面向脑控车辆的模糊融合控制方法研究

doi:10.3778/j.issn.1002-8331.2101-0325

摘要/Abstract

摘要： 脑控车辆（brain-controlled vehicle，BCV）是指利用脑机接口（brain-computer interface，BCI）解析驾驶员的脑电信号（electroencephalogram，EEG）从而获得控制命令的车辆，其性能受BCI本身性能影响很大。目前，BCI的识别准确率、可识别分类的指令数都受限，并且其指令识别时间较长，因此仅靠脑电信号控制的车辆，其控制性能并不理想。针对在BCI性能受限的情况下提高脑控车辆的控制性能这一问题，基于模糊逻辑，提出了一种模糊脑控融合控制的方法：基于模糊离散事件系统（fuzzy discrete event system，FDES）监督理论对于驾驶员给出的脑控指令的正确程度进行监督评估；同时基于模糊逻辑设计一个自动控制器根据车辆当前状况进行模糊推理得到自动决策；根据评估后驾驶员指令的正确程度与自动决策进行二次模糊推理，对自动决策作出更符合人意图的调整，得到最终决策。为证明所提方法的有效性，采用一种新型的SSVEP（steady-state visual evoked potential）型脑机接口设备。并基于此平台，设计了后续实验，验证了所提出的方法能够在BCI性能受限的情况下提高脑控车辆的控制性能。

关键词: 脑机接口, 脑控车辆, 脑电信号, 模糊逻辑

Abstract: A brain-controlled vehicle（BCV） is a vehicle that uses the brain-computer interface（BCI） to analyze the driver’s electroencephalogram（EEG） to obtain human control commands. Its performance is greatly affected by the capability of BCI. At present, the recognition accuracy of BCI and the number of instructions that can be recognized are limited. At the same time, the recognition time of instructions is relatively long. Therefore, the performance of BCV controlled only by EEG signals is usually unsatisfactory. Aiming at the problem of improving the performance of BCV under the condition of limited BCI performance, a fusion brain control strategy based on fuzzy logic is proposed. Firstly, based on fuzzy discrete event system（FDES） supervision theory, instructions of the driver are supervised and evaluated. At the same time, based on fuzzy logic, an automatic controller which gives an automatic decision through fuzzy reasoning according to the current state of the vehicle is designed. Then according to the automatic decision and the correct degree of the driver’s instructions, a second fuzzy reasoning is made to obtain the final decision, making the final decision more in line with human intentions. To verify the effectiveness of the proposed method, a new type of steady-state visual evoked potential（SSVEP） BCI device is adopted. And based on this platform, the follow-up experiments are designed to verify that the proposed method can improve the performance of BCV under the condition of limited BCI performance.

Key words: brain-computer interface, brain-controlled vehicle, electroencephalogram（EEG） signal, fuzzy logic

董娜, 张文锜, 吴志强. 面向脑控车辆的模糊融合控制方法研究[J]. 计算机工程与应用, 2022, 58(17): 298-305.

DONG Na, ZHANG Wenqi, WU Zhiqiang. Research on Fuzzy Fusion Control Method for Brain-Controlled Vehicles[J]. Computer Engineering and Applications, 2022, 58(17): 298-305.

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