The Relationship Between the Movement Difficulty and Brain Activity Before Arm Movements
The Relationship Between the Movement Difficulty and Brain Activity Before Arm Movements
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DOI：
10.1007/978-3-030-04239-4_47
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Tomoki Semoto;I. Nambu;Y. Wada
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文献类型：
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作者：
Tomoki Semoto;I. Nambu;Y. Wada
脑机接口（BCI）是一种可以使用大脑活动控制外部设备的技术。如果在执行运动之前能够从大脑活动中提取运动相关信息，则可以预期BCI的灵活性和安全性将得到改善。在这项研究中，我们研究了运动难度水平是否可以从脑电图（EEG）数据解码。我们进行了一项实验，其中五名参与者进行了三种不同难度的手臂伸展运动，在这些运动之前测量了大脑活动。要分类的难度水平，我们提取事件相关的频谱扰动（ERSP）的数据，并进行分类，使用相关向量机（RVM）。利用ERSP数据进行单次试验分类不能获得很高的分类精度。然而，使用平均试验ERSP数据的分类准确率平均为66.0%（每个参与者分别为53.9%、82.3%、79.6%、53.1%和61.1%）。这些结果表明，与运动难度相关的信息可能是从运动前的大脑活动中解码出来的，尽管在未来的工作中有必要提高单次试验水平的表现。
A brain-computer interface (BCI) is a technology that can control external devices using brain activity. It is expected that the flexibility and safety of a BCI will be improved if movement-related information can be extracted from brain activity before executing the movement. In this study, we examined whether movement difficulty levels can be decoded from electroencephalogram (EEG) data. We conducted an experiment where in five participants performed arm reaching movements with three different levels of difficulty, brain activity was measured before these movements. To classify the levels of difficulty, we extracted event-related spectrum perturbation (ERSP) data and performed classification using a relevance vector machine (RVM). Single-trial classification using ERSP data could not obtain high classification accuracy. However, classification accuracies using averaged-trial ERSP data were 66.0% on average (53.9%, 82.3%, 79.6%, 53.1% and 61.1% for each participant). These results show that information related to movement difficulty might be decoded from brain activity before movement, although it is necessary to improve the performance at the single-trial level in future work.