(action='ignore')
import os; from glob import glob
from mne.io import read_raw_eeglab as load 
import numpy as np; import pandas as pd; import pickle
from matplotlib import pyplot as plt; plt.style.use('ggplot')
from matplotlib.pyplot import plot, xlim, ylim, title, xlabel, ylabel, imshow
from scipy.io import loadmat, savemat
from scipy.signal import butter, sosfiltfilt, find_peaks
from scipy.stats import pearsonr
from scipy.interpolate import interp1d
from scipy.misc import imresize as resize
def fft_half(x, fs=500): return np.fft.fft(x.reshape(-1))[:int(len(x)/2)]/len(x), np.linspace(0,fs/2,int(len(x)/2))
def find(x): return np.argwhere( x.flatten() ).flatten()
def smooth(x, span): return np.convolve(x,np.ones(int(span))/int(span), mode='same')
def interp_nan(x):
    placeholder = np.arange(x.size)
    return np.interp(placeholder, placeholder[np.isfinite(x)], data[np.isfinite(x)])
def bandpower(x, band, fs=500):
  X, freq = fft_half(x,fs)
  return np.mean( abs(X[np.where( (freq > band[0]) & (freq <= band[1]))])**2 )
def find_seg(ind): # Handy function
  ind_diff = np.diff(np.concatenate((np.ones(1)*-1,ind),axis=0))
  discontinuous = np.where(ind_diff!=1)[0]
  data_seg = np.zeros((len(discontinuous)-1,2),dtype='int')
  for idx in range(len(discontinuous)-1): data_seg[idx,:]=[ind[discontinuous[idx]] ,ind[ discontinuous[idx+1]-1]]
  return data_seg
def bandpass_filt(x, band=(8,18), fs=500, order=10):
  #if x.shape[0]>x.shape[1]: x=x.transpose()
  return sosfiltfilt(butter(order,[band[0]/(0.5*fs),band[1]/(0.5*fs)],btype='band',output='sos'),x).reshape(-1)
def get_upper_envelope(y, x, lower_limit = -10000000):
  peaks,_ = find_peaks(y, height=lower_limit)
  peaks_x = np.concatenate((np.array([t[0]]),x[peaks],np.array([t[-1]])), axis=0)
  peaks_y = np.concatenate((np.array([y[0]]),y[peaks],np.array([y[-1]])), axis=0)
  return interp1d(peaks_x, peaks_y, kind = 'cubic')(np.linspace(peaks_x[0], peaks_x[-1], len(y)))

After downloading dataset, you can import EEG dataset using the function load_data(). You need to define the path of dataset on your local machine, in [bids_path] variable.

# File loading demo

""" Important notice: Change these directories to fit your local environment """
bids_path = YOUR_OWN_PATH_1 # Ex: 'D:/eeg_files/data_BIDS/'
save_directory = YOUR_OWN_PATH_2 # Ex: 'D:/eeg_files/output/'
if not os.path.isdir( save_directory ): os.mkdir( save_directory )
figure_directory =  '%s/figures/'%save_directory; if not os.path.isdir( figure_directory ): os.mkdir( figure_directory )
figure_dpi = 300

step_names = ['BL', 'SR1', 'SR2', 'SR3', 'SR4', 'SR5', 'R1', 'R2', 'R3']
CAL = [1e-8, 1e-6] # Voltage calibration value set by eeglab.
def load_data(sbjIdx=0, sessIdx=0, verbose = True):
    fname_screw = '%ssub-%02d/ses-%02d/eeg/sub-%02d-%s_screw'%(bids_path,sbjIdx+1,sessIdx+1,sbjIdx+1,step_names[sessIdx])
    fname_hdeeg = '%ssub-%02d/ses-%02d/eeg/sub-%02d-%s_hdeeg'%(bids_path,sbjIdx+1,sessIdx+1,sbjIdx+1,step_names[sessIdx])
    
    # Load screw EEG data
    print( '** Data loading ... Subject %d, Session %d (Step: %s) **'%(sbjIdx+1, sessIdx+1,step_names[sessIdx]))
    EEG_screw = load( fname_screw+'.set', preload = True, verbose = False)
    if ~hasattr(EEG_screw, 'data'): EEG_screw.data = EEG_screw.get_data()
    EEG_screw.data = EEG_screw.data/CAL[0] # Voltage value calibration
    if verbose: print( 'Screw data shape: [%d channels x %d times]'%EEG_screw.data.shape )
    chInfo_screw = pd.read_csv(fname_screw + '_eeg_channels.tsv', delimiter='\t')
    channel_status = list( chInfo_screw['status'] )
    screw_bad_channels = [i for i in range(len(channel_status)) if channel_status[i].__contains__("bad")]
    if verbose: print( 'Screw bad channels: %s'%screw_bad_channels )
    EEG_screw.bad_channels = np.array( screw_bad_channels )
    
    # Load hdEEG data if exist
    if os.path.exists(fname_hdeeg+'.set'):
        EEG_hdeeg = load( fname_hdeeg+'.set', preload=True, verbose = False)
        if ~hasattr(EEG_hdeeg, 'data'): EEG_hdeeg.data = EEG_hdeeg.get_data()
        EEG_hdeeg.data = EEG_hdeeg.data/CAL[1] # Voltage value calibration
        if verbose: print( 'hdEEG data shape: [%d channels x %d times]'%EEG_hdeeg.data.shape )
        # Load channel info
        chInfo_hdeeg = pd.read_csv(fname_hdeeg + '_eeg_channels.tsv', delimiter='\t')
        channel_status = list( chInfo_hdeeg['status'] )
        hdeeg_bad_channels = [i for i in range(len(channel_status)) if channel_status[i].__contains__("bad")]
        if verbose: print( 'hdEEG bad channels: %s'%hdeeg_bad_channels )
        EEG_hdeeg.bad_channels = np.array( hdeeg_bad_channels )
    else:
        EEG_hdeeg = None
        print( '... hdEEG file does not exist' )
    # Sleep score data
    sleep_score = pd.read_csv(fname_screw + '_eeg_events.tsv', delimiter='\t') 

    return EEG_screw, EEG_hdeeg, sleep_score

Now we're all set for EEG signal processing.

3-2) Sleep wave analysis (SWA, Spindle)

Several analysis can be done on this data such as spectral power, slow wave activity, sleep spindles, traveling wave, interaction among different oscillatory activities. Here, we provide examples of the sleep wave analyses including SWA (slow wave activity) and spindle. For that, example EEG data from the dataset (subject 1, session 1) is loaded.

