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
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