论文标题
基于多模式数据集，使用用户优化和稳健的BCI系统解码多级电动机相关意图
Decoding Multi-class Motor-related Intentions with User-optimized and Robust BCI System Based on Multimodal Dataset
论文作者
论文摘要
基于脑电图（EEG）的大脑计算机界面（BCI）可用于康复和控制外部设备。为电机执行（ME）和运动图像（MI）解码了五项掌握任务。在此实验中，要求八个健康受试者想象并掌握五个物体。通过在从这些ME和MI实验中获取的数据上检测到肌肉肌电图（EMG）上的肌肉信号（EMG）后，对EEG信号进行了分析。通过仅完善与用户执行电动机意图的确切时间相对应的数据，提出的方法可以仅使用由各种电动机意图生成的EEG数据训练解码模型，这些数据与特定类别具有很强的相关性。我的五个离线任务的准确度为70.73％，MI的准确度为47.95％。此方法可以应用于将来的应用，例如用BCIS控制机器人手。
A brain-computer interface (BCI) based on electroencephalography (EEG) can be useful for rehabilitation and the control of external devices. Five grasping tasks were decoded for motor execution (ME) and motor imagery (MI). During this experiment, eight healthy subjects were asked to imagine and grasp five objects. Analysis of EEG signals was performed after detecting muscle signals on electromyograms (EMG) with a time interval selection technique on data taken from these ME and MI experiments. By refining only data corresponding to the exact time when the users performed the motor intention, the proposed method can train the decoding model using only the EEG data generated by various motor intentions with strong correlation with a specific class. There was an accuracy of 70.73% for ME and 47.95% for MI for the five offline tasks. This method may be applied to future applications, such as controlling robot hands with BCIs.