# Select sample data
EEG_screw, EEG_hdeeg, sleep_score = load_data(sbjIdx=0, sessIdx=0, verbose = True)
** Data loading ... Subject 1, Session 1 (Step: BL) **
Screw data shape: [4 channels x 43200000 times]
Screw bad channels: []
hdEEG data shape: [36 channels x 3600000 times]
hdEEG bad channels: [9, 12, 22, 27, 31, 34, 35]

1. Detection of SWA (Slow wave activity)

""" SWA Detection """
def detect_swa(data, fs=500, t=EEG_hdeeg.times, shift_band = (.05, 6),
               thr_duration = (0.12, 0.70), thr_std = 1, thr_xcorr = 0.7, verbose=False):
  
  # Data filtering & downshifting
  data_filt = bandpass_filt(data, shift_band, fs)
  data_shift= data.reshape(-1) - get_upper_envelope(data_filt,t)
  std_data = np.std( data_shift )

  # Get upper/lower indeces near zero
  cross_negative = np.insert( find( data_shift<0 ), 0,0)
  cross_positive = find( data_shift>=0 )
  ups = cross_negative[ find(np.diff(cross_negative)>1) ]+1
  downs = cross_positive[ find(np.diff(cross_positive)>1) ]
  if downs[0] < ups[0]:
    min_length = min(downs.shape[0], ups.shape[0]);
    updowns=np.array( [list(downs),list(ups) ])
  else:
    min_length = min(downs.shape[0], ups.shape[0]-1);
    updowns=np.array( [downs[0:min_length-1],ups[1:min_length] ])
    
  # Main inspection loop
  result, minmax = [], []
  for j in range(min_length-1):
    data_piece = data_shift[updowns[0][j]:updowns[1][j]]
    min_where = np.argmin(data_piece)
    n =len(data_piece)
    
    # Rejection criteria
    flag_long = n < (thr_duration[0]*fs)
    flag_short = n >= (thr_duration[1]*fs)
    flag_amptd = np.max(np.abs(data_piece)) < (std_data*thr_std)
    shift_squared = np.diff(data_filt[updowns[0][j]:updowns[1][j]-1])*np.diff(data_filt[updowns[0][j]+1:updowns[1][j]])
    n_inflection = find(shift_squared<0).shape[0]
    flag_inflection = n_inflection>2
    reject = flag_long | flag_short | flag_amptd | flag_inflection
    
    # Calculate correlation coefficient
    if not reject:
      min_where = np.argmin(data_piece)
      templateL = np.arange(0,min_where) / (min_where-1)
      templateR = np.arange(0,n-min_where)[::-1] / (n-min_where) 
      template = np.concatenate((templateL,templateR),axis=0)*np.min(data_piece)
      template[find(np.isnan(template))]=0
    
      r, _ = pearsonr( template, data_piece )
      if r > thr_xcorr:
        result.append([updowns[0][j],updowns[1][j]])
        minmax.append([ np.min(data_piece), np.max(data_piece) ])
  if verbose: print('[SWA detection] %d events are detected'%len(result))
  return np.array(result, dtype=int), np.array(minmax, dtype='float16')

""" Calculation: SWA detection """
data, t, fs = EEG_hdeeg.data[3,:], EEG_hdeeg.times, EEG_screw.info['sfreq']
swa_idx, swa_minmax = detect_swa(data, fs=fs, t=t, verbose=True);
[SWA detection] 7796 events are detected

Slow wave events detected by the function detect_swa() can be drawn as below.

""" Event-by-event visualizer """
cut_epoch = (-int(fs*2),int(fs*2)) # unit:sec
sample_events = [32, 49, 60, 85]
n_samples = len(sample_events)
counter = 1

# For easy visualization
indices_swa = np.zeros(data.shape, dtype='bool')
for ii in range(swa_idx.shape[0]): indices_swa[swa_idx[ii,0]:swa_idx[ii,1]] = True
data_swa = data.copy()
data_swa[np.where(~indices_swa)] = np.nan

# Visualization code
plt.figure(figsize=(12,12))
for evIdx in sample_events:#range(n_event):
    x_epoch = t[swa_idx[evIdx,0]+cut_epoch[0]:swa_idx[evIdx,0]+cut_epoch[1]]
    y_epoch = data[swa_idx[evIdx,0]+cut_epoch[0]:swa_idx[evIdx,0]+cut_epoch[1]]
    y_epoch_swa = data_swa[swa_idx[evIdx,0]+cut_epoch[0]:swa_idx[evIdx,0]+cut_epoch[1]]
    
    plt.subplot(n_samples,1,counter); counter+=1
    plot( x_epoch, y_epoch, 'k' )
    plot( x_epoch, y_epoch_swa, 'r' )
    ylim((-400,400))
    xlabel('Time (sec)')
    ylabel('Voltage (uV)')
    title('Slow waves around event #%03d'%evIdx)
    plt.gca().set_facecolor((1,1,1))
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig( '%sFig_QQ_TutorialSWA.jpg'%(figure_directory), dpi=figure_dpi )

png

2. Detection of Spindles

Similarly, detection and visualization of spindle events can be done by following scripts.

""" Spindle Detection Function """
from scipy import signal
def detect_spindle( data, fs=500 ):
    spindlef = bandpass_filt( data, band=(8,18), fs=fs )
    thr_updown = ( np.std(spindlef)*5, np.std(spindlef)*2.5 )
    thr_duration = 0.30 # sec
    thr_interval = 0.10 # sec
    hilb_amptd = np.abs( signal.hilbert( spindlef ))
    amptd_envelop = smooth( hilb_amptd, fs/5 )
    big_enough = np.where(amptd_envelop>thr_updown[1])[0]
    idx_merged = np.array([])
    if len(big_enough)>0:
        # Duration Thresholding
        start_end = find_seg( big_enough )
        long_enough = start_end[np.where( (start_end[:,1]-start_end[:,0])>(thr_duration*fs))[0],:]
        # Amplitude Thresholding
        include_flag = []
        for i in range(long_enough.shape[0]):
          data_to_inspect = data[range(long_enough[i,0],long_enough[i,1])]
          if len( np.where(data_to_inspect>thr_updown[0])[0])==0:
            include_flag.append(i)
        strong_enough = long_enough[include_flag,:]
        # Merging short interval events
        idx_merged = strong_enough.copy()
        interval = strong_enough[1:,0] - strong_enough[0:-1,1]
        id_to_merge = np.where(interval < thr_interval*fs)[0]
        for j in range(len(id_to_merge))[::-1]:
          idx_merged[id_to_merge[j],1]= idx_merged[id_to_merge[j]+1,1]
          idx_merged = np.delete(idx_merged, id_to_merge[j]+1,axis=0)
    return idx_merged, amptd_envelop

""" Calculation: Spindle """
data = EEG_hdeeg.data[3,:]
spindle_idx, amptd_envelop = detect_spindle( data, fs )

""" Spindle: event-by-event visualizer """
cut_epoch = (-int(fs*3),int(fs*3)) # unit:sec
n_event = spindle_idx.shape[0]

sample_events = [11,30]
n_samples = len(sample_events)
counter = 1
# Visualization code
plt.figure(figsize=(12,6))
for evIdx in sample_events:
    
    # Epoching
    x = t[spindle_idx[evIdx,0]:spindle_idx[evIdx,1]]
    y = data[spindle_idx[evIdx,0]:spindle_idx[evIdx,1]]
    x_epoch = t[spindle_idx[evIdx,0]+cut_epoch[0]:spindle_idx[evIdx,0]+cut_epoch[1]]
    y_epoch = data[spindle_idx[evIdx,0]+cut_epoch[0]:spindle_idx[evIdx,0]+cut_epoch[1]]
    
    # Plotting
    plt.subplot(2,1,counter); 
    plot( x_epoch, y_epoch, 'k' )
    plot( x, y, 'r' )
    plt.ylim((-400,400))
    counter+=1
    plot( [0,0]+t[spindle_idx[evIdx,0]],plt.ylim(), color=(1,0,0), linestyle = '--' )
    plot( [0,0]+t[spindle_idx[evIdx,1]],plt.ylim(), color=(1,0,0), linestyle = '--' )
    plt.text(t[spindle_idx[evIdx,0]], 300, 'Onset > ', horizontalalignment = 'right', color=(1,0,0))
    plt.text(t[spindle_idx[evIdx,1]], 300, ' < Offset', horizontalalignment = 'left', color=(1,0,0))

    xlabel('Time (sec)')
    ylabel('Voltage (uV)')
    title('Spindles around event #%03d'%evIdx)
    plt.gca().set_facecolor((1,1,1))

plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig( '%sFig_QQ_TutorialSpindle.jpg'%(figure_directory), dpi=figure_dpi )
plt.show();
    

png

3-3) Time-frequency transformation (Spectrogram)

In many cases, EEG signals are transformed into time-frequency representation. In our demonstration, we'll calculate delta-band (1-4 Hz) and theta-band (4-13 Hz) powers to compare NREM vs. REM sleep EEGs. Function get_spectrogram() which takes multi-channel time domain EEG data (channel * time) to produce time-frequency domain EEG data (time * frequency * channel) is provided below.

