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

2024-08-12

arXiv.org

AI Summary

To address poor generalizability in cross-subject intracranial electroencephalography (iEEG) decoding caused by inter-subject variability in electrode implantation locations, this paper proposes a robust classification framework that does not rely on individual electrode spatial coordinates. The core method introduces subject-specific learnable projection networks that adaptively map raw iEEG signals from each participant into a shared low-dimensional representation space, enabling end-to-end training optimized for F1-score maximization. To our knowledge, this is the first approach achieving high-accuracy cross-subject transfer without requiring prior electrode coordinate information, while retaining neurophysiological interpretability—evidenced by brain-region weight visualizations identifying the superior temporal gyrus and postcentral gyrus as key encoding areas. Evaluated on the Music Reconstruction and AJILE12 datasets, the method achieves an average F1-score of 0.83, outperforming HTNet and EEGNet by approximately 7%.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Learning with Manifolds
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization

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.

Problem

• Develops iEEG decoder robust to electrode variability
• Maps neural data to common low-dimensional space
• Improves decoding accuracy across multiple datasets

Innovation

• Deep neural network with projection network
• Maps neural data to common low-dimensional space
• Robust to electrode implantation variability