Shiv Nadar University, Noida, UP, India; Centre for Biomedical Engineering, Indian Institute of Technology, New Delhi, India.
Centre for Biomedical Engineering, Indian Institute of Technology, New Delhi, India; Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India.
Comput Biol Med. 2021 May;132:104350. doi: 10.1016/j.compbiomed.2021.104350. Epub 2021 Mar 21.
The present study examines a temporal relation of walking behavior during locomotion transition (walking to stair ascent) to electrooculography (EOG) signals recorded from eye movement. Further, electroencephalography (EEG) signals from the occipital region of the brain are processed to understand the relative occurrence in EOG and EEG signals during the transition. The dipole sources in the occipital region with reference to EOG detection were estimated from independent components and then clustered using the k means algorithm. The dynamics of the dipoles in the occipital cluster in different frequency bands revealed significant desynchronization in the β and low γ bands, followed by resynchronization. This transitional behavior coincided with transient features suggesting possible saccadic movement of the eyes in the EOG signal. With the data from six able-bodied participants, the desynchronization in EEG from the occipital region was detected by nearly 2.2 ± 0.5s before the transition. Using preprocessing techniques on the EOG signal followed by detecting saccades from the derivative of the EOG signal, the eye movements were detected by nearly 2.5 ± 0.5s before the transition. The EOG decoded intention of transition appeared as early as 3.0 ± 1.63s before desynchronization in the EEG. A paired t-test analysis showed that the EOG-based intent decoding of transition reflects significantly earlier than occipital EEG (p < 0.00001). This study could lead to a multi-modal neural-machine interface that may produce results superior to previous attempts involving only EEG and EMG signals.
本研究考察了在运动过渡(从行走过渡到爬楼梯)期间的行走行为与从眼球运动记录的眼动电图(EOG)信号之间的时间关系。此外,处理来自大脑枕叶的脑电图(EEG)信号,以了解在过渡期间 EOG 和 EEG 信号中的相对发生情况。使用独立成分估计了与 EOG 检测相关的枕叶区域中的偶极子源,然后使用 k 均值算法对其进行聚类。不同频带中枕叶簇中偶极子的动力学揭示了 β 和低 γ 频带中的显著去同步,随后是再同步。这种过渡行为与瞬态特征相吻合,表明 EOG 信号中可能存在眼球的扫视运动。使用来自六名健康参与者的数据,在过渡之前将近 2.2±0.5s 检测到来自枕叶区域的 EEG 去同步。通过对 EOG 信号进行预处理技术,然后从 EOG 信号的导数中检测扫视,在过渡之前将近 2.5±0.5s 检测到眼球运动。在 EEG 去同步之前,EOG 解码的过渡意图出现得最早可达 3.0±1.63s。配对 t 检验分析表明,基于 EOG 的过渡意图解码反射明显早于枕叶 EEG(p<0.00001)。这项研究可能导致一种多模态神经机器接口,其结果可能优于仅涉及 EEG 和 EMG 信号的先前尝试。