# Spectrogram function
def get_spectrogram( data, t=EEG_hdeeg.times, fs=500, 
                    fft_win_size=2**11, t_resolution=2, freq_cut = None, verbose = False): 
  if verbose:print('Calc Spec.. shape: %s, winsize: [%04d], t_res: [%d] s'%(data.shape,fft_win_size,t_resolution))

  # Spectrogram time vector
  t_fft = [t[0]+(((fft_win_size*.5)+1)/fs),
           t[-1]-(((fft_win_size*.5)+1)/fs)];
  t_vec = np.linspace( t_fft[0], t_fft[-1], int(np.diff(t_fft)/t_resolution)+1);
  
  # Memory pre-occupation
  n_ch, n_t = data.shape[0], len(t_vec)
  _,f = fft_half( np.zeros(fft_win_size), fs);
  if freq_cut is None:
    n_f = len(f)
    Spec_f = f;
  else:
    n_f = np.where(f<freq_cut)[0][-1]+1;
    Spec_f = f[0:n_f];
  Spec = np.zeros((n_t, n_f, n_ch), dtype='float16');

  # Get sliding window indicies
  idx = np.zeros((len(t_vec),2), dtype='int')
  for tIdx in range(len(t_vec)):
    idx_center=np.where(t<t_vec[tIdx])[0][-1]
    idx[tIdx,0] = int(idx_center-fft_win_size*.5)
    idx[tIdx,1] = int(idx_center+fft_win_size*.5-1)
  short = np.where(~(np.diff(idx,1)==fft_win_size-1 ))
  idx[short,1] = idx[short,1]+1
  Spec_t = t[np.array(np.mean(idx,1), dtype='int')];
    
  # FFT calculation
  hann = np.hanning( fft_win_size-1 );
  for chanIdx in range(n_ch):
    epoch = data[chanIdx, :];
    for tIdx in range(len(t_vec)):
      d =  hann * epoch[idx[tIdx,0]:idx[tIdx,1]]
      [x,f]= fft_half(d, fs);
      if freq_cut is None:
        if len(x) < n_f:
            Spec[tIdx,:-1,chanIdx] = abs(x);
        else:
            Spec[tIdx,:,chanIdx] = abs(x);
      else:
        Spec[tIdx,:,chanIdx] = abs(x[:n_f]);
  if verbose: print('Done: spectrogram calculation')
  return np.squeeze(Spec), Spec_t, Spec_f

# Calculate spectrogram
Spec, Spec_t, Spec_f = get_spectrogram( data = EEG_hdeeg.data, t = EEG_hdeeg.times, verbose = True)

# Sample channel visualization
from matplotlib.pyplot import cm
for chanIdx in range(1):
    plt.figure(figsize=(12,4))
    plt.contourf( Spec_t/60, Spec_f, Spec[:,:,chanIdx].transpose(),
               cmap=cm.jet, levels = np.linspace(0,20,30))
    plt.xlim((0,120))
    plt.ylim((1,30))
    plt.xlabel('Time (min)')
    plt.ylabel('Freq (Hz)')
    cbar = plt.colorbar(ticks=(0,5,10,15,20))
    cbar.set_label('Amplitude (uV/Hz)')
    plt.title('Sample time-frequency representation of EEG data')
    plt.savefig( '%sFig_QQ_TutorialSpectrogram.jpg'%(figure_directory), dpi=figure_dpi )

Calc Spec.. shape: (36, 3600000), winsize: [2048], t_res: [2] s
Done: spectrogram calculation

png

3-4) Topographical reconstruction of HD-EEG

Using channel coordinates, spectral/temporal features of EEG data can be visualized spatially. Scripts below provides a demonstration of topographical reconstruction of (1) delta power during NREM sleep, and (2) theta power during REM sleep as function of experimental condition (e.g., sleep deprivation). To select related timepoints (i.e., NREM and REM periods) from spectrogram, sleep stage label data should be prepared. As our sleep stage label data and the spectrogram calculated above have different sampling rate, it should be matched first.

# Scaling sleep state
sleep_state = np.zeros( (EEG_screw.data.shape[1],1), dtype='float16' )
state_label = ['WAKE', 'NREM', 'REM']
srate_factor_upscale = int(EEG_screw.data.shape[1] / sleep_score.shape[0])
for stateIdx in range(len(state_label)):
    for tIdx in np.where( sleep_score['type'] == state_label[stateIdx] )[0]:
        sleep_state[tIdx*srate_factor_upscale:(tIdx+1)*srate_factor_upscale] = stateIdx+1

# Slicing HDEEG-existing part & resizing
t_ZT1_ZT3 = [int(19*EEG_screw.info['sfreq']*(60**2)), int(21*EEG_screw.info['sfreq']*(60**2))]
sleep_state_hdeeg = sleep_state[t_ZT1_ZT3[0]:t_ZT1_ZT3[1]]
sleep_state_spec = np.round((np.array( resize(sleep_state_hdeeg, (Spec_t.shape[0],1), interp = 'nearest' ), dtype='int32')/256)*2)

# Visualization
plt.figure(figsize=(10,3))
plt.subplot(1,4,(1,3))
plt.plot( Spec_t/60, sleep_state_spec )
plt.yticks((0,1,2), labels=state_label)
xlabel('Time (min)');

# Count proportion of each state
plt.subplot(1,4,(4))
for stateIdx in [0,1,2]:
    plt.bar(stateIdx, 100*np.where(sleep_state_spec==stateIdx)[0].shape[0]/sleep_state_spec.shape[0], color='k')
plt.xticks((0,1,2), labels=state_label)
ylabel('Proportion (%)')

plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig( '%sFig_QQ_TutorialLabeling.jpg'%(figure_directory), dpi=figure_dpi )

png

Next, we need to extract band power information of each channel to draw topographical map. To easily extract specific band power with given state, we made a handy function bandpower_state() as below. It slices in temporal and frequency domain for corresponding point of sleep state and frequency bin, respectively.

def bandpower_state( data=(Spec,Spec_t,Spec_f),sleep_state=sleep_state_spec,
                    band_name='delta',state_name='NREM'):
    band_dict = {'delta':[1,4], 'theta':[5,10], 'high_gamma':[100,250], 'overall':[1,250]}
    state_dict = {'WAKE':0, 'NREM':1, 'REM':2}
    band, state = band_dict[band_name], state_dict[state_name]
    bandIdx = (np.where(Spec_f<band[0])[0][-1]+1, np.where(Spec_f<band[1])[0][-1])
    stateIdx = np.where( sleep_state == state )[0]
    seg = find_seg(stateIdx)
    data = np.zeros((0,36))
    for segIdx in range(seg.shape[0]):
        tIdx = (np.where( Spec_t<Spec_t[seg[segIdx,0]] )[0][-1]+1, 
                np.where( Spec_t<Spec_t[seg[segIdx,1]] )[0][-1])
        data = np.concatenate((data, np.mean( Spec[tIdx[0]:tIdx[1],bandIdx[0]:bandIdx[1],:], axis=1 )), 0)
    return np.squeeze(np.mean(data,0))

# Example Callback of function & bad channel rejection
data_topo = bandpower_state((Spec,Spec_t,Spec_f), sleep_state_spec, 'delta', 'NREM')
data_topo[EEG_hdeeg.bad_channels] = np.nan
data_topo[EEG_hdeeg.bad_channels] = np.nanmedian(data_topo)

Finally, we need 2D interpolation function to draw power topography from given power distribution sampled at electrode points.

