Maximum approximate entropy and r threshold: A new approach for regularity changes detection

Approximate entropy (ApEn) has been widely used as an estimator of regularity in many scientific fields. It has proved to be a useful tool because of its ability to distinguish different system's dynamics when there is only available short-length noisy data. Incorrect parameter selection (embedding dimension $m$, threshold $r$ and data length $N$) and the presence of noise in the signal can undermine the ApEn discrimination capacity. In this work we show that $r_{max}$ ($ApEn(m,r_{max},N)=ApEn_{max}$) can also be used as a feature to discern between dynamics. Moreover, the combined use of $ApEn_{max}$ and $r_{max}$ allows a better discrimination capacity to be accomplished, even in the presence of noise. We conducted our studies using real physiological time series and simulated signals corresponding to both low- and high-dimensional systems. When $ApEn_{max}$ is incapable of discerning between different dynamics because of the noise presence, our results suggest that $r_{max}$ provides additional information that can be useful for classification purposes. Based on cross-validation tests, we conclude that, for short length noisy signals, the joint use of $ApEn_{max}$ and $r_{max}$ can significantly decrease the misclassification rate of a linear classifier in comparison with their isolated use

A Study of Nonlinear Dynamics of EEG Responses to Simulated Unmanned Vehicle Tasks

In the contemporary world, mental workload becomes higher as technology evolves and task demand becomes overwhelming. The operators of a system are usually required to complete tasks with higher complicity within a shorter period of time. Continuous operation under a high level of mental workload can be a major source of risk and human error, thus put the operator in a hazardous working environment. Therefore, it is necessary to monitor and assess mental workload. In this study, an unmanned vehicle operation with visual detection tasks was investigated by means of nonlinear analysis of EEG time series. Nonlinear analysis is considered more advantageous compared with traditional power spectrum analysis of EEG. Besides, nonlinear analysis is more capable to capture the nature of EEG data and human performance, which is a process that subjects to constant changes. By examining the nonlinear dynamics of EEG, it is more likely to obtain a deeper understanding of brain activity. The objective of this study is to investigate the mental workload under different task levels through the examination of brain activity via nonlinear dynamics of EEG time series in simulated unmanned ground vehicle visual detection tasks. The experiment was conducted by the team lead by Dr. Lauren Reinerman Jones at Institute for Simulation & Training, University of Central Florida. One hundred and fifty subjects participated the experiment to complete four visual detection task scenarios (1) change detection, (2) threat detection task, (3) dual task with different change detection task rates, and (4) dual task with different threat detection task rates. Their EEG was recorded during performing the tasks at nine EEG channels. This study develops a massive data processing program to calculate the largest Lyapunov exponent, correlation dimension of the EEG data. This study also develops the program for performing 0-1 test on the EEG data in Python language environment. The result of this study verifies the existence of chaotic dynamics in EEG time series, reveals the change in brain activity as the effect of changing task demand in more detailed level, and obtains new insights from the psychophysiological mental workload measurement used in the preliminary study. The results of this study verified the existence of the chaotic dynamics in the EEG time series. This study also supported the hypothesis that EEG data exhibits change in the level of nonlinearity corresponding to differed task levels. The nonlinear analysis of EEG time series data is able to discriminate the change in brain activity derived from the changes in task load. All nonlinear dynamics analysis techniques used in this study is able to find the difference of nonlinearity in EEG among task levels, as well as between single task scenario and dual task scenario