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).

翻译：本研究评估了不同神经网络模型在通过脑电图信号解读心理构建方面的有效性。通过对16种主流神经网络模型及其变体在四种脑机接口范式下的评估，我们衡量了它们的信息表征能力。基于全面的文献综述发现，我们提出了EEGNeX——一种新型纯卷积神经网络架构。我们将其与现有前沿策略及包含11项不同运动想象脑电解码任务的脑机接口基准库进行对比，结果表明EEGNeX优于其他最先进方法。值得注意的是，在不同场景下，其分类准确率相较于竞争模型具有统计学显著性的2.1%至8.5%提升（p < 0.05）。本研究不仅为设计高效的脑电数据神经网络模型提供了更深入的见解，也为未来探索生物电脑信号与神经网络架构之间的关系奠定了基础。为促进更广泛的科学合作，我们已将包括EEGNeX在内的所有基准模型公开于（https://github.com/chenxiachan/EEGNeX）。