College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China.
Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai 201210, China.
Sensors (Basel). 2022 Jul 28;22(15):5631. doi: 10.3390/s22155631.
The brain-computer interface (BCI) is a highly cross-discipline technology and its successful application in various domains has received increasing attention. However, the BCI-enabled automobile industry is has been comparatively less investigated. In particular, there are currently no studies focusing on brain-controlled driving mode selection. Specifically, different driving modes indicate different driving styles which can be selected according to the road condition or the preference of individual drivers.
In this paper, a steady-state visual-evoked potential (SSVEP)-based driving mode selection system is proposed. Upon this system, drivers can select the intended driving modes by only gazing at the corresponding SSVEP stimuli. A novel EEG processing algorithm named inter-trial distance minimization analysis (ITDMA) is proposed to enhance SSVEP detection. Both offline and real-time experiments were carried out to validate the effectiveness of the proposed system.
The results show that a high selection accuracy of up to 92.3% can be realized, although this depends on the specific choice of flickering duration, the number of EEG channels, and the number of training signals. Additionally, energy consumption is investigated in terms of which the proposed brain-controlled system considerably differs from a traditional driving mode selection system, and the main reason is shown to be the existence of a detection error.
脑机接口（BCI）是一门高度交叉学科的技术，其在各个领域的成功应用受到了越来越多的关注。然而，在汽车行业，BCI 的应用还相对较少。特别是，目前还没有研究专注于脑控驾驶模式选择。具体来说，不同的驾驶模式表示不同的驾驶风格，可以根据道路状况或驾驶员个人喜好进行选择。
本文提出了一种基于稳态视觉诱发电位（SSVEP）的驾驶模式选择系统。在该系统中，驾驶员只需注视相应的 SSVEP 刺激，即可选择预期的驾驶模式。提出了一种新的 EEG 处理算法，称为试验间距离最小化分析（ITDMA），以增强 SSVEP 检测。进行了离线和实时实验以验证所提出系统的有效性。
结果表明，尽管选择的闪烁持续时间、EEG 通道数量和训练信号数量等因素会影响选择精度，但仍可以实现高达 92.3%的高选择精度。此外，还研究了能量消耗，与传统的驾驶模式选择系统相比，所提出的脑控系统的能量消耗有很大的不同，主要原因是存在检测误差。
