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

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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.