首页> 外文会议>2014 IEEE Symposium on Computational Intelligence in Brain Computer Interfaces >Electroencephalographic method using fast Fourier transform overlap processing for recognition of right- or left-handed elbow flexion motor imagery
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Electroencephalographic method using fast Fourier transform overlap processing for recognition of right- or left-handed elbow flexion motor imagery

机译:使用快速傅里叶变换重叠处理的脑电图方法来识别右手或左手肘部弯曲运动图像

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Recently, systems using motor imagery (MI) have been developed as practical examples of brain-computer interface (BCI). Electroencephalography (EEG) was used to generate an electroencephalogram of elbow flexion. In addition, a method was proposed to extract the feature values that would enable the recognition right- or left-handed elbow flexion MI. In the proposed method, fast Fourier transform overlap processing was used to determine the time period required to extract feature values. In this study, the following two experiments were performed. 1) the recognition of right- or left-handed elbow flexion by analyzing only the MI time period and 2) recognition of the right- or left-handed when the MI time period was presumed. In the first experiment, right- or left-handed elbow flexion MI was processed for 20 subjects using support vector machine and the proposed method was used to extract the feature values. In the second experiment, the presumed MI time was determined using the channels in which the highest accuracy was obtained in the first experiment, and then, right- or left-handed recognition was processed for the time period presumed. In the first experiment, the recognition accuracy of the proposed method was superior to that of the previous method in 15 of 20 the subjects. In the second experiment, the mean accuracy was 7.2%. Therefore, the recognition accuracy can be improved by improving the MI detection method.
机译:最近,已经开发了使用运动图像(MI)的系统作为脑机接口(BCI)的实际示例。脑电图(EEG)用于生成肘部弯曲的脑电图。另外,提出了一种提取特征值的方法,该方法将能够识别右手或左手肘屈曲MI。在提出的方法中,使用快速傅里叶变换重叠处理来确定提取特征值所需的时间段。在这项研究中,进行了以下两个实验。 1)仅通过分析MI时间段来识别右手或左手肘部屈曲; 2)在假定MI时间段时假定是右手或左手屈曲。在第一个实验中,使用支持向量机对20位受试者进行了右手或左手肘部屈曲MI的处理,并使用提出的方法提取了特征值。在第二个实验中,使用在第一个实验中获得最高准确度的通道确定假定的MI时间,然后在假定的时间段内处理右手或左手识别。在第一个实验中,在20名受试者中的15名受试者中,该方法的识别准确度优于先前方法。在第二个实验中,平均准确度为7.2%。因此,可以通过改进MI检测方法来提高识别精度。

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