A sliding window and Fourier decomposition method based 1D-CNN framework for motor imagery EEG classification

Electroencephalography (EEG) signal classification is fundamental requirement for effective motor imagery (MI) brain-computer interface (BCI) applications. However, current MI classification methods often depend on handcrafted spatial filtering or complex time-frequency transformations that increase computational complexity. To mitigate this limitation, we propose a unified architecture that integrates a sliding window (SW), a Fourier decomposition method (FDM), and a one-dimensional convolutional neural network (1D-CNN) for MI-EEG classification. The raw EEG data were segmented using the SW approach to augment the data and extract more temporal feature patterns from each trial. The segmented signal, containing time-domain features, was subsequently processed using a band-pass filter and FDM to extract frequency-based features. Here, FDM decomposes the signal into eight orthogonal Fourier Intrinsic Band Functions (FIBFs). A 1D-CNN architecture was employed to learn representations from the decomposed EEG signals. The proposed framework was evaluated on the BCI Competition III Dataset IVa (BCIC III IVa) and BCI Competition IV-2a (BCIC IV 2a) datasets. Our method achieved accuracies of 79.14% and 93.67% on the BCIC IV 2a and BCIC III IVa datasets, respectively, under the subject-dependent evaluation protocol. To evaluate cross-subject generalization, the proposed framework was validated using the Leave-One-Subject-Out (LOSO) protocol, achieving average classification accuracies of 76.49% on the BCIC III Dataset IVa and 55.50% on the BCIC IV 2a dataset. Furthermore, deployment on the NVIDIA Jetson Nano demonstrated complete pipeline latencies of 58.119 ms/trial and 85.055 ms/trial, corresponding to throughputs of 17.21 and 11.76 trials/s for the BCIC III IVa and BCIC IV 2a datasets, respectively. The results show the strong potential of the developed MI-EEG classification method to assist subjects with motor ailments.