抓握和提升任务中利用运动前EEG进行的不依赖受试者的轨迹预测
Subject-independent trajectory prediction using pre-movement EEG during grasp and lift task
AI总结：
本文提出基于MLP与CNN-LSTM的运动前EEG解码框架，在WAY-EEG-GAL上以LOSO验证跨受试者3D抓握轨迹预测，为康复外骨骼和假肢BCI提供依据。
摘要：
脑机接口（BCI）系统可用于从头皮脑活动中解码运动学信息，以控制康复或力量增强设备。本研究利用头皮脑电图（EEG）信号，在三维（3D）空间中执行抓握和提升任务的手部运动学解码。研究使用了公开数据库WAY-EEG-GAL中的12名受试者。具体而言，提出了基于多层感知器（MLP）和卷积神经网络-长短期记忆（CNN-LSTM）的深度学习框架，这些框架利用编码在运动前EEG数据中的运动神经信息。通过将EEG数据滤波到七个频率范围，分析频谱特征以进行手部运动学解码。表现最佳的频带频谱特征被用于进一步分析不同EEG窗口大小和滞后窗口。从运动开始起设置适当的滞后窗口，使该方法在真正意义上成为运动前方法。此外，采用留一受试者法（LOSO）进行受试者间手部轨迹解码分析。Pearson相关系数和手部轨迹被用作性能指标，以评估神经解码器的解码性能。本研究探索了可能是首次仅利用伸手和抓握任务期间的EEG信号进行受试者间3D手部轨迹解码的可行性。研究结果可能为利用运动前EEG信号解码3D手部运动学提供可行信息，以用于外骨骼/外穿服和假肢等实际BCI应用。
英文摘要：
Brain-computer interface (BCI) systems can be utilized for kinematics decoding from scalp brain activation to control rehabilitation or power-augmenting devices. In this study, the hand kinematics decoding for grasp and lift task is performed in three-dimensional (3D) space using scalp electroencephalogram (EEG) signals. Twelve subjects from the publicly available database WAY-EEG-GAL, has been utilized in this study. In particular, multi-layer perceptron (MLP) and convolutional neural network-long short-term memory (CNN-LSTM) based deep learning frameworks are proposed that utilize the motor-neural information encoded in the pre-movement EEG data. Spectral features are analyzed for hand kinematics decoding using EEG data filtered in seven frequency ranges. The best performing frequency band spectral features has been considered for further analysis with different EEG window sizes and lag windows. Appropriate lag windows from movement onset, make the approach pre-movement in true sense. Additionally, inter-subject hand trajectory decoding analysis is performed using leave-one-subject-out (LOSO) approach. The Pearson correlation coefficient and hand trajectory are considered as performance metric to evaluate decoding performance for the neural decoders. This study explores the feasibility of inter-subject 3-D hand trajectory decoding using EEG signals only during reach and grasp task, probably for the first time. The results may provide the viable information to decode 3D hand kinematics using pre-movement EEG signals for practical BCI applications such as exoskeleton/exosuit and prosthesis.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
