Objective: The key principle of motor imagery (MI) decoding for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) is to extract task-discriminative features from spectral, spatial, and temporal domains jointly and efficiently, whereas limited, noisy, and non-stationary EEG samples challenge the advanced design of decoding algorithms.
Methods: Inspired by the concept of cross-frequency coupling and its correlation with different behavioral tasks, this paper proposes a lightweight Interactive Frequency Convolutional Neural Network (IFNet) to explore cross-frequency interactions for enhancing representation of MI characteristics. IFNet first extracts spectro-spatial features in low and high-frequency bands, respectively. Then the interplay between the two bands is learned using an element-wise addition operation followed by temporal average pooling. Combined with repeated trial augmentation as a regularizer, IFNet yields spectro-spatio-temporally robust features for the final MI classification. We conduct extensive experiments on two benchmark datasets: the BCI competition IV 2a (BCIC-IV-2a) dataset and the OpenBMI dataset.
Results: Compared with state-of-the-art MI decoding algorithms, IFNet achieves significantly superior classification performance on both datasets while improving the winner's result in BCIC-IV-2a by 11%. Moreover, by conducting sensitivity analysis on decision windows, we show IFNet attains the best trade-off between decoding speed and accuracy. Detailed analysis and visualization verify IFNet can capture the coupling across frequency bands along with the known MI signatures.
Conclusion: We demonstrate the effectiveness and superiority of the proposed IFNet for MI decoding.
Significance: This study suggests IFNet holds promise for rapid response and accurate control in MI-BCI applications.