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通过脑电图信号解读心理构想的有效性。通过评估16种主流神经网络模型及其变体在四种脑机接口范式中的表现，我们衡量了其信息表征能力。基于全面的文献综述结果，我们提出了EEGNeX——一种全新的纯卷积神经网络架构。我们将其与现有前沿策略及涵盖11种不同脑电运动想象分类任务的MOABB基准进行对比，发现EEGNeX优于其他最先进方法。值得注意的是，在不同场景下，其分类准确率相比竞品提升2.1%-8.5%，且具有统计学显著性（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深入见解，也为未来探索脑电生物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，我们已将包含EEGNeX在内的所有基准模型公开发布于(https://github.com/chenxiachan/EEGNeX)。