Approximate entropy as an indicator of non-linearity in self paced voluntary finger movement EEG

This study investigates the indications of non-linear dynamic structures in electroencephalogram signals. The iterative amplitude adjusted surrogate data method along with seven non-linear test statistics namely the third order autocorrelation, asymmetry due to time reversal, delay vector variance method, correlation dimension, largest Lyapunov exponent, non-linear prediction error and approximate entropy has been used for analysing the EEG data obtained during self paced voluntary finger-movement. The results have demonstrated that there are clear indications of non-linearity in the EEG signals. However the rejection of the null hypothesis of non-linearity rate varied based on different parameter settings demonstrating significance of embedding dimension and time lag parameters for capturing underlying non-linear dynamics in the signals. Across non-linear test statistics, the highest degree of non-linearity was indicated by approximate entropy (APEN) feature regardless of the parameter settings

Nonlinear analysis methods for modelling of EEG and ECG signals

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A Combined Linear & Nonlinear Approach for Classification of Epileptic EEG Signals

The use of both linear autoregressive model coefficients and nonlinear measures for classification of EEG signals recorded from healthy subjects and epilepsy patients is investigated. A total of seven nonlinear measures namely the approximate entropy, largest lyapunov exponent, correlation dimension, nonlinear prediction error, hurst exponent, third order autocovariance, asymmetry due to time reversal, are used in this study. The class separability of individual and combined feature sets is measured using linear discriminant analysis (LDA) algorithm where the multiple features are selected by sequential floating forward search (SFFS) algorithm. The results have shown that the use of combined feature sets provide a better characterization of EEG signals compared to individual features

Minimising prediction error for optimal nonlinear modelling of EEG signals using genetic algorithm

Genetic algorithm (GA) is used for jointly estimating the embedding dimension and time lag parameters in order to achieve an optimal reconstruction of time series in state space. The conventional methods (false nearest neighbours and first minimum of the mutual information for estimating the embedding dimension and time lag, respectively) are also included for comparison purposes. The performance of GA and conventional parameters are tested by a one step ahead prediction modelling and estimation of dynamic invariants (i.e. approximate entropy). The results of this study indicated that the parameters selected by GA provide a better reconstruction (i.e. lower root mean square error) of EEG signals used for a Brain-Computer Interface (BCI) application. Additionally, GA based parameters are found to be computationally less intensive since both parameters are jointly optimised. In order to further illustrate the superiority of the embedding parameters estimated by GA, approximate entropy (ApEn) features using embedding parameters estimated by GA and conventional methods were computed. Next these ApEn features were used to classify the EEG signals into two classes (movement and non-movement) for BCI application. These results show that the embedding parameters estimated by GA are more appropriate than those estimated by the conventional methods for nonlinear modelling of EEG signals in state spac

Emotion Recognition Based on Spatially Smooth Spectral Features of the EEG

The primary aim of this study was to select the optimal feature subset for discrimination of three dimensions of emotions (arousal, valence, liking) from subjects using electroencephalogram (EEG) signals. The EEG signals were collected from 25 channels on 21 healthy subjects whilst they were watching movie segments with emotional content. The band power values extracted from eleven frequency bands, namely delta (0.5-3.5 Hz), theta (4-7.5 Hz), alpha (8-12 Hz), beta (13-30 Hz), gamma (30-50 Hz), low theta (4-6 Hz), high theta (6-8 Hz), low alpha (8-10 Hz), high alpha (10-12 Hz), low beta (13-18 Hz) and high beta (18-30 Hz) bands, were used as EEG features. The most discriminative features for classification of EEG feature sets were selected using sequential floating forward search (SFFS) algorithm and a modified version of SFFS algorithm, which imposes the topographical smoothness of spectral features, along with linear discriminant analysis (LDA) classifier. The best classification accuracies for three emotional dimensions were obtained for liking (72.22%) followed by arousal (67.50%) and valence (66.67%). SFFS-LDA and modified SFFS-LDA algorithms produced slightly different classification accuracies. However, the findings suggested that the use of modified SFFS-LDA algorithm provides more robust feature subsets for understanding of underlying functional neuroanatomic mechanisms corresponding to distinct emotional states

On the complexity and energy analyses in EEG between alcoholic and control subjects during delayed matching to sample paradigm

In this study, we have investigated the electrophysiological differences between alcoholic and control subjects using two different approaches namely complexity and energy (power) analyses. The electroencephalogram data used in this study were recorded from 77 alcoholic and 44 control subjects while the subjects were performing delayed matching to sample object recognition task for three types of stimuli. These were a single stimulus and a second matching or nonmatching stimulus that followed the single stimulus after a delay. The experimental paradigm evokes object recognition, visual short-term memory, and decision-making abilities. The results indicated that all regions (i.e. frontal, central, temporal, parietal, and occipital) in the brain exhibit more complexity and less energy for alcoholic subjects as compared to controls. When different visual stimuli pairs were compared among alcoholic and control subjects, the results from energy analysis showed groupwise differences in occipital and parietal regions. These results provide a strong indication on the impairment in brain's electrophysiological activity for alcoholic subjects due to a history of long-term alcohol abuse. © 2008 Imperial College Press