RISE-iEEG: Robust to Inter-Subject Electrodes Implantation Variability iEEG Classifier

TL;DR

RISE-iEEG is a novel deep learning model that enhances cross-subject iEEG decoding by compensating for electrode implantation variability, achieving higher accuracy and interpretability.

Contribution

We introduce RISE-iEEG, a robust neural decoder that generalizes across subjects without needing electrode coordinate data, outperforming existing models.

Findings

RISE-iEEG achieves about 7% higher F1 score than HTNet and EEGNet.

The model's performance is consistent across multiple datasets.

Key brain regions identified align with known physiological functions.

Abstract

Intracranial electroencephalography (iEEG) is increasingly used for clinical and brain-computer interface applications due to its high spatial and temporal resolution. However, inter-subject variability in electrode implantation poses a challenge for developing generalized neural decoders. To address this, 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 to Inter-Subject Electrode Implantation Variability iEEG Classifier. RISE-iEEG employs a deep neural network structure preceded by a participant-specific projection network. The projection network maps the neural data of individual participants onto a common low-dimensional space, compensating for the implantation variability. In other words, we developed an iEEG decoder model that can be applied across multiple participants' data without requiring the coordinates of electrode for each participant. The performance of RISE-iEEG across multiple datasets, including the Music Reconstruction dataset, and AJILE12 dataset, surpasses that of advanced iEEG decoder models such as HTNet and EEGNet. Our analysis shows that the performance of RISE-iEEG is about 7\% higher than that of HTNet and EEGNet in terms of F1 score, with an average F1 score of 0.83, which is the highest result among the evaluation methods defined. Furthermore, Our analysis of the projection network weights reveals that the Superior Temporal and Postcentral lobes are key encoding nodes for the Music Reconstruction and AJILE12 datasets, which aligns with the primary physiological principles governing these regions. This model improves decoding accuracy while maintaining interpretability and generalization.