TriNet: A Hybrid Feature Integration Approach for Motor Imagery Classification in Brain-Computer Interface

Brain-computer interface (BCI) is inevitably a promising technology holding the potential to revolutionize the world with its wide range of applications. From healthcare to innovative computer gaming, integrating BCI for intelligent control has become an emergent scope. However, optimizing motor imagery (MI) classification in non-invasive BCI remains a significant challenge due to the poor quality of the acquired signal. In this paper, we propose a unified approach for MI classification by combining features from three diverse domains. Initially, the EEG data is preprocessed using bandpass filtering to extract the relevant EEG signals. Next, the preprocessed signal is fed simultaneously to three branches to extract three distinct categories of features from the signal. Specifically, spectral features are extracted using the fast-Fourier transform (FFT) and a spatial transformer is utilized to extract spatial features from the EEG data. Moreover, the third branch extracts temporal features using an encoder-decoder architecture. The features obtained using the three branches are concatenated together to obtain a comprehensive features set which is finally classified using extreme learning machine (ELM). Our proposed approach which uses a novel combination of features from three distinct domains is hereby named TriNet and is validated using two benchmark datasets BCI Competition IV-2a and BCI Competition IV-2b. The experimental results show an accuracy of 87.30% and 92.64% respectively on BCI IV-2a and BCI IV-2b datasets in subject-specific classification. Moreover, TriNet is also tested in subject-independent classification setup, and average classification accuracies of 63.92% and 78.60% are obtained on BCI IV-2a and 2b datasets respectively which is an improvement of 8 to 10% compared to the existing methods. The classification performance and computational cost comparisons demonstrate the superior performance of TriNet compared to the existing methods highlighting its potential to enhance MI classification in BCI.