Effective EEG Channels for Emotion Identification over the Brain Regions using Differential Evolution Algorithm

Al-Qazzaz, Noor Kamal; Sabir, Mohannad K.; Ali, Sawal Hamid Bin Mohd; Ahmad, Siti Anom; Grammer, Karl

doi:10.1109/embc.2019.8856854

Cited by 22 publications

(11 citation statements)

References 21 publications

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“…Emotional changes would be elicited using different physiological signals such as galvanic skin response (GSR) [15], electrodermal activity (EDA) [17], blood volume pressure (BVP) [18], and skin temperature (ST) [19], evoked potentials (EP) [20], electrocardiogram (ECG) [21], electromyogram (EMG) [22], and electroencephalogram (EEG) [23][24][25][26][27][28][29][30]. Clinically, EEG signals have been widely used as useful indicators of different mental states such as epilepsy, Alzheimer's disease (AD) and vascular dementia (VaD) [31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

“…Studies on EEG signal processing have been conducted to identify the brain activity patterns involved in cognitive science, neuropsychological research, clinical assessments, and consciousness research [42][43][44][45][46][47]. Recently, EEG has been widely used to assess and evaluate the human emotional states with excellent time resolution [3,15,[28][29][30]48]. EEG can provide useful information of emotional states that have been described as a potential biomarker to evaluate different emotional responses from multi-channel EEG datasets over the brain regions [38].…”

Section: Introductionmentioning

confidence: 99%

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Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Al-Qazzaz

Sabir

Ali

et al. 2019

Sensors

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Identifying emotions has become essential for comprehending varied human behavior during our daily lives. The electroencephalogram (EEG) has been adopted for eliciting information in terms of waveform distribution over the scalp. The rationale behind this work is twofold. First, it aims to propose spectral, entropy and temporal biomarkers for emotion identification. Second, it aims to integrate the spectral, entropy and temporal biomarkers as a means of developing spectro-spatial ( S S ) , entropy-spatial ( E S ) and temporo-spatial ( T S ) emotional profiles over the brain regions. The EEGs of 40 healthy volunteer students from the University of Vienna were recorded while they viewed seven brief emotional video clips. Features using spectral analysis, entropy method and temporal feature were computed. Three stages of two-way analysis of variance (ANOVA) were undertaken so as to identify the emotional biomarkers and Pearson’s correlations were employed to determine the optimal explanatory profiles for emotional detection. The results evidence that the combination of applied spectral, entropy and temporal sets of features may provide and convey reliable biomarkers for identifying S S , E S and T S profiles relating to different emotional states over the brain areas. EEG biomarkers and profiles enable more comprehensive insights into various human behavior effects as an intervention on the brain.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Al-Qazzaz

Sabir

Ali

et al. 2019

Sensors

Self Cite

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“…The idea of the differential entropy is the compartment and all functions caused by a specific features subset [ 49 , 50 ]. By implementing this uncertainty measure, plenty of useful channels can be attained significantly.…”

Section: Methodsmentioning

confidence: 99%

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Asghar

Khan

Rizwan

et al. 2020

Sensors

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Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.

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“…Interestingly, shifting effect after filtering the signal has been involved in this approach. One of the important features of SG filter, it offers advantages of preserving features of a time series that includes its relative minima and maxima, which indicate a highly significant concern relating to segmentation of a signal such as EEG [22][23][24][25].…”

Section: Denoising Stagementioning

confidence: 99%

Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children

Jaafer¹

2021

alkej

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The aim of the present study was to distinguish between healthy children and those with epilepsy by electroencephalography (EEG). Two biomarkers including Hurst exponents (H) and Tsallis entropy (TE) were used to investigate the background activity of EEG of 10 healthy children and 10 with epilepsy. EEG artifacts were removed using Savitzky-Golay (SG) filter. As it hypothesize, there was a significant changes in irregularity and complexity in epileptic EEG in comparison with healthy control subjects using t-test (p< 0.05). The increasing in complexity changes were observed in H and TE results of epileptic subjects make them suggested EEG biomarker associated with epilepsy and a reliable tool for detection and identification of this disease in children. Generally, this study aims to provide an evident of a reliable biomarker to detect epilepsy in children to give them an additional chance to live a high quality life.

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Effective EEG Channels for Emotion Identification over the Brain Regions using Differential Evolution Algorithm

Cited by 22 publications

References 21 publications

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

Electroencephalogram Profiles for Emotion Identification over the Brain Regions Using Spectral, Entropy and Temporal Biomarkers

An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering

Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children

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