Ju Jiawei, Feleke Aberham Genetu, Luo Longxi, Fan Xinan
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China.
Beijing Machine and Equipment Institute China.
Cyborg Bionic Syst. 2022 Jul 19;2022:9847652. doi: 10.34133/2022/9847652. eCollection 2022.
In this paper, we propose simultaneous and sequential hybrid brain-computer interfaces (hBCIs) that incorporate electroencephalography (EEG) and electromyography (EMG) signals to classify drivers' hard braking, soft braking, and normal driving intentions to better assist driving for the first time. The simultaneous hBCIs adopt a feature-level fusion strategy (hBCI-FL) and classifier-level fusion strategies (hBCIs-CL). The sequential hBCIs include the hBCI-SE1, where EEG signals are prioritized to detect hard braking, and hBCI-SE2, where EMG signals are prioritized to detect hard braking. Experimental results show that the proposed hBCI-SE1 with spectral features and the one-vs-rest classification strategy performs best with an average system accuracy of 96.37% among hBCIs. This work is valuable for developing human-centric intelligent assistant driving systems to improve driving safety and driving comfort and promote the application of BCIs.
在本文中，我们首次提出了同时性和顺序性混合脑机接口（hBCI），其结合了脑电图（EEG）和肌电图（EMG）信号，以对驾驶员的急刹车、缓刹车和正常驾驶意图进行分类，从而更好地辅助驾驶。同时性hBCI采用特征级融合策略（hBCI-FL）和分类器级融合策略（hBCIs-CL）。顺序性hBCI包括hBCI-SE1（优先使用EEG信号检测急刹车）和hBCI-SE2（优先使用EMG信号检测急刹车）。实验结果表明，所提出的具有频谱特征和一对多分类策略的hBCI-SE1在hBCI中表现最佳，平均系统准确率为96.37%。这项工作对于开发以用户为中心的智能辅助驾驶系统以提高驾驶安全性和驾驶舒适性以及促进脑机接口的应用具有重要价值。
