用AR+脑电提升运动意图识别，让瘫痪患者康复训练更主动。
Enhancing Interpretability of AR-SSVEP-Based Motor Intention Recognition via CNN-BiLSTM and SHAP Analysis on EEG Data
- 结合CNN-BiLSTM与多头注意力，提取脑电信号中运动意图特征
- 在7名健康人上实现高精度实时运动意图识别，可支持康复训练
- 用SHAP可视化特征贡献，让模型决策过程透明可解释
运动功能障碍患者在康复训练中主观参与度低。传统基于稳态视觉诱发电位（SSVEP）的脑机接口系统依赖外部视觉刺激设备，限制了其在真实场景中的应用。本研究提出一种增强现实稳态视觉诱发电位（AR-SSVEP）系统，以解决患者主动性不足及治疗师负担过重的问题。首先，设计四类基于HoloLens 2的脑电实验任务，采集七名健康受试者的脑电数据进行分析。其次，在传统CNN-BiLSTM架构基础上引入多头注意力机制（MACNN-BiLSTM），提取十项时频特征输入卷积神经网络学习高层表征，再通过双向长短期记忆网络建模序列依赖性，并利用多头注意力聚焦于与运动意图相关的模式。最后，采用SHAP（SHapley Additive exPlanations）方法可视化各脑电特征对模型决策的贡献，显著提升模型可解释性。研究结果提升了实时运动意图识别性能，为运动功能障碍患者的康复提供有效支持。
原文摘要 · Abstract (English)
Patients with motor dysfunction show low subjective engagement in rehabilitation training. Traditional SSVEP-based brain-computer interface (BCI) systems rely heavily on external visual stimulus equipment, limiting their practicality in real-world settings. This study proposes an augmented reality steady-state visually evoked potential (AR-SSVEP) system to address the lack of patient initiative and the high workload on therapists. Firstly, we design four HoloLens 2-based EEG classes and collect EEG data from seven healthy subjects for analysis. Secondly, we build upon the conventional CNN-BiLSTM architecture by integrating a multi-head attention mechanism (MACNN-BiLSTM). We extract ten temporal-spectral EEG features and feed them into a CNN to learn high-level representations. Then, we use BiLSTM to model sequential dependencies and apply a multi-head attention mechanism to highlight motor-intention-related patterns. Finally, the SHAP (SHapley Additive exPlanations) method is applied to visualize EEG feature contributions to the neural network's decision-making process, enhancing the model's interpretability. These findings enhance real-time motor intention recognition and support recovery in patients with motor impairments.
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