Optimizing Brain-Computer Interface Performance: Advancing EEG Signals Channel Selection through Regularized CSP and SPEA II Multi-Objective Optimization

Brain-computer interface systems and the recording of brain activity has garnered significant attention across a diverse spectrum of applications. EEG signals have emerged as a modality for recording neural electrical activity. Among the methodologies designed for feature extraction from EEG data, the method of RCSP has proven to be an approach, particularly in the context of MI tasks. RCSP exhibits efficacy in the discrimination and classification of EEG signals. In optimizing the performance of this method, our research extends to a comparative analysis with conventional CSP techniques, as well as optimized methodologies designed for similar applications. Notably, we employ the meta-heuristic multi-objective Strength Pareto Evolutionary Algorithm II (SPEA-II) as a pivotal component of our research paradigm. This is a state-of-the-art approach in the selection of an subset of channels from a multichannel EEG signal with MI tasks. Our main objective is to formulate an optimum channel selection strategy aimed at identifying the most pertinent subset of channels from the multi-dimensional electroencephalogram (EEG) signals. One of the primary objectives inherent to channel selection in the EEG signal analysis pertains to the reduction of the channel count, an approach that enhances user comfort when utilizing gel-based EEG electrodes. Additionally, within this research, we took benefit of ensemble learning models as a component of our decision-making. This technique serves to mitigate the challenges associated with overfitting, especially when confronted with an extensive array of potentially redundant EEG channels and data noise. Our findings not only affirm the performance of RCSP in MI-based BCI systems, but also underscore the significance of channel selection strategies and ensemble learning techniques in optimizing the performance of EEG signal classification.

An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis

Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder. However, combining complementary information from these two modalities is challenging due to their heterogeneity. Many existing methods fall short of capturing the interaction between these modalities, frequently defaulting to a simple combination of latent features. In this paper, we propose a novel Cross-Attentive Multi-modal Fusion framework (CAMF), which aims to capture both intra-modal and inter-modal relationships between fMRI and sMRI, enhancing multi-modal data representation. Specifically, our CAMF framework employs self-attention modules to identify interactions within each modality while cross-attention modules identify interactions between modalities. Subsequently, our approach optimizes the integration of latent features from both modalities. This approach significantly improves classification accuracy, as demonstrated by our evaluations on two extensive multi-modal brain imaging datasets, where CAMF consistently outperforms existing methods. Furthermore, the gradient-guided Score-CAM is applied to interpret critical functional networks and brain regions involved in schizophrenia. The bio-markers identified by CAMF align with established research, potentially offering new insights into the diagnosis and pathological endophenotypes of schizophrenia.