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Analysis of eyes open, eye closed EEG signals using second-order difference plot.

机译:使用二阶差分图分析睁眼,闭眼EEG信号。

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摘要

An assistive technology developed for "hands free" control of electrical devices to be used by severely impaired people within their environment, relies upon using signal processing techniques for analyzing eyes closed (EC) and eyes open (EO) states in the electroencephalography (EEG) signal. Here, we apply a signal processing technique used in continuous chaotic modeling to investigate differences in the EEG time series between EC and EO states. This method is used to detect the degree of variability from a second-order difference plot, and quantifying this using a central tendency measures. The study used EEG time series of EO and EC states from 33 able-bodied and 17 spinal cord injured participants. The results found an increased EEG variability in brain activity during EC compared to EO. This increased EEG variability occurred in the O2 electrode, which overlays the primary visual cortex V1, and could be a result of the replacement of the coherent information obtained during EO by noise. A continuous measure of the variability was then used to demonstrate that this technique has the potential to be used as a switching mechanism for assistive technologies.
机译:为“免提”控制电气设备而开发的辅助技术,供严重受损的人在其环境中使用,它依赖于使用信号处理技术来分析脑电图(EEG)中的闭眼(EC)和睁眼(EO)状态信号。在这里,我们应用在连续混沌建模中使用的信号处理技术来研究EC状态和EO状态之间的EEG时间序列的差异。该方法用于从二阶差异图检测变化程度,并使用集中趋势量度对其进行量化。该研究使用了来自33位身体健全和17位脊髓受伤的参与者的EO和EC状态的EEG时间序列。结果发现,与EO相比,EC期间脑电活动的EEG变异性增加。这种增加的脑电图变异性发生在O2电极上,该电极覆盖了主要的视觉皮层V1,并且可能是在EO期间用噪声代替了相干信息的结果。然后使用可变性的连续度量来证明该技术具有用作辅助技术的转换机制的潜力。

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