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EEG-based golf putt outcome prediction using support vector machine

机译:使用支持向量机的基于EEG的高尔夫推杆结果预测

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In this paper, a method is proposed to predict the putt outcomes of golfers based on their electroencephalogram (EEG) signals recorded before the impact between the putter and the ball. This method can be used into a brain-computer interface system that encourages golfers for putting when their EEG patterns show that they are ready. In the proposed method, multi-channel EEG trials of a golfer are collected from the electrodes placed at different scalp locations in one particular second when she/he concentrates on putting preparation. The EEG trials are used to predict two possible outcomes: successful or failed putts. This binary classification is performed by the support vector machine (SVM). Based on the collected time-domain EEG signals, the spectral coherences from 22-pair electrodes are calculated and then used as the feature and input for the SVM algorithm. Our experimental results show that the proposed method using EEG coherence significantly outperforms the SVM with other popular features such as power spectral density (PSD), average PSD, power, and average spectral coherence.
机译:在本文中,提出了一种基于高尔夫球手在击球和击球之前记录的脑电图(EEG)信号预测高尔夫球手推杆结果的方法。该方法可以用于脑机接口系统,该系统在高尔夫球手的EEG模式显示准备就绪时鼓励高尔夫球手进行推杆练习。在提出的方法中,当高尔夫球员专注于推杆准备工作时,会在一秒钟内从放置在头皮上不同位置的电极收集高尔夫球手的多通道EEG试验。脑电图试验用于预测两种可能的结果:推杆成功或失败。此二进制分类由支持向量机(SVM)执行。根据收集的时域EEG信号,计算22对电极的光谱相干性,然后将其用作特征和SVM算法的输入。我们的实验结果表明,所提出的使用EEG相干性的方法明显优于SVM,并具有其他流行的功能,例如功率谱密度(PSD),平均PSD,功率和平均谱相干性。

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