用脑电波控制轮椅，深度学习提升识别准确率
EEG-based AI-BCI Wheelchair Advancement: A Brain-Computer Interfacing Wheelchair System Using Deep Learning Approach
- 基于脑电图信号，用五种模型分析用户意图
- XGBoost模型准确率达60%，表现最佳
- 可集成到树莓派系统，适合残障人士辅助出行
本研究提出一种基于脑机接口（BCI）的智能轮椅系统，利用人工智能技术分析脑电信号实现轮椅导航。系统采用滑动窗口法将预处理后的脑电数据划分为固定长度片段，每段包含统计特征、不同频段的傅里叶系数及对应操作标签，数据来自公开的Kaggle数据集。对比了五种模型：XGBoost、CatBoost、GRU、SVC。其中XGBoost表现最优，准确率达60%；而CatBoost在训练与测试间误差显著，存在过拟合现象。最佳模型SVC被集成至tkinter图形界面，成功模拟出多方向轮椅运动。最终提出一套基于树莓派的脑机接口轮椅系统，具备实际部署潜力。
原文摘要 · Abstract (English)
This study offers a revolutionary strategy to developing wheelchairs based on the Brain-Computer Interface (BCI) that incorporates Artificial Intelligence (AI) using a The device uses electroencephalogram (EEG) data to mimic wheelchair navigation. Five different models were trained on a pre-filtered dataset that was divided into fixed-length windows using a sliding window technique. Each window contained statistical measurements, FFT coefficients for different frequency bands, and a label identifying the activity carried out during that window that was taken from an open-source Kaggle repository. The XGBoost model outperformed the other models, CatBoost, GRU, SVC, and XGBoost, with an accuracy of 60%. The CatBoost model with a major difference between training and testing accuracy shows overfitting, and similarly, the best-performing model, with SVC, was implemented in a tkinter GUI. The wheelchair movement could be simulated in various directions, and a Raspberry Pi-powered wheelchair system for brain-computer interface is proposed here.
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