Utilization of intracranial electroencephalography (iEEG) is rapidly increasing for clinical and brain-computer interface applications. iEEG facilitates the recording of neural activity with high spatial and temporal resolution, making it a desirable neuroimaging modality for studying neural dynamics. Despite its benefits, iEEG faces challenges such as inter-subject variability in electrode implantation, which makes the development of unified neural decoder models across different patients difficult. In this research, we introduce a novel decoder model that is robust to inter-subject electrode implantation variability. We call this model RISE-iEEG, which stands for Robust Inter-Subject Electrode Implantation Variability iEEG Classifier. RISE-iEEG employs a deep neural network structure preceded by a patient-specific projection network. The projection network maps the neural data of individual patients onto a common low-dimensional space, compensating for the implantation variability. This model can be applied across multiple patients' data without requiring the coordinates of electrode for each patient. The performance of RISE-iEEG across multiple datasets, including the Audio-Visual dataset, Music Reconstruction dataset, and Upper-Limb Movement dataset, surpasses that of state-of-the-art iEEG decoder models such as HTNet and EEGNet. Our analysis shows that RISE-iEEG outperforms HTNet and EEGNet by 10% in terms of the F1 score, achieving an average F1 score of 0.83 ± 0.03 on the Music Reconstruction dataset, the highest among all eva‎luated methods. Furthermore, the analysis of projection network weights in the Music Reconstruction dataset across patients suggests that the Superior Temporal lobe serves as the primary encoding neural node. This finding aligns with the auditory processing physiology. The projection network effectively addresses the variability in electrode implantation, significantly improving classification accuracy. Therefore, the proposed model will find broad applications in neural decoding tasks, offering better decoding accuracy without sacrificing interpretability and generalization.

بهزاد نظري

فرزانه شايق بروجني , جلال ذهبي