# Preparation of 2D power topography
""" (1) Class for 2D interpolation """
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate

class bi_interp2:
  def __init__(self, x, y, z, xb, yb, xi, yi, method='linear'):
    self.x = x
    self.y = y
    self.z = z
    self.xb = xb
    self.yb = yb
    self.xi = xi
    self.yi = yi
    self.x_new, self.y_new = np.meshgrid(xi, yi)
    self.id_out = np.zeros([len(self.xi), len(self.xi)], dtype='bool')
    self.x_up, self.y_up, self.x_dn, self.y_dn = [], [], [], []
    self.interp_method = method
    self.z_new = []

  def __call__(self):
    self.find_boundary()
    self.interp2d()
    return self.x_new, self.y_new, self.z_new

  def find_boundary(self):
    self.divide_plane()
    # sort x value
    idup = self.sort_arr(self.x_up)
    iddn = self.sort_arr(self.x_dn)
    self.x_up = self.x_up[idup]
    self.y_up = self.y_up[idup]
    self.x_dn = self.x_dn[iddn]
    self.y_dn = self.y_dn[iddn]
    self.remove_overlap()
    # find outline, use monotone cubic interpolation
    ybnew_up = self.interp1d(self.x_up, self.y_up, self.xi)
    ybnew_dn = self.interp1d(self.x_dn, self.y_dn, self.xi)
    for i in range(len(self.xi)):
        idt1 = self.y_new[:, i] > ybnew_up[i]
        idt2 = self.y_new[:, i] < ybnew_dn[i]
        self.id_out[idt1, i] = True
        self.id_out[idt2, i] = True
    # expand data points
    self.x = np.concatenate((self.x, self.x_new[self.id_out].flatten(), self.xb))
    self.y = np.concatenate((self.y, self.y_new[self.id_out].flatten(), self.yb))
    self.z = np.concatenate((self.z, np.zeros(np.sum(self.id_out) + len(self.xb))))

  def interp2d(self):
    pts = np.concatenate((self.x.reshape([-1, 1]), self.y.reshape([-1, 1])), axis=1)
    self.z_new = interpolate.griddata(pts, self.z, (self.x_new, self.y_new), method=self.interp_method)
    self.z_new[self.id_out] = np.nan
    
  def remove_overlap(self):
    id1 = self.find_val(np.diff(self.x_up) == 0, None)
    id2 = self.find_val(np.diff(self.x_dn) == 0, None)
    for i in id1:
      temp = (self.y_up[i] + self.y_up[i+1]) / 2
      self.y_up[i+1] = temp
      self.x_up = np.delete(self.x_up, i)
      self.y_up = np.delete(self.y_up, i)
    for i in id2:
      temp = (self.y_dn[i] + self.y_dn[i + 1]) / 2
      self.y_dn[i+1] = temp
      self.x_dn = np.delete(self.x_dn, i)
      self.y_dn = np.delete(self.y_dn, i)

  def divide_plane(self):
    ix1 = self.find_val(self.xb == min(self.xb), 1)
    ix2 = self.find_val(self.xb == max(self.xb), 1)
    iy1 = self.find_val(self.yb == min(self.yb), 1)
    iy2 = self.find_val(self.yb == max(self.yb), 1)
    # divide the plane with Quadrant
    qd = np.zeros([self.xb.shape[0], 4], dtype='bool')
    qd[:, 0] = (self.xb > self.xb[iy2]) & (self.yb > self.yb[ix2])
    qd[:, 1] = (self.xb > self.xb[iy1]) & (self.yb < self.yb[ix2])
    qd[:, 2] = (self.xb < self.xb[iy1]) & (self.yb < self.yb[ix1])
    qd[:, 3] = (self.xb < self.yb[iy2]) & (self.yb > self.yb[ix1])
    # divide the array with y axis
    self.x_up = self.xb[qd[:, 0] | qd[:, 3]]
    self.y_up = self.yb[qd[:, 0] | qd[:, 3]]
    self.x_dn = self.xb[qd[:, 1] | qd[:, 2]]
    self.y_dn = self.yb[qd[:, 1] | qd[:, 2]]

  def find_val(self, condition, num_of_returns):
    # find the value that satisfy the condition
    ind = np.where(condition == 1)
    return ind[:num_of_returns]

  def sort_arr(self, arr):
    # return sorting index
    return sorted(range(len(arr)), key=lambda i: arr[i])

  def interp1d(self, xx, yy, xxi):
    # find the boundary line
    interp_obj = interpolate.PchipInterpolator(xx, yy)
    return interp_obj(xxi)
  
""" (2) Function for Topography plot """
from matplotlib import cm
from pandas import read_csv
montage_file = bids_path+'montage.csv'
from mpl_toolkits.mplot3d import Axes3D

