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

Abstract

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.

Comments for Current Interpretation EEG Alpha Activity: A Review and Analysis

“…Amount of alpha suppression in response to eyes open increases with maturation and decreases after 40 years old [23], depends on the neurohumoral status [27,49] and even eyes movement direction [50]. Decreased amount of alpha suppression in response to eyes open was demonstrated in spinal cord injured in comparison with healthy able-bodied participants [51], and in attention deficit patients [52]. Moreover increased activation magnitude is result of upper alpha stimulating neuronfeedback training [32,53].…”

Cortical Connections Between Dorsal and Ventral Visual Streams in Humans: Evidence by TMS/EEG Co-Registration

“…Consequently, in a theoretical frame, the study of brain connections by means of TMS/ EEG co-registration could bring to different conclusions when the brain is stimulated during sleep or when awake, when subjects lie in a resting state with closed eyes, or when they are performing a cognitive task. In this sense, besides the well known and diffuse change from alfa to beta activity when passing from a closed to an open eyes condition, a greater variability was reported when EEG was recorded in a closed eyes condition with respect to an open eyes condition, but it must be noted that this was evident only in the primary visual cortex (Thuraisingham et al 2007).…”

Increase in regularity and decrease in variability seen in electroencephalography (EEG) signals from alert to fatigue during a driving simulated task

“…In order that we do not count sample values corresponding to spurious high frequency noisy components, the radius r, when the CTM reaches a value 0.90 is chosen as a measure of the total variability of the system. This measure of central tendency was found to be better than the measurement based on the average length, which tends to neglect the fine structure distribution [13].…”

“…This technique measures the degree of variability seen in a second-order difference plot. A second-order plot is similar to a Poincare plot where instead of plotting successive values of the time series against each other, successive differences in the time series are plotted against each other [13]. These differences approximate to the first derivatives in a uniformly sampled time series.…”

“…For continuous data as the sampling interval decreases, the rates scale appropriately and approach the result of the continuous data. Therefore, provided the sampling interval is small, the differences will approach a scaled rate of the continuous function where the scaling factor is the sampling frequency [13].…”