Investigation of Theta Rhythm Effect in Detection of Finger Movement.

Journal of Experimental Neuroscience Pub Date : 2019-02-19 eCollection Date: 2019-01-01 DOI:10.1177/1179069519828737
Seniha Ketenci, Temel Kayikcioglu
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引用次数: 7

Abstract

Movements cause changes in cortical rhythms emanating from the sensorimotor area. It is known that alpha and beta brainwaves take an important role of motor activity and motor imagery. Besides, theta rhythm is considered to carry substantial information about movement initiation and execution. In this study, effect of theta brainwave on movement detection was investigated in four-right handed participants who performed extensions with fingers of right hand using electroencephalography (EEG). Movement and rest epochs from continuous EEG record were extracted using muscle signals. Channels located over sensorimotor area were selected and referenced according to common average and Laplacian reference methods. Power spectral density function was used to display existence of theta band in frequency domain. To analyze theta, alpha and beta rhythms of the epochs individually and together, we filtered them to their interval range with Butterworth bandpass infinite filter before feature extraction and classification stages. Then, principal component analysis and Hjorth parameters were chosen to extract efficient features in the study aiming to investigate the effect of theta brainwaves on finger movement detection. According to classification accuracies using support vector machine classifier, alpha, beta, theta rhythms and also their different combinations were compared with each other. The performance of the epochs including alpha, beta and theta rhythms were the best and they were classified ~2% to 4% higher value in accuracy than the signals including only alpha and beta rhythms. According to this, it has proved that theta brainwave takes a role and makes contribution to motor activity.

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θ节奏效应在手指运动检测中的研究。
运动引起由感觉运动区发出的皮层节律的变化。众所周知,脑电波在运动活动和运动意象中起着重要作用。此外,θ节奏被认为携带有关运动开始和执行的大量信息。在本研究中,利用脑电图(EEG)研究了theta脑波对四右手参与者右手手指伸展运动检测的影响。利用肌肉信号提取连续脑电图记录中的运动和休息时间。选取位于感觉运动区域上方的通道,并根据共同平均法和拉普拉斯参考法进行参考。用功率谱密度函数表示频带在频域的存在性。在特征提取和分类阶段之前,我们使用Butterworth带通无限滤波器将theta, alpha和beta节律单独和一起分析到它们的区间范围。然后,采用主成分分析和Hjorth参数提取有效特征,研究θ脑波对手指运动检测的影响。根据支持向量机分类器的分类准确率,对α、β、θ节奏及其不同组合进行了比较。包含α、β和θ节奏的信号表现最好,其准确率比仅包含α和β节奏的信号高2% ~ 4%。据此,证明了θ脑波在运动活动中发挥作用并作出贡献。
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