EAD: An EEG Adapter for Automated Classification

TL;DR

This paper introduces EEG Adapter (EAD), a versatile framework that enhances EEG signal classification across different devices and tasks by leveraging a foundational model, achieving state-of-the-art accuracy and demonstrating strong generalization.

Contribution

The paper presents EAD, a novel adaptable framework for EEG classification that works with any acquisition device and improves robustness and accuracy over existing methods.

Findings

Achieved state-of-the-art accuracy of 99.33% on EEG-ImageNet

Achieved state-of-the-art accuracy of 92.31% on BrainLat

Demonstrated effective zero-shot EEG classification

Abstract

While electroencephalography (EEG) has been a popular modality for neural decoding, it often involves task specific acquisition of the EEG data. This poses challenges for the development of a unified pipeline to learn embeddings for various EEG signal classification, which is often involved in various decoding tasks. Traditionally, EEG classification involves the step of signal preprocessing and the use of deep learning techniques, which are highly dependent on the number of EEG channels in each sample. However, the same pipeline cannot be applied even if the EEG data is collected for the same experiment but with different acquisition devices. This necessitates the development of a framework for learning EEG embeddings, which could be highly beneficial for tasks involving multiple EEG samples for the same task but with varying numbers of EEG channels. In this work, we propose EEG Adapter (EAD), a flexible framework compatible with any signal acquisition device. More specifically, we leverage a recent EEG foundational model with significant adaptations to learn robust representations from the EEG data for the classification task. We evaluate EAD on two publicly available datasets achieving state-of-the-art accuracies 99.33% and 92.31% on EEG-ImageNet and BrainLat respectively. This illustrates the effectiveness of the proposed framework across diverse EEG datasets containing two different perception tasks: stimulus and resting-state EEG signals. We also perform zero-shot EEG classification on EEG-ImageNet task to demonstrate the generalization capability of the proposed approach.