EEG and EMG-based human-machine interface for navigation of mobility-related assistive wheelchair (MRA-W)

Abstract

The control of human-machine interfaces (HMIs), such as motorized wheelchairs, has been widely investigated using biopotentials produced by electrochemical processes in the human body. However, many studies in this field sometimes overlook crucial factors like special users' needs, who often have inadequate muscle mass and strength, and paresis needed to operate a wheelchair. This study proposes a novel solution: an economical, universally compatible, and user-centric manual-to-powered wheelchair conversion kit. The powered wheelchair is operated using a hybrid control system integrating electroencephalogram (EEG) and electromyography (EMG), utilizing an LSTM network. It uses a low-cost electroencephalogram (EEG) headset and a wearable electromyography (EMG) electrode armband to solve these constraints. The proposed system comprised three crucial objectives: the development of an EEG-based user attentive detection system, an EMG-based navigation system, and a transform conventional wheelchair into a powered wheelchair. Human test subjects were utilized to evaluate the proposed system, and the study complied with accepted ethical guidelines. We selected four EEG features (p < 0.023) for the attentive detection system and six EMG features (p < 0.037) to detect navigation intentions. User attentive detection was achieved at 83.33 (±0.34) %, while the navigation intention system produced 86.67 (±0.52) % accuracy. The overall system was successful in reaching an accuracy rate of 85.0 (±0.19) % and a weighted average precision of 0.89. After the dataset was trained using an LSTM network, the overall accuracy produced was 97.3 (±0.5) %, higher than the accuracy produced by the Quadratic SVM classifier. By giving older and disabled people a more convenient way to use powered wheelchairs, this research helps to build ergonomic and cost-effective biopotential-based HMIs, enhancing their quality of life.