Deep Learning Classification of EEG Responses to Multi-Dimensional Transcranial Electrical Stimulation

TL;DR

This paper presents a deep learning framework that classifies EEG responses to transcranial electrical stimulation, achieving high accuracy and surpassing human performance, with potential for bedside consciousness assessment.

Contribution

It introduces a novel deep learning approach to classify EEG responses to multi-dimensional TES, demonstrating high accuracy and open-sourcing the dataset and code for reproducibility.

Findings

Convolutional Neural Network achieved 92% F1-score.

TDCS stimulation of the angular gyrus elicited reliable responses.

Framework surpasses human-level classification accuracy.

Abstract

A major shortcoming of medical practice is the lack of an objective measure of conscious level. Impairment of consciousness is common, e.g. following brain injury and seizures, which can also interfere with sensory processing and volitional responses. This is also an important pitfall in neurophysiological methods that infer awareness via command following, e.g. using functional MRI or electroencephalography (EEG). Transcranial electrical stimulation (TES) can be employed to non-invasively stimulate the brain, bypassing sensory inputs, and has already showed promising results in providing reliable indicators of brain state. However, current non-invasive solutions have been limited to magnetic stimulation, which is not easily translatable to clinical settings. Our long-term vision is to develop an objective measure of brain state that can be used at the bedside, without requiring patients to understand commands or initiate motor responses. In this study, we demonstrated the feasibility of a framework using Deep Learning algorithms to classify EEG brain responses evoked by a defined multi-dimensional pattern of TES. We collected EEG-TES data from 11 participants and found that delivering transcranial direct current stimulation (tDCS) to posterior cortical areas targeting the angular gyrus elicited an exceptionally reliable brain response. For this paradigm, our best Convolutional Neural Network model reached a 92% classification F1-score on Holdout data from participants never seen during training, significantly surpassing human-level performance at 60-70% accuracy. These findings establish a framework for robust consciousness measurement for clinical use. In this spirit, we documented and open-sourced our datasets and codebase in full, to be used freely by the neuroscience and AI research communities, who may replicate our results with free tools like GitHub, Kaggle, and Colab.