def get_boundary():
 return np.array([
    -4.400, 0.030, -4.180, 0.609, -3.960, 1.148, -3.740, 1.646, -3.520, 2.105, -3.300, 2.525, -3.080, 2.908, -2.860, 3.255,
    -2.640, 3.566, -2.420, 3.843, -2.200, 4.086, -1.980, 4.298, -1.760, 4.4799, -1.540, 4.6321, -1.320, 4.7567, -1.100, 4.8553,
    -0.880, 4.9298, -0.660, 4.9822, -0.440, 5.0150, -0.220, 5.0312,0, 5.035, 0.220, 5.0312, 0.440, 5.0150, 0.660, 4.9822,
    0.880, 4.9298, 1.100, 4.8553, 1.320, 4.7567, 1.540, 4.6321,1.760, 4.4799, 1.980, 4.2986, 2.200, 4.0867, 2.420, 3.8430,
    2.640, 3.5662, 2.860, 3.2551, 3.080, 2.9087, 3.300, 2.5258,3.520, 2.1054, 3.740, 1.6466, 3.960, 1.1484, 4.180, 0.6099,
    4.400, 0.0302, 4.400, 0.0302, 4.467, -0.1597, 4.5268, -0.3497,4.5799, -0.5397, 4.6266, -0.7297, 4.6673, -0.9197, 4.7025, -1.1097,
    4.7326, -1.2997, 4.7579, -1.4897, 4.7789, -1.6797, 4.7960, -1.8697,4.8095, -2.0597, 4.8199, -2.2497, 4.8277, -2.4397, 4.8331, -2.6297,
    4.8366, -2.8197, 4.8387, -3.0097, 4.8396, -3.1997, 4.8399, -3.3897,4.8384, -3.5797, 4.8177, -3.7697, 4.7776, -3.9597, 4.7237, -4.1497,
    4.6620, -4.3397, 4.5958, -4.5297, 4.5021, -4.7197, 4.400, -4.8937,4.1800, -5.1191, 3.9600, -5.3285, 3.7400, -5.5223, 3.5200, -5.7007,
    3.3000, -5.8642, 3.0800, -6.0131, 2.8600, -6.1478, 2.6400, -6.2688,2.4200, -6.3764, 2.2000, -6.4712, 1.9800, -6.5536, 1.7600, -6.6241,
    1.5400, -6.6833, 1.3200, -6.7317, 1.1000, -6.7701, 0.8800, -6.7991,0.6600, -6.8194, 0.4400, -6.8322, 0.2200, -6.8385, 0, -6.840,
    -0.220, -6.8385, -0.440, -6.8322, -0.660, -6.8194, -0.880, -6.7991,-1.100, -6.7701, -1.320, -6.7317, -1.540, -6.6833, -1.760, -6.6241,
    -1.980, -6.5536, -2.200, -6.4712, -2.420, -6.3764, -2.640, -6.2688,-2.860, -6.1478, -3.080, -6.0131, -3.300, -5.8642, -3.520, -5.7007,
    -3.740, -5.5223, -3.960, -5.3285, -4.180, -5.1191, -4.400, -4.89370,-4.5021, -4.7197, -4.5958, -4.5297, -4.6620, -4.3397, -4.7237, -4.1497,
    -4.7776, -3.9597, -4.8177, -3.7697, -4.8384, -3.5797, -4.8399, -3.3897,-4.8397, -3.1997, -4.8387, -3.0097, -4.8367, -2.8197, -4.8331, -2.6297,
    -4.8277, -2.4397, -4.8200, -2.2497, -4.8095, -2.0597, -4.7960, -1.8697,-4.7789, -1.6797, -4.7579, -1.4897, -4.7326, -1.2997, -4.7025, -1.1097,
    -4.6673, -0.9197, -4.6266, -0.7297, -4.5799, -0.5397, -4.5268, -0.3497,-4.4670, -0.1597, -4.4000, 0.03025]).reshape(-1, 2)

def plot_topo2d(data, plot_opt = True, clim=(-5,5), montage_file=montage_file, topo_resolution = (500,500)):
  # Zero-padding
  short = 38-len(data)
  if short: data=np.concatenate((data, np.tile(.00000001, short)), axis=0)
  # Get head boundary image coordinates
  boundary = get_boundary()
  montage_table = read_csv(montage_file)
  x, y = np.array(montage_table['X_ML']), np.array(montage_table['Y_AP'])
  xb, yb = boundary[:, 0], boundary[:, 1]
  xi, yi = np.linspace(min(xb), max(xb), topo_resolution[0]),np.linspace(min(yb), max(yb), topo_resolution[1])
  xx, yy, topo_data = bi_interp2(x, y, data, xb, yb, xi, yi)()
  if plot_opt:
    topo_to_draw = topo_data.copy()
    topo_to_draw[np.where(topo_data>clim[1])] = clim[1]
    topo_to_draw[np.where(topo_data<clim[0])] = clim[0]
    plt.contourf(xx, yy, topo_to_draw, cmap=cm.jet, levels = np.linspace(clim[0],clim[1],50))
    plt.grid(False)
    plt.gca().set_aspect('equal','box')
    plt.xlabel('ML coordinate (mm)', fontsize=15);
    plt.ylabel('AP coordinate (mm)', fontsize=15);
    plt.text(0, 0.0, 'BP', color='w', fontsize=10, weight='bold', ha='center',va='center');
    plt.text(0,-4.2, 'LP', color='w', fontsize=10, weight='bold', ha='center',va='center');
    if clim is not None: plt.clim(clim)
    plt.plot(montage_table['X_ML'][0:36],montage_table['Y_AP'][0:36], 'w.')    
    plt.axis( (-5.5, 5.5, -7, 5.2) ) 
    plt.gca().set_facecolor((1,1,1))

  return xx, yy, topo_data

Using above function of plot_topo2d(), power topography of delta-band activity across different sleep state (WAKE, NREM, and REM) can be drawn as follow.

plt.figure(figsize=(10,6))
counter=0
for state in ['WAKE', 'NREM', 'REM']:
    # Get bandpower from each channel
    data_topo = bandpower_state((Spec,Spec_t,Spec_f), sleep_state_spec, 'delta', state)
    data_topo[EEG_hdeeg.bad_channels] = np.nan
    data_topo[EEG_hdeeg.bad_channels] = np.nanmean(data_topo)

    # Transform into 2D topography
    xx,yy,topo_NREM_delta = plot_topo2d( data_topo, plot_opt = False )
    clim=(0,6.5)

    counter += 1
    plt.subplot(1,3,counter)
    plt.contourf(xx, yy, topo_NREM_delta, cmap=cm.jet, levels = np.linspace(clim[0],clim[1],30))
    plt.grid(False)
    plt.gca().set_aspect('equal','box')
    plt.xlabel('ML coordinate (mm)', fontsize=15, color='k');
    if state is 'WAKE': plt.ylabel('AP coordinate (mm)', fontsize=15, color='k');
    plt.text(0, 0.0, 'BP', color='k', fontsize=10, weight='bold', ha='center',va='center');
    plt.text(0,-4.2, 'LP', color='k', fontsize=10, weight='bold', ha='center',va='center');
    plt.axis( (-5.5, 5.5, -7, 5.2) ) 
    plt.gca().set_facecolor((1,1,1))
    plt.xticks(color='k'); plt.yticks(color='k')
    plt.clim(clim)
    title( 'Delta power [%s]'%(state))

plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig( '%sFig_QQ_TutorialTopography.jpg'%(figure_directory), dpi=figure_dpi )

png

3-5) Example trace figures

Below figures depict some example cases of EEG analysis (e.g., drawing multichannel raw data, rich visualization of SWA and spindle) contained in this dataset. For the purpose of demonstration, few pieces of data were arbitrarily selected.

# Figure: Example signal visualization of screw/hdeeg data

# Data selection for figure QQQ
EEG_screw, EEG_hdeeg, sleep_score = load_data(example_info_trace['subject'],example_info_trace['day'], verbose=True)
start_point = int(3235*EEG_screw.info['sfreq'])
data_length = int(60*EEG_screw.info['sfreq'])
toi = (start_point+0,start_point+data_length-1)
t_toi = EEG_hdeeg.times[toi[0]:toi[1]]
hdeeg_toi = EEG_hdeeg.data[:,toi[0]:toi[1]]
hdeeg_toi[ EEG_hdeeg.bad_channels, :] = np.nan
screw_toi = EEG_screw.data[:2,t_ZT1_ZT3[0]+toi[0]:t_ZT1_ZT3[0]+toi[1]]
acc_toi = abs(EEG_screw.data[2:3,t_ZT1_ZT3[0]+toi[0]:t_ZT1_ZT3[0]+toi[1]])
data_toi = np.concatenate((10*acc_toi, 2*screw_toi, hdeeg_toi), axis=0 )

# Filter EEG data (1-50 Hz)
for chIdx in range(1,data_toi.shape[0]): data_toi[chIdx,:] = bandpass_filt(data_toi[chIdx,:], band=(1,50))

