We present a novel approach to EEG decoding for non-invasive brain machine interfaces (BMIs), with a focus on motor-behavior classification. While conventional convolutional architectures such as EEGNet and DeepConvNet are effective in capturing local spatial patterns, they are markedly less suited for modeling long-range temporal dependencies and nonlinear dynamics. To address this limitation, we integrate an Echo State Network (ESN), a prominent paradigm in reservoir computing into the decoding pipeline. ESNs construct a high-dimensional, sparsely connected recurrent reservoir that excels at tracking temporal dynamics, thereby complementing the spatial representational power of CNNs. Evaluated on a skateboard-trick EEG dataset preprocessed via the PREP pipeline and implemented in MNE-Python, our ESNNet achieves 83.2% within-subject and 51.3% LOSO accuracies, surpassing widely used CNN-based baselines. Code is available at https://github.com/Yutiankunkun/Motion-Decoding-Using-Biosignals

翻译：我们提出了一种用于非侵入式脑机接口（BMI）的脑电图解码新方法，重点关注运动行为分类。尽管传统卷积架构如EEGNet和DeepConvNet在捕捉局部空间模式方面效果显著，但在建模长程时间依赖性和非线性动力学方面明显不足。为解决这一局限，我们将储备池计算中的代表性范式——回声状态网络（ESN）整合到解码流程中。ESN构建了一个高维、稀疏连接的循环储备池，擅长追踪时间动态特性，从而与卷积神经网络的空间表征能力形成互补。在通过PREP流程预处理、基于MNE-Python实现的滑板技巧脑电图数据集上评估，我们的ESNNet模型获得了83.2%的个体内准确率和51.3%的留一受试者交叉验证准确率，超越了广泛使用的基于卷积神经网络的基线方法。代码发布于https://github.com/Yutiankunkun/Motion-Decoding-Using-Biosignals