通过错误相关电位校正改善单手开合运动想象分类
Improving single-hand open/close motor imagery classification by error-related potentials correction.
作者信息
Lei Yanghao, Wang Dong, Wang Weizhen, Qu Hao, Wang Jing, Shi Bin
机构信息
Institute of Robotics and Intelligent System, School of Mechanical Engineering, Xi'an Jiaotong University, Xi' an,710049, China.
Research Institute of NRR-Neurorehabilitation Robot, Xi' an Jiaotong University, Xi' an,710049, China.
出版信息
Heliyon. 2023 Jul 20;9(8):e18452. doi: 10.1016/j.heliyon.2023.e18452. eCollection 2023 Aug.
OBJECTIVE
The ability of a brain-computer interface (BCI) to classify brain activity in electroencephalograms (EEG) during motor imagery (MI) tasks is an important performance indicator. Because the cortical regions that drive the single-handed open and closed tasks overlap, it is difficult to classify the EEG signals during executing both tasks.
APPROACH
The addition of special EEG features can improve the accuracy of classifying single-hand open and closed tasks. In this work, we designed a hybrid BCI paradigm based on error-related potentials (ErrP) and motor imagery (MI) and proposed a strategy to correct the classification results of MI by using ErrP information. The ErrP and MI features of EEG data from 11 subjects were superimposed.
MAIN RESULTS
The corrected strategy improved the classification accuracy of single-hand open/close MI tasks from 52.3% to 73.7%, an increase of approximately 21%.
SIGNIFICANCE
Our hybrid BCI paradigm improves the classification accuracy of single-hand MI by adding ErrP information, which provides a new approach for improving the classification performance of BCI.
目的
脑机接口（BCI）在运动想象（MI）任务期间对脑电图（EEG）中的脑活动进行分类的能力是一项重要的性能指标。由于驱动单手打开和闭合任务的皮层区域重叠，在执行这两项任务时对EEG信号进行分类很困难。
方法
添加特殊的EEG特征可以提高单手打开和闭合任务的分类准确性。在这项工作中，我们设计了一种基于错误相关电位（ErrP）和运动想象（MI）的混合BCI范式，并提出了一种利用ErrP信息校正MI分类结果的策略。对11名受试者的EEG数据的ErrP和MI特征进行了叠加。
主要结果
校正后的策略将单手打开/闭合MI任务的分类准确率从52.3%提高到73.7%，提高了约21%。
意义
我们的混合BCI范式通过添加ErrP信息提高了单手MI的分类准确率，为提高BCI的分类性能提供了一种新方法。
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