Compute and visualize ERDS maps

This example calculates and displays ERDS maps of event-related EEG data. ERDS (sometimes also written as ERD/ERS) is short for event-related desynchronization (ERD) and event-related synchronization (ERS) [1]. Conceptually, ERD corresponds to a decrease in power in a specific frequency band relative to a baseline. Similarly, ERS corresponds to an increase in power. An ERDS map is a time/frequency representation of ERD/ERS over a range of frequencies [2]. ERDS maps are also known as ERSP (event-related spectral perturbation) [3].

We use a public EEG BCI data set containing two different motor imagery tasks available at PhysioNet. The two tasks are imagined hand and feet movement. Our goal is to generate ERDS maps for each of the two tasks.

First, we load the data and create epochs of 5s length. The data sets contain multiple channels, but we will only consider the three channels C3, Cz, and C4. We compute maps containing frequencies ranging from 2 to 35Hz. We map ERD to red color and ERS to blue color, which is the convention in many ERDS publications. Note that we do not perform any significance tests on the map values, but instead we display the whole time/frequency maps.

References

[1]G. Pfurtscheller, F. H. Lopes da Silva. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clinical Neurophysiology 110(11), 1842-1857, 1999.
[2]B. Graimann, J. E. Huggins, S. P. Levine, G. Pfurtscheller. Visualization of significant ERD/ERS patterns in multichannel EEG and ECoG data. Clinical Neurophysiology 113(1), 43-47, 2002.
[3]S. Makeig. Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. Electroencephalography and Clinical Neurophysiology 86(4), 283-293, 1993.
  • ../../_images/sphx_glr_plot_time_frequency_erds_001.png
  • ../../_images/sphx_glr_plot_time_frequency_erds_002.png

Out:

Extracting edf Parameters from /home/ubuntu/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R06.edf...
EDF file detected
Setting channel info structure...
Created Raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Ready.
Extracting edf Parameters from /home/ubuntu/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R10.edf...
EDF file detected
Setting channel info structure...
Created Raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Ready.
Extracting edf Parameters from /home/ubuntu/mne_data/MNE-eegbci-data/physiobank/database/eegmmidb/S001/S001R14.edf...
EDF file detected
Setting channel info structure...
Created Raw.info structure...
Reading 0 ... 19999  =      0.000 ...   124.994 secs...
Ready.
Removing orphaned offset at the beginning of the file.
89 events found
Events id: [1 2 3]
45 matching events found
0 projection items activated
Loading data for 45 events and 961 original time points ...
0 bad epochs dropped
Applying baseline correction (mode: percent)
Applying baseline correction (mode: percent)
Applying baseline correction (mode: percent)
Applying baseline correction (mode: percent)
Applying baseline correction (mode: percent)
Applying baseline correction (mode: percent)

# Authors: Clemens Brunner <clemens.brunner@gmail.com>
#
# License: BSD (3-clause)


import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
import mne
from mne.datasets import eegbci
from mne.io import concatenate_raws, read_raw_edf
from mne.time_frequency import tfr_multitaper


def center_cmap(cmap, vmin, vmax):
    """Center given colormap (ranging from vmin to vmax) at value 0.

    Note that eventually this could also be achieved by re-normalizing a given
    colormap by subclassing matplotlib.colors.Normalize as described here:
    https://matplotlib.org/users/colormapnorms.html#custom-normalization-two-linear-ranges
    """  # noqa: E501
    vzero = abs(vmin) / (vmax - vmin)
    index_old = np.linspace(0, 1, cmap.N)
    index_new = np.hstack([np.linspace(0, vzero, cmap.N // 2, endpoint=False),
                           np.linspace(vzero, 1, cmap.N // 2)])
    cdict = {"red": [], "green": [], "blue": [], "alpha": []}
    for old, new in zip(index_old, index_new):
        r, g, b, a = cmap(old)
        cdict["red"].append((new, r, r))
        cdict["green"].append((new, g, g))
        cdict["blue"].append((new, b, b))
        cdict["alpha"].append((new, a, a))
    return LinearSegmentedColormap("erds", cdict)


# load and preprocess data ####################################################
subject = 1  # use data from subject 1
runs = [6, 10, 14]  # use only hand and feet motor imagery runs

fnames = eegbci.load_data(subject, runs)
raws = [read_raw_edf(f, preload=True, stim_channel='auto') for f in fnames]
raw = concatenate_raws(raws)

raw.rename_channels(lambda x: x.strip('.'))  # remove dots from channel names

events = mne.find_events(raw, shortest_event=0, stim_channel='STI 014')

picks = mne.pick_channels(raw.info["ch_names"], ["C3", "Cz", "C4"])

# epoch data ##################################################################
tmin, tmax = -1, 4  # define epochs around events (in s)
event_ids = dict(hands=2, feet=3)  # map event IDs to tasks

epochs = mne.Epochs(raw, events, event_ids, tmin - 0.5, tmax + 0.5,
                    picks=picks, baseline=None, preload=True)

# compute ERDS maps ###########################################################
freqs = np.arange(2, 36, 1)  # frequencies from 2-35Hz
n_cycles = freqs  # use constant t/f resolution
vmin, vmax = -1, 1.5  # set min and max ERDS values in plot
cmap = center_cmap(plt.cm.RdBu, vmin, vmax)  # zero maps to white

for event in event_ids:
    power = tfr_multitaper(epochs[event], freqs=freqs, n_cycles=n_cycles,
                           use_fft=True, return_itc=False, decim=2)
    power.crop(tmin, tmax)

    fig, ax = plt.subplots(1, 4, figsize=(12, 4),
                           gridspec_kw={"width_ratios": [10, 10, 10, 1]})
    for i in range(3):
        power.plot([i], baseline=[-1, 0], mode="percent", vmin=vmin, vmax=vmax,
                   cmap=(cmap, False), axes=ax[i], colorbar=False, show=False)
        ax[i].set_title(epochs.ch_names[i], fontsize=10)
        ax[i].axvline(0, linewidth=1, color="black", linestyle=":")  # event
        if i > 0:
            ax[i].set_ylabel("")
            ax[i].set_yticklabels("")
    fig.colorbar(ax[0].collections[0], cax=ax[-1])
    fig.suptitle("ERDS ({})".format(event))
    fig.show()

Total running time of the script: ( 0 minutes 2.414 seconds)

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