Recognition of Drivers' Hard and Soft Braking Intentions Based on Hybrid Brain-Computer Interfaces.

IF 10.5 Q1 ENGINEERING, BIOMEDICAL
Cyborg and bionic systems (Washington, D.C.) Pub Date : 2022-07-19 eCollection Date: 2022-01-01 DOI:10.34133/2022/9847652
Jiawei Ju, Aberham Genetu Feleke, Longxi Luo, Xinan Fan
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引用次数: 0

Abstract

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.

基于脑机混合接口的驾驶员硬、软制动意图识别
在本文中,我们提出了同步和顺序混合脑机接口(hBCIs),结合脑电图(EEG)和肌电图(EMG)信号来分类驾驶员的硬制动,软制动和正常驾驶意图,以更好地辅助驾驶。同时hbci采用特征级融合策略(hBCI-FL)和分类器级融合策略(hbci - cl)。顺序hbci包括hBCI-SE1,其中脑电图信号优先检测硬制动,以及hBCI-SE2,其中肌电信号优先检测硬制动。实验结果表明,结合光谱特征和1 -vs-rest分类策略的hBCI-SE1在hbci中表现最好,平均系统准确率为96.37%。该工作对于开发以人为中心的智能辅助驾驶系统,提高驾驶安全性和舒适性,促进脑机接口的应用具有重要价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
7.70
自引率
0.00%
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审稿时长
21 weeks
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