To improve the performance of brain-controlled intelligent car based on motor imagery (MI), a method based on neurofeedback (NF) with electroencephalogram (EEG) for controlling intelligent car is proposed. A mental strategy of MI in which the energy column diagram of EEG features related to the mental activity is presented to subjects with visual feedback in real time to train them to quickly master the skills of MI and regulate their EEG activity, and combination of multi-features fusion of MI and multi-classifiers decision were used to control the intelligent car online. The average, maximum and minimum accuracy of identifying instructions achieved by the trained group (trained by the designed feedback system before the experiment) were 85.71%, 90.47% and 76.19%, respectively and the corresponding accuracy achieved by the control group (untrained) were 73.32%, 80.95% and 66.67%, respectively. For the trained group, the average, longest and shortest time consuming were 92 s, 101 s, and 85 s, respectively, while for the control group the corresponding time were 115.7 s, 120 s, and 110 s, respectively. According to the results described above, it is expected that this study may provide a new idea for the follow-up development of brain-controlled intelligent robot by the neurofeedback with EEG related to MI.
为了提高基于运动想象（MI）的脑控智能小车的控制性能，本文提出一种基于脑电（EEG）信号神经反馈（NF）控制智能小车的方法。采用 MI 心理策略，通过实时呈现该心理活动相关 EEG 信号特征的能量柱形图给受试者，训练受试者快速掌握 MI 技能并调节其 EEG 信号的活动，并以 MI 多特征融合和多分类器决策相结合的方法，从而在线脑控智能小车。训练组（试验前接受设计的反馈系统训练）取得平均、最高和最低的识别指令准确率分别为 85.71%、90.47% 和 76.19%，对照组（不接受训练）对应的准确率分别为 73.32%、80.95% 和 66.67%；训练组平均、最长和最短用时分别为 92 s、101 s 和 85 s，对照组对应的用时分别为 115.7 s、120 s 和 110 s。通过以上试验研究结果，期望本文可为后续基于 MI 的 EEG 信号 NF 控制智能机器人的开发提供新的思路。.
Keywords: brain-controlled robot; motor imagery; multi-classifiers decision; multi-features fusion; neurofeedback with electroencephalogram.