This repo contains a source code analysis of motor-imagery EEG data in MNE-Python environment, and deep learning model training.

In particular, on can perform CNN model selection for decoding EEG motor-imagery patterns via PyTorch. This repo was created to help students to get started with EEG and Deep learning research.

Required packages:

• pip install mne==0.18.0
• conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
• pip install braindecode

http://moabb.neurotechx.com/docs/datasets.html

For more details refer to MOABB website, where you can download these datasets.

https://drive.google.com/drive/folders/1bOm8l7o0MGL6fW7-EoPPrM3Q-jVOdmSV?usp=sharing

Four different datasets are provided - BCI competition that is publicly available online

• 'BNCI2014001R.pickle',
• 'BNCI2014004R.pickle',
• 'Weibo2014R.pickle',
• 'PhysionetRR.pickle'

"""BNCI 2014-001 Motor Imagery dataset.

Dataset IIa from BCI Competition 4 [1]_.

Dataset Description

This data set consists of EEG data from 9 subjects. The cue-based BCI paradigm consisted of four different motor imagery tasks, namely the imag- ination of movement of the left hand (class 1), right hand (class 2), both feet (class 3), and tongue (class 4). Two sessions on different days were recorded for each subject. Each session is comprised of 6 runs separated by short breaks. One run consists of 48 trials (12 for each of the four possible classes), yielding a total of 288 trials per session.

The subjects were sitting in a comfortable armchair in front of a computer screen. At the beginning of a trial ( t = 0 s), a fixation cross appeared on the black screen. In addition, a short acoustic warning tone was presented. After two seconds ( t = 2 s), a cue in the form of an arrow pointing either to the left, right, down or up (corresponding to one of the four classes left hand, right hand, foot or tongue) appeared and stayed on the screen for 1.25 s. This prompted the subjects to perform the desired motor imagery task. No feedback was provided. The subjects were ask to carry out the motor imagery task until the fixation cross disappeared from the screen at t = 6 s.

Twenty-two Ag/AgCl electrodes (with inter-electrode distances of 3.5 cm) were used to record the EEG; the montage is shown in Figure 3 left. All signals were recorded monopolarly with the left mastoid serving as reference and the right mastoid as ground. The signals were sampled with. 250 Hz and bandpass-filtered between 0.5 Hz and 100 Hz. The sensitivity of the amplifier was set to 100 μV . An additional 50 Hz notch filter was enabled to suppress line noise

.. [1] Tangermann, M., Müller, K.R., Aertsen, A., Birbaumer, N., Braun, C., Brunner, C., Leeb, R., Mehring, C., Miller, K.J., Mueller-Putz, G. and Nolte, G., 2012. Review of the BCI competition IV. Frontiers in neuroscience, 6, p.55. """

"""BNCI 2014-004 Motor Imagery dataset.

Dataset B from BCI Competition 2008.

Dataset description

This data set consists of EEG data from 9 subjects of a study published in [1]_. The subjects were right-handed, had normal or corrected-to-normal vision and were paid for participating in the experiments. All volunteers were sitting in an armchair, watching a flat screen monitor placed approximately 1 m away at eye level. For each subject 5 sessions are provided, whereby the first two sessions contain training data without feedback (screening), and the last three sessions were recorded with feedback.

Three bipolar recordings (C3, Cz, and C4) were recorded with a sampling frequency of 250 Hz.They were bandpass- filtered between 0.5 Hz and 100 Hz, and a notch filter at 50 Hz was enabled. The placement of the three bipolar recordings (large or small distances, more anterior or posterior) were slightly different for each subject (for more details see [1]). The electrode position Fz served as EEG ground. In addition to the EEG channels, the electrooculogram (EOG) was recorded with three monopolar electrodes.

The cue-based screening paradigm consisted of two classes, namely the motor imagery (MI) of left hand (class 1) and right hand (class 2). Each subject participated in two screening sessions without feedback recorded on two different days within two weeks. Each session consisted of six runs with ten trials each and two classes of imagery. This resulted in 20 trials per run and 120 trials per session.

Data of 120 repetitions of each MI class were available for each person in total. Prior to the first motor im- agery training the subject executed and imagined different movements for each body part and selected the one which they could imagine best (e. g., squeezing a ball or pulling a brake).

Each trial started with a fixation cross and an additional short acoustic warning tone (1 kHz, 70 ms). Some seconds later a visual cue was presented for 1.25 seconds. Afterwards the subjects had to imagine the corresponding hand movement over a period of 4 seconds. Each trial was followed by a short break of at least 1.5 seconds. A randomized time of up to 1 second was added to the break to avoid adaptation

For the three online feedback sessions four runs with smiley feedback were recorded, whereby each run consisted of twenty trials for each type of motor imagery. At the beginning of each trial (second 0) the feedback (a gray smiley) was centered on the screen. At second 2, a short warning beep (1 kHz, 70 ms) was given. The cue was presented from second 3 to 7.5. At second 7.5 the screen went blank and a random interval between 1.0 and 2.0 seconds was added to the trial.

.. [1] R. Leeb, F. Lee, C. Keinrath, R. Scherer, H. Bischof, G. Pfurtscheller. Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15, 473–482, 2007

"""Motor Imagery dataset from Weibo et al 2014.

Dataset from the article Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery [1]_.

It contains data recorded on 10 subjects, with 60 electrodes.

This dataset was used to investigate the differences of the EEG patterns between simple limb motor imagery and compound limb motor imagery. Seven kinds of mental tasks have been designed, involving three tasks of simple limb motor imagery (left hand, right hand, feet), three tasks of compound limb motor imagery combining hand with hand/foot (both hands, left hand combined with right foot, right hand combined with left foot) and rest state.

At the beginning of each trial (8 seconds), a white circle appeared at the center of the monitor. After 2 seconds, a red circle (preparation cue) appeared for 1 second to remind the subjects of paying attention to the character indication next. Then red circle disappeared and character indication (‘Left Hand’, ‘Left Hand & Right Foot’, et al) was presented on the screen for 4 seconds, during which the participants were asked to perform kinesthetic motor imagery rather than a visual type of imagery while avoiding any muscle movement. After 7 seconds, ‘Rest’ was presented for 1 second before next trial (Fig. 1(a)). The experiments were divided into 9 sections, involving 8 sections consisting of 60 trials each for six kinds of MI tasks (10 trials for each MI task in one section) and one section consisting of 80 trials for rest state. The sequence of six MI tasks was randomized. Intersection break was about 5 to 10 minutes.

.. [1] Yi, Weibo, et al. "Evaluation of EEG oscillatory patterns and cognitive process during simple and compound limb motor imagery." PloS one 9.12 (2014). https://doi.org/10.1371/journal.pone.0114853 """

#%% PhysionetMI

"""Physionet Motor Imagery dataset.

Physionet MI dataset: https://physionet.org/pn4/eegmmidb/

This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers.

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org). Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks:

1. A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes.

2. A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes.

3. A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

4. A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

imagined: bool (default True) if True, return runs corresponding to motor imagination.

executed: bool (default False) if True, return runs corresponding to motor execution.

.. [1] Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N. and Wolpaw, J.R., 2004. BCI2000: a general-purpose brain-computer interface (BCI) system. IEEE Transactions on biomedical engineering, 51(6), pp.1034-1043.

.. [2] Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E. and PhysioBank, P., PhysioNet: components of a new research resource for complex physiologic signals Circulation 2000 Volume 101 Issue 23 pp. E215–E220. """