Exploring the Number of Repetitions in Trials for the Performance Convergence of Classification in Motor Imagery Task with Hand-Grasping

2019 7th International Winter Conference on Brain-Computer Interface (BCI) Pub Date : 2019-02-01 DOI:10.1109/IWW-BCI.2019.8737308

Young-Tak Kim, Seung-Bo Lee, Hakseung Kim, Ji-Hoon Jeong, Seong-Whan Lee, Dong-Joo Kim

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引用次数: 1

Abstract

Motor imagery-based brain-computer interface (BCI) has been widely used to translate user’s motor intentions in BCI applications. In general, experiment trial of motor imagery task is repeated to improve the accuracy of the motor imagery-based BCI application, but it is not well known whether the accuracy would converge from a certain number of trial repetition. This study identified that how many trials are required in the classification model for motor imagery task with hand-grasping to show reliable classification performance. Five participants equipped with an electroencephalography device were enrolled, and they were requested to perform the motor imagery tasks with hand-grasping and unfolding. Trials were classified into hand-grasping, unfolding and resting. We observed that the classification performance is converged when more than 40 trials are used in the model. This finding could be utilized to develop reliable motor imagery-based BCI application with increasing the efficiency of the experiment.

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手抓动作意象任务分类收敛的重复次数试验探讨

基于运动图像的脑机接口(BCI)在脑机接口应用中被广泛用于翻译用户的运动意图。一般情况下，为了提高基于运动图像的脑机接口应用的准确率，会反复进行运动图像任务的实验试验，但在一定次数的重复试验后，准确率是否会收敛尚不清楚。本研究确定了手抓动作意象任务的分类模型需要多少次试验才能显示出可靠的分类性能。五名参与者配备了脑电图仪，并被要求完成手抓和展开的运动想象任务。试验分为手抓、展开和休息。我们观察到，当模型中使用超过40次试验时，分类性能是收敛的。这一发现可用于开发可靠的基于运动图像的脑机接口应用，提高实验效率。

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2019 7th International Winter Conference on Brain-Computer Interface (BCI)

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