Amrita Mind Brain Center, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India.
Department of Electronics and Communication Engineering, School of Engineering, Amrita Vishwa Vidyapeetham, Kollam, Kerala, India.
PLoS One. 2022 Jun 23;17(6):e0270366. doi: 10.1371/journal.pone.0270366. eCollection 2022.
For brain-computer interfaces, resolving the differences between pre-movement and movement requires decoding neural ensemble activity in the motor cortex's functional regions and behavioural patterns. Here, we explored the underlying neural activity and mechanisms concerning a grasped motor task by recording electroencephalography (EEG) signals during the execution of hand movements in healthy subjects. The grasped movement included different tasks; reaching the target, grasping the target, lifting the object upwards, and moving the object in the left or right directions. 163 trials of EEG data were acquired from 30 healthy participants who performed the grasped movement tasks. Rhythmic EEG activity was analysed during the premovement (alert task) condition and compared against grasped movement tasks while the arm was moved towards the left or right directions. The short positive to negative deflection that initiated around -0.5ms as a wave before the onset of movement cue can be used as a potential biomarker to differentiate movement initiation and movement. A rebound increment of 14% of beta oscillations and 26% gamma oscillations in the central regions was observed and could be used to distinguish pre-movement and grasped movement tasks. Comparing movement initiation to grasp showed a decrease of 10% in beta oscillations and 13% in gamma oscillations, and there was a rebound increment 4% beta and 3% gamma from grasp to grasped movement. We also investigated the combination MRCPs and spectral estimates of α, β, and γ oscillations as features for machine learning classifiers that could categorize movement conditions. Support vector machines with 3rd order polynomial kernel yielded 70% accuracy. Pruning the ranked features to 5 leaf nodes reduced the error rate by 16%. For decoding grasped movement and in the context of BCI applications, this study identifies potential biomarkers, including the spatio-temporal characteristics of MRCPs, spectral information, and choice of classifiers for optimally distinguishing initiation and grasped movement.
对于脑机接口，要解决运动前和运动之间的差异，需要解码运动皮层功能区域的神经集合活动和行为模式。在这里，我们通过记录健康受试者执行手部运动时的脑电图 (EEG) 信号，探索了与抓握运动任务相关的潜在神经活动和机制。抓握运动包括不同的任务；到达目标、抓住目标、向上提起物体以及将物体向左或向右移动。从 30 名执行抓握运动任务的健康参与者中采集了 163 次 EEG 数据。在运动前（警觉任务）条件下分析节律性 EEG 活动，并与手臂向左或向右移动时的抓握运动任务进行比较。在运动提示开始前约 -0.5ms 作为波开始时出现的短暂正到负的偏移可以用作区分运动启动和运动的潜在生物标志物。在中央区域观察到β 振荡和γ 振荡分别增加 14%和 26%，可以用来区分运动前和抓握运动任务。将运动启动与抓握进行比较显示β 振荡减少 10%，γ 振荡减少 13%，从抓握到抓握运动的反弹增量为 4%β和 3%γ。我们还研究了 MRCPs 的组合和α、β和γ 振荡的谱估计作为可以对运动条件进行分类的机器学习分类器的特征。三阶多项式核支持向量机的准确率为 70%。将排名特征修剪到 5 个叶节点可将错误率降低 16%。对于解码抓握运动和在 BCI 应用的背景下，这项研究确定了潜在的生物标志物，包括 MRCPs 的时空特征、谱信息以及为最佳区分启动和抓握运动而选择的分类器。
