Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Data

A new geometric-stochastic multimodal deep learning framework has been developed to predict vulnerability to Sudden Unexpected Death in Epilepsy (SUDEP) and acute ischemic stroke, integrating various physiological signals such as EEG, ECG, and fMRI. This approach utilizes advanced mathematical models to enhance predictive accuracy and interpretability of biomarkers derived from complex brain dynamics.

A novel approach to EEG decoding for non-invasive brain-machine interfaces (BMIs) has been introduced, focusing on motor-behavior classification. This method integrates an Echo State Network (ESN) into the decoding pipeline, enhancing the ability to track temporal dynamics while maintaining the spatial representational power of convolutional neural networks (CNNs). The new ESNNet achieved significant accuracy rates, outperforming traditional CNN-based models.

A new Python package named ContextualSHAP has been proposed to enhance SHAP (SHapley Additive exPlanations) by integrating it with OpenAI's GPT, allowing for the generation of contextualized textual explanations tailored to user-defined parameters. This development aims to bridge the gap in providing meaningful explanations for end-users, particularly those lacking technical expertise.