论文笔记——An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network（10年被引用66次）

题目：利用自适应概率网络设计一种在线脑机接口楼方法控制手部抓握

概要：这篇文章提出了一种新的脑机接口方法，控制手部，系列手部抓握动作和张开在虚拟现实环境中。这篇文章希望在现实生活中利用脑机接口技术控制抓握。BCI研究的一个难点是被试者训练问题。现在，大多数方法采用的离线的无反馈训练

我们研究了被试者在进行运动想象时候，是否能够在没有离线训练而直接就在线训练中取得良好的表现。

另外一个重要的话题是设计在线BCI系统，机器学习的方法分类以不同天数标记的大脑信号。

设计了概率神经网络

只在线训练了三分钟，第一天的分类率就达到了79.0%，第二天的分类率达到了84.0%，而且只是用第一天的分类模型，没有做其他调整。

This paper presents a new online single-trial EEG-based brain鈥揷omputer interface (BCI) for controlling hand holding and sequence of hand grasping and opening in an interactive virtual reality environment. The goal of this research is to develop an interaction technique that will allow the BCI to be effective in real-world scenarios for hand grasp control. One of the major challenges in the BCI research is the subject training. Currently, in most online BCI systems, the classifier was trained offline using the data obtained during the experiments without feedback, and used in the next sessions in which the subjects receive feedback.

We investigated whether the subject could achieve satisfactory online performance without offline training while the subjects receive feedback from the beginning of the experiments during hand movement imagination.

Another important issue in designing an online BCI system is the machine learning to classify the brain signal which is characterized by significant day-to-day and subject-to-subject variations and time-varying probability distributions. Due to these variabilities, we introduce the use of an adaptive probabilistic neural network (APNN) working in a time-varying environment for classification of EEG signals. The experimental evaluation on ten na茂ve subjects demonstrated that an average classification accuracy of 75.4% was obtained during the first experiment session (day) after about 3 min of online training without offline training, and 81.4% during the second session (day). The average rates during third and eighth sessions are 79.0% and 84.0%, respectively, using previously calculated classifier during the first sessions, without online training and without the need to calibrate. The results obtained from more than 5000 trials on ten subjects showed that the method could provide a robust performance over different experiment sessions and different subjects.