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Electrocorticographic signals comparison in sensorimotor cortex between contralateral and ipsilateral hand movements

机译:对侧和同侧手运动之间的感觉运动皮层中的皮质电信号比较

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Brain machine interfaces (BMIs) have emerged as a technology to restore lost functionality in motor impaired patients. Most BMI systems employed neural signals from contralateral hemisphere. But many studies have also demonstrated the possibility to control hand movement using signals from ipsilateral one. However, the relationship of neural signals in sensorimotor cortex between contralateral and ipsilateral hand movement control is still unclear. In this study, the electrocorticographic signals (ECoG) of sensorimotor cortex were analyzed in two epilepsy participants when they performed a visual guided rock-scissors-paper task by using contralateral and ipsilateral hand respectively. Although typical beta suppression followed increased gamma were observed during the movements of each individual hands, the stronger responses were found in two participants when their contralateral hands were used during the task. We further extracted the power spectrum of high gamma frequency band (70-135Hz) of ECoG signals as neural features to decode the hand movements. The results showed that the classification accuracy of contralateral decoding and ipsilateral decoding were 81% and 78% for participator one (P1) and 84% and 77% for participator two (P2). The accuracy of ipsilateral decoding was only slightly lower than that of contralateral one. The hand movement information contained in ipsilateral sensorimotor cortex suggested that the ipsilateral hemisphere might be also involved in neural modulation as well as contralateral hemisphere did when performing unimanual movement, which would expand the clinical application of BMIs.
机译:脑机接口(BMI)已作为一种技术恢复,以恢复运动障碍患者的功能丧失。大多数BMI系统采用对侧半球的神经信号。但是许多研究也证明了使用同侧的信号控制手部运动的可能性。然而,对侧和同侧手运动控制之间的感觉运动皮层中的神经信号之间的关系仍然不清楚。在这项研究中,当两个癫痫参与者分别使用对侧和同侧手执行视觉引导的剪刀布纸任务时,分析了两个癫痫参与者的感觉运动皮层的电皮质信号(ECoG)。尽管在每个单独的手的运动过程中观察到典型的β抑制都伴随着伽马值的增加,但是在任务中使用两只对侧手时,在两个参与者中发现了更强的响应。我们进一步提取了ECoG信号的高伽玛频段(70-135Hz)的功率谱,作为神经特征来解码手部动作。结果表明,参与者1(P1)的对侧解码和同侧解码的分类准确度分别为81%和78%,参与者2(P2)的分类准确度分别为84%和77%。同侧解码的准确性仅比对侧解码的准确性略低。同侧感觉运动皮层中包含的手部运动信息表明,同侧半球和对侧半球在进行单手运动时也可能参与神经调节,这将扩大BMI的临床应用。

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