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Objective: Motor Imagery (MI) serves as a crucial experimental paradigmwithin the realm of Brain Computer Interfaces (BCIs), aiming to decoding motorintentions from electroencephalogram (EEG) signals. Method: Drawing inspirationfrom Riemannian geometry and Cross-Frequency Coupling (CFC), this paperintroduces a novel approach termed Riemann Tangent Space Mapping usingDichotomous Filter Bank with Convolutional Neural Network (DFBRTS) to enhancethe representation quality and decoding capability pertaining to MI features.DFBRTS first initiates the process by meticulously filtering EEG signalsthrough a Dichotomous Filter Bank, structured in the fashion of a completebinary tree. Subsequently, it employs Riemann Tangent Space Mapping to extractsalient EEG signal features within each sub-band. Finally, a lightweightconvolutional neural network is employed for further feature extraction andclassification, operating under the joint supervision of cross-entropy andcenter loss. To validate the efficacy, extensive experiments were conductedusing DFBRTS on two well-established benchmark datasets: the BCI competition IV2a (BCIC-IV-2a) dataset and the OpenBMI dataset. The performance of DFBRTS wasbenchmarked against several state-of-the-art MI decoding methods, alongsideother Riemannian geometry-based MI decoding approaches. Results: DFBRTSsignificantly outperforms other MI decoding algorithms on both datasets,achieving a remarkable classification accuracy of 78.16% for four-class and71.58% for two-class hold-out classification, as compared to the existingbenchmarks.

Enhancing Motor Imagery Decoding in Brain Computer Interfaces using Riemann Tangent Space Mapping and Cross Frequency Coupling