Hosseini Seyyed Moosa, Shalchyan Vahid
Neuroscience and Neuroengineering Research Lab, Department of Biomedical Engineering, School of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran.
Front Hum Neurosci. 2022 Jun 30;16:901285. doi: 10.3389/fnhum.2022.901285. eCollection 2022.
The principal goal of the brain-computer interface (BCI) is to translate brain signals into meaningful commands to control external devices or neuroprostheses to restore lost functions of patients with severe motor disabilities. The invasive recording of brain signals involves numerous health issues. Therefore, BCIs based on non-invasive recording modalities such as electroencephalography (EEG) are safer and more comfortable for the patients. The BCI requires reconstructing continuous movement parameters such as position or velocity for practical application of neuroprostheses. The BCI studies in continuous decoding have extensively relied on extracting features from the amplitude of brain signals, whereas the brain connectivity features have rarely been explored. This study aims to investigate the feasibility of using phase-based connectivity features in decoding continuous hand movements from EEG signals. To this end, the EEG data were collected from seven healthy subjects performing a 2D center-out hand movement task in four orthogonal directions. The phase-locking value (PLV) and magnitude-squared coherence (MSC) are exploited as connectivity features along with multiple linear regression (MLR) for decoding hand positions. A brute-force search approach is employed to find the best channel pairs for extracting features related to hand movements. The results reveal that the regression models based on PLV and MSC features achieve the average Pearson correlations of 0.43 ± 0.03 and 0.42 ± 0.06, respectively, between predicted and actual trajectories over all subjects. The delta and alpha band features have the most contribution in regression analysis. The results also demonstrate that both PLV and MSC decoding models lead to superior results on our data compared to two recently proposed feature extraction methods solely based on the amplitude or phase of recording signals ( < 0.05). This study verifies the ability of PLV and MSC features in the continuous decoding of hand movements with linear regression. Thus, our findings suggest that extracting features based on brain connectivity can improve the accuracy of trajectory decoder BCIs.
脑机接口（BCI）的主要目标是将脑信号转化为有意义的指令，以控制外部设备或神经假体，从而恢复重度运动功能障碍患者丧失的功能。侵入式脑信号记录涉及诸多健康问题。因此，基于脑电图（EEG）等非侵入式记录方式的BCI对患者来说更安全、更舒适。BCI需要重建连续的运动参数，如位置或速度，以便神经假体能够实际应用。在连续解码方面的BCI研究广泛依赖于从脑信号幅度中提取特征，而脑连接特征却很少被探索。本研究旨在探讨使用基于相位的连接特征从EEG信号中解码连续手部运动的可行性。为此，从7名健康受试者收集了EEG数据，这些受试者在四个正交方向上执行二维中心向外手部运动任务。锁相值（PLV）和平方相干幅值（MSC）被用作连接特征，同时结合多元线性回归（MLR）来解码手部位置。采用暴力搜索方法来寻找用于提取与手部运动相关特征的最佳通道对。结果表明，基于PLV和MSC特征的回归模型在所有受试者的预测轨迹与实际轨迹之间分别实现了0.43±0.03和0.42±0.06的平均皮尔逊相关性。δ波和α波频段特征在回归分析中贡献最大。结果还表明，与最近提出的仅基于记录信号幅度或相位的两种特征提取方法相比，PLV和MSC解码模型在我们的数据上都取得了更好的结果（P<0.05）。本研究验证了PLV和MSC特征在线性回归连续解码手部运动中的能力。因此，我们的研究结果表明，基于脑连接提取特征可以提高轨迹解码器BCI的准确性。
