  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).
