A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface

Abstract

In building a practical and robust brain-computer interface (BCI), the classification of motor imagery (MI) from electroencephalography (EEG) across multiple days is a long-standing challenge due to the large variability of the EEG signals. We collected a large dataset of MI from 5 different days with 25 subjects, the first open-access dataset to address BCI issues across 5 different days with a large number of subjects. The dataset includes 5 session data from 5 different days (2–3 days apart) for each subject. Each session contains 100 trials of left-hand and right-hand MI. In this report, we provide the benchmarking classification accuracy for three conditions, namely, within-session classification (WS), cross-session classification (CS), and cross-session adaptation (CSA), with subject-specific models. WS achieves an average classification accuracy of up to 68.8%, while CS degrades the accuracy to 53.7% due to the cross-session variability. However, by adaptation, CSA improves the accuracy to 78.9%. We anticipate this new dataset will significantly push further progress in MI BCI research in addressing the cross-session and cross-subject challenge.

Classification of Motor-Imagery Tasks Using a Large EEG Dataset by Fusing Classifiers Learning on Wavelet-Scattering Features

“…SPE, SEN, PRE, and F 1 values provided by the WITS-SVM are balanced, which are 74% and 77%, 77%, and 0.75, respectively. The 10-fold CV accuracies obtained from other methods (CSP, FBCSP, FBCNet, EEGNet, and deep ConvNets) reported in a previous study [5] as shown in Table II do not support the 1% statistical significance level ( p ≮ 0.01), whereas those obtained from WIS-SVM, WTS-SVM, and WTIS-SVM do.…”

“…Figure 6 shows the training progresses of the LSTM and Bi-LSTM, in which convergences were reached at early stages. The 10-fold CV results produced from CSP, FBCSP, FBCNet, EEGNet, and deep ConvNets as shown in Table II were originally reported in reference [5]. Table III shows the 95% confidence intervals of the mean accuracies computed from the t-test of 10-fold CV obtained from the SVM-based classifiers using wavelet scattering features of the EGG signals, FRPs of the EGG signals, and fusion of two SVM-based classifiers.…”

“…The publicly available big MI-BCI EEG database [5] used in this study consists of EEG recordings from 25 participants (males = 13, females = 12, and years of age = 20-24). Each participant performed 5 independent sessions over 2 or 3 days, where each session included 100 trials of left-hand (class 1) and right-hand (class 2) grasping MI, and each trial lasted about 7.5 sec.…”

“…To compare the proposed approach with a deep-learning model other than those reported in [5], both long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM) networks [43], [44] were used to learn on and classify the same training and test data, respectively. LSTM networks are a popular recurrent deep-learning approach for classifying sequential data or time series because these networks can learn long-term dependencies between time steps of the data.…”

“…MI-based brain-computer interface (MI-BCI) is a signal-based coordination that enables the control or recognition of brain activity over computer-based devices [3], [4]. In comparison with other BCI methods, MI-BCI allows direct interaction between a user and external devices without limb movement or stimulation [5].…”