# Rescaling sleep state data
sleep_state = np.zeros( (EEG_screw.data.shape[1],1), dtype='float16' )
state_label = ['WAKE', 'NREM', 'REM']
score_exist = sleep_score.shape[0]>0
if score_exist:
    srate_factor_upscale = int(EEG_screw.data.shape[1] / sleep_score.shape[0])
    for stateIdx in range(len(state_label)):
        for tIdx in np.where( sleep_score['type'] == state_label[stateIdx] )[0]:
            sleep_state[tIdx*srate_factor_upscale:(tIdx+1)*srate_factor_upscale] = stateIdx+1
    t_ZT1_ZT3 = [int(19*EEG_screw.info['sfreq']*(60**2)), int(21*EEG_screw.info['sfreq']*(60**2))]
    sleep_state_hdeeg = sleep_state[t_ZT1_ZT3[0]:t_ZT1_ZT3[1]]
    sleep_state_toi_500Hz = np.round(sleep_state_hdeeg)
WAKE = sleep_state_toi_500Hz==1
wake_toi = np.where( WAKE[toi[0]:toi[1]] == 1 )[0]
sleep_toi = np.where( WAKE[toi[0]:toi[1]] == 0 )[0]

# Channel label list for visualization
ch_names = ['ACC', 'Frontal', 'Parietal']; [ ch_names.append(x) for x in EEG_hdeeg.ch_names ]

# Plot multi channels 
x, y = t_toi, data_toi
spacing=8000 
color_template = np.array([[1,.09,.15],[1,.75,.28],[.4,.2,0],[.6,.7,.3],[.55,.55,.08]])*0
color_space = np.tile( color_template, 
                  (int(np.ceil([ float(y.shape[0])/color_template.shape[0]])[0]), 1) )
color_space = np.zeros((y.shape[0],3))
plt.figure(figsize=(15,10))
y_center = np.linspace( -spacing, spacing, int(y.shape[0]) )
for chanIdx in range(y.shape[0]):
    shift = y_center[chanIdx] + np.nanmean(y[chanIdx,:])
    plt.plot(x, y[chanIdx,:]-shift, color=color_space[chanIdx,], linewidth=1);
    plt.xlabel('Time (sec)', color=[0,0,0])
    plt.xticks(color=[0,0,0]); plt.yticks(color=[0,0,0])
    plt.yticks(y_center, ch_names[::-1]);
    plt.ylim((-1.1*spacing,1.25*spacing))

state_indicator_ypos = spacing*1.23
plot( x[wake_toi], np.ones((wake_toi.shape[0],1))*state_indicator_ypos, '.', color=[.1, 1, .1])
plot( x[sleep_toi], np.ones((sleep_toi.shape[0],1))*state_indicator_ypos,2 '.', color=[.5,.5,.5])
plt.gca().set_facecolor((1,1,1))
plt.savefig( '%sFig_QQ_ExampleTrace.jpg'%(figure_directory), dpi=figure_dpi )
plt.show();


** Data loading ... Subject 2, Session 7 (Step: R1) **
Screw data shape: [4 channels x 43200000 times]
Screw bad channels: []
hdEEG data shape: [36 channels x 3600000 times]
hdEEG bad channels: [0, 29, 34]

png

# Figure: Example SWA propagation (anterior to posterior)

# Data selection for figure QQQ
evIdx = example_info_swa['event']
EEG_screw, EEG_hdeeg, sleep_score = load_data(example_info_swa['subject'],example_info_swa['day'], verbose=True)
data, t, fs = EEG_hdeeg.data[3,:], EEG_hdeeg.times, EEG_screw.info['sfreq']
swa_idx, swa_minmax = detect_swa(data, fs=fs, t=t, verbose=True);
data_filtered = EEG_hdeeg.data.copy()
for chIdx in range(data_filtered.shape[0]): data_filtered[chIdx,:] = bandpass_filt(data_filtered[chIdx,:], band=(1,30))            

# Preprocessing for easy visualization
cut_epoch = (-int(fs*.2),int(fs*.4)) # unit:sec

x_epoch = t[swa_idx[evIdx,0]+cut_epoch[0]:swa_idx[evIdx,0]+cut_epoch[1]]
y_epoch = data_filtered[:,swa_idx[evIdx,0]+cut_epoch[0]:swa_idx[evIdx,0]+cut_epoch[1]]
y_epoch[EEG_hdeeg.bad_channels,:] = np.nan

# Plot multi channels 
spacing=3000
color_space = np.zeros((y_epoch.shape[0],3))
plt.figure(figsize=(3,10))
y_center = np.linspace( -spacing, spacing, int(y_epoch.shape[0]) )
for chanIdx in range(y_epoch.shape[0]):
    shift = y_center[chanIdx] + np.nanmean(y_epoch[chanIdx,:])
    plt.plot(x_epoch-t[swa_idx[evIdx,0]], y_epoch[chanIdx,:]-shift, color=color_space[chanIdx,], linewidth=1);
    plt.xlabel('Time (sec)', color=[0,0,0])
    plt.xticks(color=[0,0,0]); plt.yticks(color=[0,0,0])
    plt.yticks(y_center, EEG_hdeeg.ch_names[::-1]);
    plt.ylim((-1.05*spacing,1.20*spacing))
plot( [0,0],plt.ylim(), color=(1,0,0), linestyle = '--' )
plot( [0,0]+t[swa_idx[evIdx,1]]-t[swa_idx[evIdx,0]],plt.ylim(), color=(1,0,0), linestyle = '--' )
plt.text(0, 1.12*spacing, 'Onset > ', horizontalalignment = 'right', color=(1,0,0))
plt.text(t[swa_idx[evIdx,1]]-t[swa_idx[evIdx,0]], 1.12*spacing, ' < Offset', horizontalalignment = 'left', color=(1,0,0))
plt.xlim((-.2,.4))
plt.gca().set_facecolor((1,1,1))

plt.title('Example SWA Activity')
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.savefig( '%sFig_QQ_ExampleSWA.jpg'%(figure_directory), dpi=figure_dpi )
plt.show();

** Data loading ... Subject 1, Session 1 (Step: BL) **
Screw data shape: [4 channels x 43200000 times]
Screw bad channels: []
hdEEG data shape: [36 channels x 3600000 times]
hdEEG bad channels: [9, 12, 22, 27, 31, 34, 35]
[SWA detection] 7796 events are detected

png

# Data selection for example figure
evIdx = example_info_spindle['event']
EEG_screw, EEG_hdeeg, sleep_score = load_data(example_info_spindle['subject'],example_info_spindle['day'], verbose=True)
data, t, fs = EEG_hdeeg.data[3,:], EEG_hdeeg.times, EEG_screw.info['sfreq']
spindle_idx, amptd_envelop = detect_spindle( data, fs )
data_filtered = EEG_hdeeg.data.copy()
for chIdx in range(data_filtered.shape[0]): data_filtered[chIdx,:] = bandpass_filt(data_filtered[chIdx,:], band=(1,50))            

