Thought identification through visual stimuli presentation from a commercially available EEG device

M. P. A. V. Gunawardhana, C. Jayatissa, J. A. Seneviratne
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Abstract

Thought identification has been the ultimate goal of brain-computer interface systems. However, due to the complex nature of brain signals, classification is difficult. But recent developments in deep learning have made the classification of multivariate time series data relatively easy. Studies have been carried out in the recent past to classify thoughts based on signals from medical-grade EEG devices. This study explores the possibility of thought identification using a commercially available EEG device using deep learning techniques. The crucial part of any EEG experiment is contamination-free data collection. Keeping the subject's mind concentrated only in the decided state is important, yet challenging. To address this issue, we have developed a graphical user interface (GUI) based program that allows stimulus controlling and data recording. With the use of the low-cost commercially available EEG device, accuracies up to 89% were achieved for the classification of high contrast signals. However, tests on complex thought identification did not produce statistically significant results over the chance accuracy.
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从市售的脑电图设备通过视觉刺激呈现思想识别
思想识别一直是脑机接口系统的终极目标。然而,由于大脑信号的复杂性,分类是困难的。但是深度学习的最新发展使得多元时间序列数据的分类变得相对容易。最近已经开展了基于医疗级脑电图设备的信号对思想进行分类的研究。本研究探索了利用深度学习技术的商用EEG设备进行思想识别的可能性。任何脑电图实验的关键部分都是无污染的数据收集。保持主体的思想只集中在决定的状态是很重要的,但具有挑战性。为了解决这个问题,我们开发了一个基于图形用户界面(GUI)的程序,允许刺激控制和数据记录。通过使用低成本的商用EEG设备,对高对比度信号的分类准确率达到89%。然而,对复杂思维识别的测试并没有产生统计上显著的结果。
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