論文筆記——An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network（10年被引用66次）
題目：利用自適應概率網絡設計一種在線腦機接口樓方法控制手部抓握
概要：這篇文章提出了一種新的腦機接口方法，控制手部，系列手部抓握動作和張開在虛擬現實環境中。這篇文章希望在現實生活中利用腦機接口技術控制抓握。BCI研究的一個難點是被試者訓練問題。現在，大多數方法采用的離線的無反饋訓練
我們研究了被試者在進行運動想象時候，是否能夠在沒有離線訓練而直接就在線訓練中取得良好的表現。
另外一個重要的話題是設計在線BCI系統，機器學習的方法分類以不同天數標記的大腦信號。
設計了概率神經網絡
只在線訓練了三分鐘，第一天的分類率就達到了79.0%，第二天的分類率達到了84.0%，而且只是用第一天的分類模型，沒有做其他調整。
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.
論文筆記——An online EEG-based brain-computer interface for controlling hand grasp using an adaptive probabilistic neural network（10年被引用66次）