用脑电波控制轮椅，准确率超93%
EEG-based AI-BCI Wheelchair Advancement: Transformer-Based Learning with Motor Imagery for Brain Computer Interface
- 基于Transformer的TFormerEEG模型分析脑电信号
- 测试准确率达93.04%，优于多个基线模型
- 适合神经康复与无障碍智能设备研究者
本文提出一种基于人工智能的脑机接口轮椅控制系统，利用右/左想象运动实现控制。系统采用来自开源脑电数据集的预处理数据，采样频率为200Hz，将数据分割为19×200的数组以捕捉运动起始时刻。设计了基于Tkinter的界面模拟轮椅移动，提供直观操作体验。提出TFormerEEG模型，一种基于Transformer的深度学习架构，用于运动想象脑电信号分类。在测试中，该模型达到93.04%的准确率，显著优于XGBoost、EEGNet和EEG-Deformer等基线模型。通过分层交叉验证，模型平均准确率为91.18%，验证了其有效性。
原文摘要 · Abstract (English)
This paper presents an Artificial Intelligence (AI) integrated approach to Brain-Computer Interface (BCI)-based wheelchair development, utilizing a motor imagery right-left-hand movement mechanism for control. The system is designed to simulate wheelchair navigation based on motor imagery right and left-hand movements using electroencephalogram (EEG) data. A pre-filtered dataset, obtained from an open-source EEG repository, was segmented into arrays of 19x200 to capture the onset of hand movements. The data was acquired at a sampling frequency of 200Hz. The system integrates a Tkinter-based interface for simulating wheelchair movements, offering users a functional and intuitive control system. We propose TFormerEEG, a Transformer-driven deep learning architecture, for motor imagery EEG classification. The model achieves a test accuracy of 93.04% compared with various machine learning baseline models, including XGBoost, EEGNet, and an EEG-Deformer model. The TFormerEEG achieved a mean accuracy of 91.18% through stratified cross-validation, showcasing the effectiveness of this model.
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