用深度学习与回声状态网络解码脑电信号，提升运动行为识别准确率。
Decoding Motor Behavior Using Deep Learning and Reservoir Computing
- 融合卷积神经网络与回声状态网络，兼顾空间特征与时间动态建模
- 在自选受试者下达到83.2%准确率，留一被试者交叉验证达51.3%
- 适合关注非侵入式脑机接口与时间序列建模的研究者使用
我们提出一种新型非侵入式脑机接口中的脑电信号解码方法，聚焦运动行为分类。传统卷积网络如EEGNet和DeepConvNet虽能捕捉局部空间模式，却难以建模长程时间依赖与非线性动力学。为此，我们在解码流程中引入回声状态网络（ESN），该模型构建高维稀疏连接的递归蓄水池，擅长追踪时间动态，从而补充CNN的空间表征能力。在经PREP预处理管道处理的滑板动作脑电数据集上，基于MNE-Python实现的ESNNet在受试者内测试中达到83.2%准确率，在留一被试者交叉验证中达51.3%，优于广泛使用的基于CNN的基线模型。代码已公开于https://github.com/Yutiankunkun/Motion-Decoding-Using-Biosignals。
原文摘要 · Abstract (English)
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
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