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.

翻译：本研究提出了一种基于脑机接口（BCI）并结合人工智能（AI）的轮椅开发创新策略。该设备利用脑电图（EEG）数据模拟轮椅导航。研究采用滑动窗口技术将预滤波数据集划分为固定长度窗口，在一个开源Kaggle存储库提供的标注数据上训练了五种不同模型。每个窗口包含统计测量值、不同频段的快速傅里叶变换（FFT）系数以及对应窗口内执行活动的标签。XGBoost模型以60%的准确率优于CatBoost、GRU、SVC等其他模型。CatBoost模型因训练与测试准确率存在显著差异而呈现过拟合现象，性能最优的SVC模型则通过tkinter图形用户界面实现。该系统可模拟多方向轮椅运动，并在此提出基于树莓派的脑机接口轮椅系统方案。