Xiong Xin, Fu Yunfa, Chen Jian, Liu Lijun, Zhang Xiabing
School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming, 650500, People's Republic of China.
Brain Topogr. 2019 Mar;32(2):240-254. doi: 10.1007/s10548-018-00696-3. Epub 2018 Dec 31.
To provide optional force and speed control parameters for brain-computer interfaces (BCIs), an effective feature extraction method of imagined force and speed of hand clenching based on electroencephalography (EEG) was explored. Twenty subjects were recruited to participate in the experiment. They were instructed to perform three different actual/imagined hand clenching force tasks (4 kg, 10 kg, and 16 kg) and three different hand clenching speed tasks (0.5 Hz, 1 Hz, and 2 Hz). Topographical maps parameters and brain network parameters of EEG were calculated as new features of imagined force and speed of hand clenching, which were classified by three classifiers: linear discrimination analysis, extreme learning machines and support vector machines. Topographical maps parameters were better for recognition of the hand clenching force task, while brain network parameters were better for recognition of the hand clenching speed task. After a combination of five types of features (energy, power spectrum of the autoregressive model, wavelet packet coefficients, topographical maps parameters and brain network parameters), the recognition rate of the hand clenching force task was 97%, and that of the hand clenching speed task was as high as 100%. The brain topographical and the brain network parameters are expected to improve the accuracy of decoding the EEG signal of imagined force and speed of hand clenching. A more efficient brain network may facilitate the recognition of force/speed of hand clenching. Combined features could significantly improve the single-trial recognition rate of imagined forces and speeds of hand clenching. The current study provides a new idea for the imagined force and speed of hand clenching that offers more control intention instructions for BCIs.
为了给脑机接口（BCI）提供可选的力和速度控制参数，探索了一种基于脑电图（EEG）的想象性手部紧握力和速度的有效特征提取方法。招募了20名受试者参与实验。他们被要求执行三种不同的实际/想象性手部紧握力任务（4千克、10千克和16千克）以及三种不同的手部紧握速度任务（0.5赫兹、1赫兹和2赫兹）。计算EEG的地形图参数和脑网络参数作为想象性手部紧握力和速度的新特征，并用三种分类器进行分类：线性判别分析、极限学习机和支持向量机。地形图参数在识别手部紧握力任务方面表现更好，而脑网络参数在识别手部紧握速度任务方面表现更好。在组合了五种类型的特征（能量、自回归模型的功率谱、小波包系数、地形图参数和脑网络参数）后，手部紧握力任务的识别率为97%，手部紧握速度任务的识别率高达100%。脑地形图和脑网络参数有望提高对想象性手部紧握力和速度的脑电信号解码的准确性。一个更高效的脑网络可能有助于识别手部紧握的力/速度。组合特征可以显著提高想象性手部紧握力和速度的单次试验识别率。当前的研究为想象性手部紧握力和速度提供了一个新的思路，为脑机接口提供了更多的控制意图指令。
