Valsan Gopal, Grychtol Bartlomiej, Lakany Heba, Conway Bernard A
Department of Bioengineering, University of Strath-clyde, Glasgow, UK.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:606-9. doi: 10.1109/IEMBS.2009.5333506.
Brain-computer interfaces (BCI) offer potential for individuals with a variety of motor and sensory disabilities to control their environment, communicate, and control mobility aids. However, the key to BCI usability rests in being able to extract relevant time varying signals that can be classified into usable commands in real time. This paper reports the first success of the Strathclyde BCI controlling a wheelchair on-line in Virtual Reality. Surface EEG recorded during wrist movement in two different directions were classified and used to control a wheelchair within a virtual reality environment. While Principal Component Analysis was used for feature vector quantiser distances were used for classification. Classification success rates between 68% and 77% were obtained using these relatively simple methods.
脑机接口（BCI）为患有各种运动和感觉障碍的个体提供了控制其环境、进行交流以及控制移动辅助设备的潜力。然而，BCI可用性的关键在于能够实时提取可被分类为可用命令的相关时变信号。本文报道了斯特拉斯克莱德大学的脑机接口首次成功地在虚拟现实中在线控制轮椅。在两个不同方向的手腕运动过程中记录的头皮脑电图被分类，并用于在虚拟现实环境中控制轮椅。虽然使用主成分分析进行特征提取，但使用向量量化距离进行分类。使用这些相对简单的方法获得了68%至77%的分类成功率。
