Lin Ke, Cinetto Andrea, Wang Yijun, Chen Xiaogang, Gao Shangkai, Gao Xiaorong
Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing 100084, People's Republic of China.
J Neural Eng. 2016 Apr;13(2):026020. doi: 10.1088/1741-2560/13/2/026020. Epub 2016 Feb 23.
A hybrid brain-computer interface (BCI) is a device combined with at least one other communication system that takes advantage of both parts to build a link between humans and machines. To increase the number of targets and the information transfer rate (ITR), electromyogram (EMG) and steady-state visual evoked potential (SSVEP) were combined to implement a hybrid BCI. A multi-choice selection method based on EMG was developed to enhance the system performance.
A 60-target hybrid BCI speller was built in this study. A single trial was divided into two stages: a stimulation stage and an output selection stage. In the stimulation stage, SSVEP and EMG were used together. Every stimulus flickered at its given frequency to elicit SSVEP. All of the stimuli were divided equally into four sections with the same frequency set. The frequency of each stimulus in a section was different. SSVEPs were used to discriminate targets in the same section. Different sections were classified using EMG signals from the forearm. Subjects were asked to make different number of fists according to the target section. Canonical Correlation Analysis (CCA) and mean filtering was used to classify SSVEP and EMG separately. In the output selection stage, the top two optimal choices were given. The first choice with the highest probability of an accurate classification was the default output of the system. Subjects were required to make a fist to select the second choice only if the second choice was correct.
The online results obtained from ten subjects showed that the mean accurate classification rate and ITR were 81.0% and 83.6 bits min(-1) respectively only using the first choice selection. The ITR of the hybrid system was significantly higher than the ITR of any of the two single modalities (EMG: 30.7 bits min(-1), SSVEP: 60.2 bits min(-1)). After the addition of the second choice selection and the correction task, the accurate classification rate and ITR was enhanced to 85.8% and 90.9 bit min(-1).
These results suggest that the hybrid system proposed here is suitable for practical use.
混合脑机接口(BCI)是一种与至少一种其他通信系统相结合的设备,它利用这两部分来建立人与机器之间的联系。为了增加目标数量和信息传输率(ITR),将肌电图(EMG)和稳态视觉诱发电位(SSVEP)相结合以实现混合BCI。开发了一种基于EMG的多选选择方法来提高系统性能。
本研究构建了一个60目标的混合BCI拼写器。单次试验分为两个阶段:刺激阶段和输出选择阶段。在刺激阶段,同时使用SSVEP和EMG。每个刺激以其给定频率闪烁以诱发SSVEP。所有刺激被等分为四个部分,频率设置相同。一个部分中每个刺激的频率不同。SSVEP用于区分同一部分中的目标。使用来自前臂的EMG信号对不同部分进行分类。要求受试者根据目标部分做出不同次数的握拳动作。使用典型相关分析(CCA)和均值滤波分别对SSVEP和EMG进行分类。在输出选择阶段,给出前两个最佳选择。分类准确率最高的第一个选择是系统的默认输出。仅当第二个选择正确时,受试者才需要握拳来选择第二个选择。
从10名受试者获得的在线结果表明,仅使用第一个选择时,平均准确分类率和ITR分别为81.0%和83.6比特/分钟(-1)。混合系统的ITR显著高于两种单一模式中的任何一种(EMG:30.7比特/分钟(-1),SSVEP:60.2比特/分钟(-1))。在添加第二个选择和校正任务后,准确分类率和ITR提高到85.8%和90.9比特/分钟(-1)。
这些结果表明这里提出的混合系统适用于实际应用。