Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D−CNN

This study proposed a motor imagery (MI) classification pipeline featuring a 1−dimensional convolutional neural network (1D-CNN) with different time/frequency feature representation techniques. The objective was to classify right hand (RH) versus right foot (RF) MI tasks in both intra- and inter-subject (pairwise and pooled) BCI settings using a 1D-CNN architecture trained on time-domain bandpass filtered electroencephalography (EEG) signals, frequency-embedded power spectral density (PSD) and cross-power spectral density (CPSD) sequences. The EEG signals were bandpass filtered with 4 Hz and 32 Hz cut-off frequencies, and PSD/CPSD sequences were estimated in the same frequency range. Thus, the number of input channels for 1D-CNN was N , N or N × N for EEG signals, PSD or CPSD sequences. We used dataset IVa from BCI Competition III in 5−fold cross-validation settings to evaluate intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCI classification accuracies. We compared the performance of the proposed methods with classification algorithms featuring common spatial patterns (CSP) for benchmarking. The best overall classification accuracies (%) for intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCIs were 86.57±11.69, 70.80±9.21 and 76.61±12.37 using 1D−CNN with time-domain EEG signals. The average classification accuracies using 1D-CNN with frequency-embedded PSD sequences were 82.57±10.20, 69.32±7.46 and 71.32±7.96 for intra-subject, inter-subject (pairwise) and inter-subject (pooled) BCIs. The proposed time/frequency feature representation techniques with 1D-CNN outperformed CSP-based algorithms (p-value < 0.05). The comparative results suggest the utility of the proposed methods for MI classification, especially for a fully zero-training inter-subject BCI.