Neural decoding from stereotactic EEG: accounting for electrode variability across subjects

TL;DR

This paper presents seegnificant, a novel neural decoding framework for sEEG data that effectively integrates variable electrode placements across subjects, enabling behavior prediction and transfer learning.

Contribution

It introduces a scalable model architecture that accounts for electrode variability, allowing multi-subject training and cross-subject generalization in sEEG neural decoding.

Findings

Successfully decoded response time from neural data across subjects.

Demonstrated transfer learning with few-shot adaptation to new subjects.

Provided a scalable framework for multi-subject sEEG data integration.

Abstract

Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial. However, in sEEG cohorts, each subject has a variable number of electrodes placed at distinct locations in their brain, solely based on clinical needs. Such heterogeneity in electrode number/placement poses a significant challenge for data integration, since there is no clear correspondence of the neural activity recorded at distinct sites between individuals. Here we introduce seegnificant: a training framework and architecture that can be used to decode behavior across subjects using sEEG data. We tokenize the neural activity within electrodes using convolutions and extract long-term temporal dependencies between tokens using self-attention in the time dimension. The 3D location of each electrode is then mixed with the tokens, followed by another self-attention in the electrode dimension to extract effective spatiotemporal neural representations. Subject-specific heads are then used for downstream decoding tasks. Using this approach, we construct a multi-subject model trained on the combined data from 21 subjects performing a behavioral task. We demonstrate that our model is able to decode the trial-wise response time of the subjects during the behavioral task solely from neural data. We also show that the neural representations learned by pretraining our model across individuals can be transferred in a few-shot manner to new subjects. This work introduces a scalable approach towards sEEG data integration for multi-subject model training, paving the way for cross-subject generalization for sEEG decoding.