# Preprocessing for easy visualization
cut_epoch = (-int(fs*2),int(fs*2)) # unit:sec

x_epoch = t[spindle_idx[evIdx,0]+cut_epoch[0]:spindle_idx[evIdx,0]+cut_epoch[1]]
y_epoch = data_filtered[:,spindle_idx[evIdx,0]+cut_epoch[0]:spindle_idx[evIdx,0]+cut_epoch[1]]
y_epoch[EEG_hdeeg.bad_channels,:] = np.nan

plt.figure(figsize=(10,8))
spacing=5000
y_center = np.linspace( -spacing, spacing, int(y_epoch.shape[0]) )
for chanIdx in range(y_epoch.shape[0]):
    shift = y_center[chanIdx] + np.nanmean(y_epoch[chanIdx,:])
    plt.plot(x_epoch-t[spindle_idx[evIdx,0]], y_epoch[chanIdx,:]-shift, color=color_space[chanIdx,], linewidth=1);
    plt.xlabel('Time (sec)', color=[0,0,0])
    plt.xticks(color=[0,0,0]); plt.yticks(color=[0,0,0])
    plt.yticks(y_center, EEG_hdeeg.ch_names[::-1]);
    plt.ylim((-1.05*spacing,1.20*spacing))
plot( [0,0],plt.ylim(), color=(1,0,0), linestyle = '--' )
plot( [0,0]+t[spindle_idx[evIdx,1]]-t[spindle_idx[evIdx,0]],plt.ylim(), color=(1,0,0), linestyle = '--' )
plt.text(0, 1.12*spacing, 'Onset > ', horizontalalignment = 'right', color=(1,0,0))
plt.text(t[spindle_idx[evIdx,1]]-t[spindle_idx[evIdx,0]], 1.12*spacing, ' < Offset', horizontalalignment = 'left', color=(1,0,0))
plt.title('Example Spindle Activity')
plt.xlim((-2,2))
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
plt.gca().set_facecolor((1,1,1))
plt.savefig( '%sFig_QQ_ExampleSpindle.jpg'%(figure_directory), dpi=figure_dpi )
plt.show();

png

3-6) Obtaining grand-averaged results

So far, figures and analyses were focused on the data from single session. To obtain grand-averaged data, we need to use a loop which extract features-of-interest from given dataset. Because of the large size of dataset, the calculation is done one-by-one. This is just an example way of doing it, so please change the pipeline to meet your analysis purpose and environment (large RAM, etc.).

# IT TAKES A LOT OF TIME!
calculationOpt = False
if calculationOpt:
    for sbjIdx in range(9):
        list_swa, list_swa_range, list_spindle, list_delta, list_theta = [], [], [], [], []
        list_Spec, list_sleep_state_toi = [], []
        list_highpow = []
        for sessIdx in range(9):

            # (1) Data loading and check availability
            EEG_screw, EEG_hdeeg, sleep_score = load_data( sbjIdx, sessIdx, verbose = False)
            score_exist = sleep_score.shape[0]>0
            hdeeg_exist = EEG_hdeeg is not None

            # (2) SWA & Spindle detection
            spindles, swas, swar = [], [], []
            if hdeeg_exist:
                for chIdx in range(EEG_hdeeg.data.shape[0]):
                    if chIdx in EEG_hdeeg.bad_channels:
                        swa_idx, swa_minmax, spindle_idx = [], [], []
                    else:
                        data, t, fs = EEG_hdeeg.data[chIdx,:], EEG_hdeeg.times, EEG_screw.info['sfreq']
                        swa_idx, swa_minmax = detect_swa(data, fs=fs, t=t);
                        spindle_idx, amptd_envelop = detect_spindle( data, fs )
                    swas.append( swa_idx )
                    swar.append( swa_minmax )
                    spindles.append( spindle_idx )
                list_swa.append( swas )
                list_swa_range.append( swar )
                list_spindle.append( spindles )
            else:
                list_swa.append( [] )
                list_swa_range.append( [] )
                list_spindle.append( [] )

            # (3) Power calculation for topography & signal quality check
            if hdeeg_exist and score_exist:
                # Scaling sleep state
                Spec, Spec_t, Spec_f = get_spectrogram( data = EEG_hdeeg.data, t = EEG_hdeeg.times )

                sleep_state = np.zeros( (EEG_screw.data.shape[1],1), dtype='float16' )
                state_label = ['WAKE', 'NREM', 'REM']
                srate_factor_upscale = int(EEG_screw.data.shape[1] / sleep_score.shape[0])
                for stateIdx in range(len(state_label)):
                    for tIdx in np.where( sleep_score['type'] == state_label[stateIdx] )[0]:
                        sleep_state[tIdx*srate_factor_upscale:(tIdx+1)*srate_factor_upscale] = stateIdx+1
                t_ZT1_ZT3 = [int(19*EEG_screw.info['sfreq']*(60**2)), int(21*EEG_screw.info['sfreq']*(60**2))]
                sleep_state_hdeeg = sleep_state[t_ZT1_ZT3[0]:t_ZT1_ZT3[1]]
                sleep_state_toi = np.round((np.array( resize(sleep_state_hdeeg, (Spec_t.shape[0],1), interp = 'nearest' ), dtype='int32')/256)*2)        
                sleep_state_toi_500Hz = np.round(sleep_state_hdeeg)

                list_Spec.append(Spec)
                list_sleep_state_toi.append(sleep_state_toi)  

            else:
                list_Spec.append([])
                list_sleep_state_toi.append([]) 

        # Save
        data = {'list_swa':list_swa,
                'list_swa_range':list_swa_range,
                'list_spindle':list_spindle,
                'list_sleep_state_toi':list_sleep_state_toi,
                'list_highpow':list_highpow,
                'list_Spec':list_Spec,
                'Spec_t':Spec_t,
                'Spec_f':Spec_f,
                'bad_channels_hdeeg':EEG_hdeeg.bad_channels}
        with open(save_directory+'data_summary-sbj%s.pickle'%(sbjIdx+1), 'wb') as f: pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)

With the scripts above, we downsized the individual subject's data from 10.1 GB to 1.5 GB. Through scanning these pickle files, grand-averaged power topography can be obtained as follow.

# Get individual topography data
import pickle
save_directory = ''

topos = np.zeros((500,500,9,9,2,3), dtype='float16')
band_label = ['delta', 'theta']
state_label = ['WAKE', 'NREM', 'REM']
condition_label = ['BL', 'SR1', 'SR2', 'SR3', 'SR4', 'SR5' ,'R1', 'R2', 'R3']    

visualizeOpt = False
for sbjIdx in range(9):
    fname_open =save_directory+'data_summary-sbj%s.pickle'%(sbjIdx+1)
    print('\n---- opening [%s]'%fname_open)
    with open(fname_open, 'rb') as f: data = pickle.load(f)
    
    if sbjIdx == 0: Spec_f, Spec_t = data['Spec_f'], data['Spec_t']
    
