基于EEG的AI-BCI轮椅进步：基于变压器的学习，用于脑机接口的运动图像

摘要(中文译文)

本文提出了一种融合人工智能的脑机接口轮椅开发方法，利用运动想象左右手运动机制进行控制。该系统旨在利用脑电图（EEG）数据，基于运动想象的左右手运动来模拟轮椅导航。从开源脑电图数据存储库获取的预过滤数据集被分割为19×200的数组，以捕捉手部运动的起始时刻。数据以200 Hz的采样频率采集。该系统集成了一个基于Tkinter的界面用于模拟轮椅运动，为用户提供了一个功能实用且直观的控制系统。我们提出了TFormerEEG，一种由Transformer驱动的深度学习架构，用于运动想象脑电图分类。与包括XGBoost、EEGNet和EEG-Deformer模型在内的多种机器学习基线模型相比，该模型的测试准确率达到93.04%。通过分层交叉验证，TFormerEEG达到了91.18%的平均准确率，展示了该模型的有效性。

Abstract

This article presents an artificial intelligence integrated approach to brain-computer interface-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 19 × 200 to capture the onset of hand movements. The data were acquired at a sampling frequency of 200 Hz. 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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