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

arXiv: https://arxiv.org/abs/2207.12369 · HTML: https://mlsanity.com/paper/2207.12369/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2022.

## Built on by (most cited first, 17 of 17)

- [Visual Decoding and Reconstruction via EEG Embeddings with Guided Diffusion](https://mlsanity.com/paper/2403.07721/) (2024)
- [CSBrain: A Cross-scale Spatiotemporal Brain Foundation Model for EEG Decoding](https://mlsanity.com/paper/2506.23075/) (2025)
- [The largest EEG-based BCI reproducibility study for open science: the MOABB benchmark](https://mlsanity.com/paper/2404.15319/) (2024)
- [EEG Foundation Challenge: From Cross-Task to Cross-Subject EEG Decoding](https://mlsanity.com/paper/2506.19141/) (2025)
- [MindBigData 2022 A Large Dataset of Brain Signals](https://mlsanity.com/paper/2212.14746/) (2022)
- [Pseudo-online framework for BCI evaluation: A MOABB perspective](https://mlsanity.com/paper/2308.11656/) (2023)
- [Geometric Neural Network based on Phase Space for BCI-EEG decoding](https://mlsanity.com/paper/2403.05645/) (2024)
- [Mitigating Subject Dependency in EEG Decoding with Subject-Specific Low-Rank Adapters](https://mlsanity.com/paper/2510.08059/) (2025)
- [GEPD:GAN-Enhanced Generalizable Model for EEG-Based Detection of Parkinson's Disease](https://mlsanity.com/paper/2508.14074/) (2025)
- [Bidirectional Time-Frequency Pyramid Network for Enhanced Robust EEG Classification](https://mlsanity.com/paper/2510.10004/) (2025)
- [The Challenge of Out-Of-Distribution Detection in Motor Imagery BCIs](https://mlsanity.com/paper/2603.13324/) (2026)
- [A Multi-dimensional Framework for Evaluating Generalization in EEG Foundation Models](https://mlsanity.com/paper/2605.28563/) (2026)
- [Learning aligned EEG representations with subject-specific encoders](https://mlsanity.com/paper/2606.16462/) (2026)
- [Zero-Shot Neural Priors for Generalizable Cross-Subject and Cross-Task EEG Decoding](https://mlsanity.com/paper/2606.23706/) (2026)
- [Enhancing Robustness of Asynchronous EEG-Based Movement Prediction using Classifier Ensembles](https://mlsanity.com/paper/2601.04286/) (2026)
- [Structure-Guided Diffusion Model for EEG-Based Visual Cognition Reconstruction](https://mlsanity.com/paper/2604.22649/) (2026)
- [Addressing Limited Data in Auditory Attention Decoding with Diffusion Generative Models](https://mlsanity.com/paper/2607.18345/) (2026)

## Abstract (arXiv)

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).