Correlation Indices of Electroencephalogram-Based Relative Powers during Human Emotion Processing

Al-Qazzaz, Noor Kamal; Sabir, Mohannad K.; Grammer, Karl

doi:10.1145/3326172.3326179

Cited by 10 publications

(10 citation statements)

References 24 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

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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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“…Investigating gender differences based on emotional changes become essential to understand various human behavior in our daily life [29]. Although, there is no common agreement on the most suitable EEG features, researches have suggested extracting features from time-domain [8], [30], [31], frequency-domain [32], time-frequency domain using wavelet transform [33]- [35] and the users of the statistical features [10] as in Table II…”

Section: A Eeg Features Extractionmentioning

confidence: 99%

“…In this study, a bandpass filter with a lower cutoff corresponding to 3 dB is 0.5 Hz and the upper cutoff frequency was selected to be 64 Hz, these conventional filters were applied to limit the frequencies of the EEG signals as in [8]. A notch band stop filter was utilized to remove the AC power line interference noise (PLIN) and it was set to the cutoff frequency of 50 Hz [8], [31].…”

Section: ) Conventional Filteringmentioning

confidence: 99%

Multichannel Optimization With Hybrid Spectral- Entropy Markers for Gender Identification Enhancement of Emotional-Based EEGs

et al. 2021

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Investigating gender differences based on emotional changes supports automatic interpretation of human intentions and preferences. This allows emotion applications to respond better to requirements and customize interactions based on affective responses. The electroencephalogram (EEG) is a tool that potentially can be used to detect gender differences. The main purpose of this paper is twofold. Firstly, it aims to use both linear and nonlinear features of EEG signals to identify emotional influences on gender behavior. Secondly, it aims to develop an automatic gender recognition model by employing optimization algorithms to identify the most effective channels for gender identification from emotional-based EEG signals. The EEGs of thirty healthy students from the University of Vienna have watched four short video clips depicting the emotions of anger, happiness, sadness and neutral. In this study, the wavelet transform (WT) de-noising technique, linear spectral mean frequency (meanF ) and nonlinear multiscale fuzzy entropy (M F E) features were used. The individual performance of these attributes was statistically examined using analysis of variance (ANOVA) to represent the gender behavior in the brain-emotion in females and males. Then, these two features were fused into a set of hybrid spectral-entropy attributes (SEA). Consequently, optimization algorithms including binary gravitation search algorithm (BGSA) and binary particle swarm optimization (BPSO), were employed to identify the optimal channels for gender classification. Finally, the k-nearest neighbors (kNN) classification technique was used for automatic gender identification of an emotional-based EEG dataset. The results show linear and nonlinear features are remarkable neuromarkers for investigating gender-based differences in emotional states. Moreover, the results show significant enhancement in the overall accuracy of classification achieved by using the BGSA optimization algorithm with the proposed hybrid SEA set when compared to individual features. Therefore, the proposed methods were effective in improving the process of automatic gender recognition from the emotional-based EEG signals.

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Correlation Indices of Electroencephalogram-Based Relative Powers during Human Emotion Processing

Cited by 10 publications

References 24 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

Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children

Multichannel Optimization With Hybrid Spectral- Entropy Markers for Gender Identification Enhancement of Emotional-Based EEGs

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