Toward reliable signals decoding for electroencephalogram: A benchmark study to EEGNeX

TL;DR

This paper introduces EEGNeX, a novel ConvNet-based neural network architecture that outperforms existing models in EEG signal decoding across multiple BCI tasks, providing a new benchmark for EEG analysis.

Contribution

Proposes EEGNeX, a new ConvNet architecture, and demonstrates its superior performance over state-of-the-art models in EEG decoding benchmarks.

Findings

EEGNeX achieves 2.1%-8.5% higher accuracy than competitors.

EEGNeX statistically significantly outperforms existing methods.

All models are publicly available for scientific use.

Abstract

This study examines the efficacy of various neural network (NN) models in interpreting mental constructs via electroencephalogram (EEG) signals. Through the assessment of 16 prevalent NN models and their variants across four brain-computer interface (BCI) paradigms, we gauged their information representation capability. Rooted in comprehensive literature review findings, we proposed EEGNeX, a novel, purely ConvNet-based architecture. We pitted it against both existing cutting-edge strategies and the Mother of All BCI Benchmarks (MOABB) involving 11 distinct EEG motor imagination (MI) classification tasks and revealed that EEGNeX surpasses other state-of-the-art methods. Notably, it shows up to 2.1%-8.5% improvement in the classification accuracy in different scenarios with statistical significance (p < 0.05) compared to its competitors. This study not only provides deeper insights into designing efficient NN models for EEG data but also lays groundwork for future explorations into the relationship between bioelectric brain signals and NN architectures. For the benefit of broader scientific collaboration, we have made all benchmark models, including EEGNeX, publicly available at (https://github.com/chenxiachan/EEGNeX).