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

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

doi:10.3390/s20010059

Cited by 29 publications

(18 citation statements)

References 76 publications

(96 reference statements)

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“…Therefore, recognizing emotion supports automatic interpretation of human intentions and preferences, allowing human computer interface applications to respond better to users requirements and customize interactions according to affective responses. Moreover, EEG signals have been described as a potential biomarker of gender differences from emotional-based EEG dataset with a classification accuracy of 92% [8], [11].…”

Section: Related Workmentioning

confidence: 99%

“…Recently, researchers indicated that to provide the best environment for recognition of emotion, they need to get the combined effect of both visual and auditory stimuli to elicit a specific emotional state [8]. Using short audio-visual video clips to create conditions that elicit various feelings has been found to be more effective than other modalities [9].…”

Section: Introductionmentioning

confidence: 99%

“…So far, few studies have examined gender differences based on emotional changes [10]- [12], and the greater part of these studies report significant differences [8]. However, some limitations of the EEG-based emotion recognition model have been identified.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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“…Therefore, removing power line interference noise has been performed by using notch filter. Consistently, butter worth has been utilized for the second order band pass filter ranging from 0.1 to 60 Hz in the first stage [21]. In the second stage, and in order to smooth out the signal with lowest level of destruction of its original features, Savitzky-Golay (SG) has been utilized.…”

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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“…However, most literature shows results on different kind of human sensations recognised by EEG spectral changes. Commonly, the main six basic emotions are fear, anger, repulsion, pleasure, sadness, and surprise described in a 2-dimensional space with dimensions valence and arousal (Al-Qazzaz, 2020). Thanks to the brain's capability to feel sophisticated emotions and to provide complex dynamic information that are reproduced by the cerebral cortex, different methods for the automatic detection of emotions through EEG signals have been studied and applied in literature.…”

Section: Introductionmentioning

confidence: 99%

Towards a reverse world in automation: human as a sensor

Bersani¹,

Sacile²,

Zero³

2021

Preprint

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Automation is more and more shifting towards a technologic world where the human component is however still involved. The autonomous cars represent an example where the shift towards a fully automated vehicle still requires the presence of the human component, that is a human-in-the-loop, which may represent the failure or the added value of such systems. Stress and fatigue represent the main concerns in safety issues during workloads, above all, in situations where the person’s behaviour represents a potential source of harm in human–computer interaction. After a brief survey of the recent literature on the human as a sensor concept, in this paper, some examples of an electroencephalography (EEG) based brain-computer interface showing the still limited but potentially challenging effectiveness of such an approach in systems engineering. The aim is to explore brain activities when a subject is conditioned by external stimuli during the execution of a cognitive task and to evaluate the human capability to react to the unexpected event faster than a machine.Different experiment tests have been designed to evaluate the human reactions under simulated driving scenarios and workload sessions. Accordingly, different methodologies to use this information are shown. Driver’s performance has been evaluated by EEG power spectrum analysis when unpredicted acoustic stimuli or breaking emergency situations are simulated during the driving session. A Fine Gaussian Support Vector Machine (SVM) approach has been carried out to classify the human’s brain activities when the participant has to drive a car along a challenging curvilinear path in a virtual simulated scenario or when he is exposed to visual disturbances while performing a common task.The results demonstrate an interesting potential correlation between external stimuli and driver’s brain wave activities in virtual driving environment. In addition, the EEG pattern recognition in the visual external stimulus test and in the driver’s stress simulation to tackle a road with different curve angles generated promising outcomes. However, despite the wide literature on the subject, the effective use of such signal still represents a hard although challenging task.

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

Cited by 29 publications

References 76 publications

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

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

Hurst Exponent and Tsallis Entropy Markers for Epileptic Detection from Children

Towards a reverse world in automation: human as a sensor

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