    # Data selection
    badch = data['bad_channels_hdeeg']
    powers = np.zeros((36,9,len(band_label),len(state_label)))
    for dayIdx in range(9):
        Spec = data['list_Spec'][dayIdx]
        sleep_state_toi = data['list_sleep_state_toi'][dayIdx]
        if len(Spec)>0:
            if Spec.shape[0] < sleep_state_toi.shape[0]:
                t = Spec_t[:Spec.shape[0]]
                sleep_state_toi = resize( sleep_state_toi, [Spec.shape[0],1] )
            else:
                t = Spec_t
            
            for band in band_label:
                for state in state_label:
                    x = bandpower_state((Spec,t,Spec_f),sleep_state_toi,band,state)
                    x[badch] = np.nan; x[badch] = np.nanmedian(x)
                    powers[:,dayIdx,band_label.index(band),state_label.index(state)] = x
        else:
            powers[:,dayIdx,:,:] = np.nan;

    for bandIdx in [0,1]:
        if bandIdx==0: clim=(0,12)#np.nanmax( powers[:,:,bandIdx,stateIdx].flatten() ))
        else:          clim=(0,6)#np.nanmax( powers[:,:,bandIdx,stateIdx].flatten() ))
        for stateIdx in [0,1,2]:
            if visualizeOpt: 
                plt.figure(figsize=(15,8))
                counter = 0
            for dayIdx in range(9):
                powers_to_draw = powers[:,dayIdx,bandIdx,stateIdx]
                
                outlier_index = np.where( powers_to_draw > (np.nanmean(powers_to_draw)+np.nanstd(powers_to_draw)*2.56))[0]
                powers_to_draw[ outlier_index ] = np.nan;
                powers_to_draw[ outlier_index ] = np.nanmedian(powers_to_draw);
                
                hdeeg_exist = np.mean( np.isnan(powers_to_draw) ) < 1
                xx,yy,topo = plot_topo2d( powers_to_draw, plot_opt = False )
                if not hdeeg_exist: topo[:] = np.nan
                topos[:,:,dayIdx,sbjIdx,bandIdx,stateIdx] = topo
                if visualizeOpt: 
                    plt.contourf(xx, yy, topo, cmap=cm.jet, levels = np.linspace(clim[0],clim[1],30))
                    plt.subplot(1,9,dayIdx+1)
                    plt.grid(False)
                    plt.gca().set_aspect('equal','box')
                    plt.axis( (-5.5, 5.5, -7, 5.2) ) 
                    plt.gca().set_facecolor((1,1,1))
                    plt.xticks([0],color='k'); plt.yticks([0],color='k')
                    plt.clim(clim)
                    title( '[%s]%s %s'%(condition_label[dayIdx],state_label[stateIdx],band_label[bandIdx]), fontsize=9)
            if visualizeOpt: plt.show()
#np.save(save_directory+'topos', topos) # Save it if you want

---- opening [D:/output_ipynb/data_summary-sbj1.pickle]

---- opening [D:/output_ipynb/data_summary-sbj2.pickle]

---- opening [D:/output_ipynb/data_summary-sbj3.pickle]

---- opening [D:/output_ipynb/data_summary-sbj4.pickle]

---- opening [D:/output_ipynb/data_summary-sbj5.pickle]

---- opening [D:/output_ipynb/data_summary-sbj6.pickle]

---- opening [D:/output_ipynb/data_summary-sbj7.pickle]

---- opening [D:/output_ipynb/data_summary-sbj8.pickle]

---- opening [D:/output_ipynb/data_summary-sbj9.pickle]
# Topography visualization loop: with [topos] matrix

# Outlier elimination
topos_to_draw = topos.copy()
topos_to_draw[:,:,1,8,0,0]= np.nan; # Bad data

print('NREM delta')
clim = (0,9)
plt.figure(figsize=(15,8))
counter = 1
for dayIdx in range(9):
    n_valid_sample = 9-np.sum( np.isnan( np.nanmean(np.nanmean( topos_to_draw[:,:,dayIdx,:,0,1], 1 ), 0)))
    if n_valid_sample > 3: # Ignoring small samples
        plt.subplot(1,6,counter); counter += 1
        hdeeg_exist = np.mean( np.isnan(powers_to_draw) ) < 1
        xx,yy,topo = plot_topo2d( powers_to_draw, plot_opt = False )
        topo = np.nanmean( topos_to_draw[:,:,dayIdx,:,0,1], axis=2) 
        plt.contourf(xx, yy, topo, cmap=cm.jet, levels = np.linspace(clim[0],clim[1],30))
        plt.grid(False)
        plt.gca().set_aspect('equal','box')
        plt.axis( (-5.5, 5.5, -7, 5.2) ) 
        plt.gca().set_facecolor((1,1,1))
        plt.xticks([999],color='k'); plt.yticks([999],color='k')
        plt.clim(clim)
        title( '%s'%(condition_label[dayIdx]), fontsize=12)
plt.savefig( '%sFig_QQ_topo_NREMdelta.jpg'%(figure_directory), dpi=figure_dpi )
plt.show()

print('REM theta')
plt.figure(figsize=(15,8))
counter = 1
clim = (0,3.5)
for dayIdx in range(9):
    n_valid_sample = 9-np.sum( np.isnan( np.nanmean(np.nanmean( topos_to_draw[:,:,dayIdx,:,1,2], 1 ), 0)))
    if n_valid_sample > 3: # Ignoring small samples
        plt.subplot(1,6,counter); counter += 1
        hdeeg_exist = np.mean( np.isnan(powers_to_draw) ) < 1
        xx,yy,topo = plot_topo2d( powers_to_draw, plot_opt = False )
        topo = np.nanmean( topos_to_draw[:,:,dayIdx,:,1,2], axis=2) 
        plt.contourf(xx, yy, topo, cmap=cm.jet, levels = np.linspace(clim[0],clim[1],30))
        plt.grid(False)
        plt.gca().set_aspect('equal','box')
        plt.axis( (-5.5, 5.5, -7, 5.2) ) 
        plt.gca().set_facecolor((1,1,1))
        plt.xticks([999],color='k'); plt.yticks([999],color='k')
        plt.clim(clim)
        title( '%s'%(condition_label[dayIdx]), fontsize=12)
plt.savefig( '%sFig_QQ_topo_REMtheta.jpg'%(figure_directory), dpi=figure_dpi )
plt.show()
        
NREM delta

png

REM theta

png

Now, do it on your own!

# Code block
   

datacite.yml
Title Dataset of high-density and screw EEG recordings under chronic sleep restriction in mice
Authors Han,Hio-Been;Program of Brain and Cognitive Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea; Center for Neuroscience, Korea Institute of Science and Technology (KIST), 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea;orcid.org/0000-0001-5669-4054
Kim,Bowon;Center for Neuroscience, Korea Institute of Science and Technology (KIST), 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea
Kim,Youngsoo;Department of Chemistry, University of Michigan, 930 N. University Ave., Ann Arbor, MI 48109-1055, United States
Choi,Jee Hyun;Center for Neuroscience, Korea Institute of Science and Technology (KIST), 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea; Division of Biomedical Science and Engineering, KIST School, University of Science and Technology, 5 Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea
Description A set of screw and high-density EEG data obtained from freely-moving mice (mus musculus). Details of experimental method are described in the original research article using the same dataset [Kim et al., 2017, PNAS].
License Creative Commons Attribution 4.0 International Public License (https://creativecommons.org/licenses/by/4.0/)
References Differential modulation of global and local neural oscillations in REM sleep by homeostatic sleep regulation [] (IsSupplementTo)
Differential modulation of NREM sleep regulation and EEG topography by chronic sleep restriction in mice [] (IsSupplementTo)
High-density EEG of auditory steady-state responses during stimulation of basal forebrain parvalbumin neurons [] (IsSupplementTo)
Funding National Research Council of Science and Technology of Korea for the project Development of Solution for Diagnosis, Treatment, and Care System of Dementia Grant, CRC-15-04-KIST
National Research Foundation (NRF) of Korea, 2017R1A2B3012659
Keywords Neuroscience
Electrophysiology
EEG
Mouse EEG
High-density EEG
Chronic sleep restriction
Brain oscillations
Sleep
Resource Type Dataset