Mohamed A K, Marwala T, John L R
School of Electrical and Information Engineering, University of Witwatersrand, Johannesburg, South Africa.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:6289-93. doi: 10.1109/IEMBS.2011.6091552.
Brain-computer interface (BCI) may be used to control a prosthetic or orthotic hand using neural activity from the brain. The core of this sensorimotor BCI lies in the interpretation of the neural information extracted from electroencephalogram (EEG). It is desired to improve on the interpretation of EEG to allow people with neuromuscular disorders to perform daily activities. This paper investigates the possibility of discriminating between the EEG associated with wrist and finger movements. The EEG was recorded from test subjects as they executed and imagined five essential hand movements using both hands. Independent component analysis (ICA) and time-frequency techniques were used to extract spectral features based on event-related (de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used for feature reduction. Mahalanobis distance (MD) clustering and artificial neural networks (ANN) were used as classifiers and obtained average accuracies of 65 % and 71 % respectively. This shows that EEG discrimination between wrist and finger movements is possible. The research introduces a new combination of motor tasks to BCI research.
脑机接口（BCI）可利用大脑的神经活动来控制假肢或矫形手。这种感觉运动脑机接口的核心在于对从脑电图（EEG）中提取的神经信息进行解读。人们期望改进对脑电图的解读，以使患有神经肌肉疾病的人能够进行日常活动。本文研究了区分与手腕和手指运动相关的脑电图的可能性。在测试对象用双手执行和想象五种基本手部动作时记录他们的脑电图。使用独立成分分析（ICA）和时频技术基于事件相关（去）同步化（ERD/ERS）提取频谱特征，同时使用 Bhattacharyya 距离（BD）进行特征约简。使用马氏距离（MD）聚类和人工神经网络（ANN）作为分类器，分别获得了 65%和 71%的平均准确率。这表明区分手腕和手指运动的脑电图是可能的。该研究为脑机接口研究引入了一种新的运动任务组合。
