This paper presents a self-adaptive autonomous online learning through a general type-2 fuzzy system (GT2 FS) for the motor imagery (MI) decoding of a brain-machine interface (BMI) and navigation of a bipedal humanoid robot in a real experiment, using electroencephalography (EEG) brain recordings only. GT2 FSs are applied to BMI for the first time in this study. We also account for several constraints commonly associated with BMI in real practice: 1) the maximum number of EEG channels is limited and fixed; 2) no possibility of performing repeated user training sessions; and 3) desirable use of unsupervised and low-complexity feature extraction methods. The novel online learning method presented in this paper consists of a self-adaptive GT2 FS that can autonomously self-adapt both its parameters and structure via creation, fusion, and scaling of the fuzzy system rules in an online BMI experiment with a real robot. The structure identification is based on an online GT2 Gath–Geva algorithm where every MI decoding class can be represented by multiple fuzzy rules (models), which are learnt in a continous (trial-by-trial) non-iterative basis. The effectiveness of the proposed method is demonstrated in a detailed BMI experiment, in which 15 untrained users were able to accurately interface with a humanoid robot, in a single session, using signals from six EEG electrodes only.

中文翻译：

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通过通用2型模糊推理系统的仿人机器人自适应在线脑机接口

本文提出了一种自适应自主在线学习，通过通用类型 2 模糊系统 (GT2 FS) 用于脑机接口 (BMI) 的运动图像 (MI) 解码和双足仿人机器人在实际实验中的导航，仅使用脑电图 (EEG) 大脑记录。本研究首次将 GT2 FS 应用于 BMI。我们还考虑了实际实践中通常与 BMI 相关的几个约束条件：1）EEG 通道的最大数量是有限且固定的；2) 不可能进行重复的用户培训课程；3) 理想地使用无监督和低复杂度的特征提取方法。本文提出的新型在线学习方法由自适应 GT2 FS 组成，它可以通过创建、融合、在真实机器人的在线 BMI 实验中对模糊系统规则进行缩放。结构识别基于在线 GT2 Gath-Geva 算法，其中每个 MI 解码类都可以由多个模糊规则（模型）表示，这些规则是在连续（逐个试验）非迭代基础上学习的。所提出方法的有效性在详细的 BMI 实验中得到证明，其中 15 名未受过训练的用户仅使用来自六个 EEG 电极的信号就能够在一次会话中准确地与类人机器人交互。