Correlation Analysis of Emotional EEG In Alpha, Beta and Gamma Frequency Bands

Choong, Wen Yean; Khairunizam, Wan; Mustafa, Wan Azani; Murugappan, M.; Hamid, Abdul; Bong, Siao Zheng; Yuvaraj, Rajamanickam; Omar, Mohammad Iqbal; Junoh, Ahmad Kadri; Ali, Hasimah; Razlan, Z. M.; Shahriman, A. B.

doi:10.1088/1742-6596/1997/1/012029

Cited by 12 publications

(5 citation statements)

References 12 publications

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“…The simulation that was designed (Figure 8) was expected to stimulate the drivers' emotions by combining the visual (from the display on the HMD) and audio (the surrounding sound) as close as possible to the actual environment. The classification rate of the designed system is expected to achieve more than 80% of emotion classification accuracies [18,23]. The emotion classification of the proposed method only focuses on several emotions (relax, fear, surprise, focus, and nervous).…”

Section: Expected Resultsmentioning

confidence: 99%

Development of Driving Simulation Experiment Protocol for the Study of Drivers’ Emotions by using EEG Signal

Abdul Hafiz Abd Halin,

Wan Khairunizam Wan Ahmad,

Wan Azani Mustafa

et al. 2024

ARAM

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The Brain-Computer Interface (BCI) is a field of research that studies the EEG signal in order to elevate our understanding of the human brain. The applications of BCI are not limited to the study of the brain wave but also include its applications. The studies of human emotions specific to the vehicle driver are limited and not vastly explored. The EEG signal is used in this study to classify the emotions of drivers. This research aims to study the emotion classifications (surprise, relax/neutral, focus, fear, and nervousness) while driving the simulated vehicle by analyse the EEG signals. The experiments were conducted in 2 conditions, autonomous and manual drive in the simulated environment. In autonomous driving, vehicle control is disabled. While in manual drive, the subjects are able to control the steering angle, acceleration, and brake pedal. During the experiments, the EEG data of the subjects is recorded and then analyzed.

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Section: Expected Resultsmentioning

confidence: 99%

Development of Driving Simulation Experiment Protocol for the Study of Drivers’ Emotions by using EEG Signal

Abdul Hafiz Abd Halin,

Wan Khairunizam Wan Ahmad,

Wan Azani Mustafa

et al. 2024

ARAM

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“…Choong et al 38 computed Pearson correlation coefficients for pairs of electrodes by utilizing the Emotiv EPOC in the traditional 2D EEG configuration in the context of EEG data representing emotional states and obtained correlation coefficients as high as 0.8142 in the alpha band. Here, we have adapted the Emotiv EPOC to our three-dimensional multilayer sensor ensemble and obtained correlation coefficients for pairs of sensors ranging from 0.062689 for Subject C to 0.3337 for Subject B.…”

Section: Resultsmentioning

confidence: 99%

A Novel Three-dimensional Multilayer Electroencephalography Paradigm

Ekpar¹

2023

Preprint

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Contemporary electroencephalography systems operate on a two-dimensional single-layer paradigm where signals from multiple layers of neuronal populations under an electrode are aggregated and recorded by that single electrode, leading to noisy signals and a lack of insight into neurological processes and keeping brain-to-brain communication, practical brain-computer interfaces and a host of applications in domains ranging from medicine to computing out of reach. Here, we introduce a novel three-dimensional multilayer electroencephalography (3D Multilayer EEG) paradigm – unlike the contemporary single-layer or two-dimensional (2D Single-layer EEG) paradigm – that leverages a nature-inspired conceptual framework in which approximations to carefully selected features of the source of the bio-signals are harnessed for characterization and manipulation of the underlying biological system. Effected through the simultaneous capture of distinct signal streams from multiple layers of neurons, this novel multilayer EEG paradigm could lead to effective computer-mediated brain-to-brain communication systems, a clearer understanding of neurological processes both in normal functioning and in disease as well as several orders of magnitude improvements in the information transfer rate in brain-computer interface systems – making these systems practical – as well as enabling a broad range of novel applications in domains ranging from medicine to social interactions, human factors including workplace optimization, economics, generic computing and human-machine interactions. Recent work demonstrating the direct imaging of signals propagating through myelinated axons and direct evidence that scalp EEG recordings can detect subcortical electrophysiological activity confirms the correctness of the principles underpinning our framework. We demonstrate the effectiveness of our novel 3D Multilayer EEG paradigm by formulating the null and alternative hypotheses for simultaneous multilayer EEG signal capture and relying on the results of analysis of a set of carefully designed experimental measurements to falsify the null hypothesis and validate the alternative hypothesis.

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“…Second, we adjust the magnitude of weights, since in real data, Pearson correlation coefficients are not particularly large. We reduce the correlation coefficients in the simulations by multiplying by 0.6 50 , resulting in values in [0, 0.6]. Third, the connectivity matrix is symmetrized: .…”

Section: Simulation Resultsmentioning

confidence: 99%

Temporal segmentation of EEG based on functional connectivity network structure

Xu,

Tang,

Liu

et al. 2023

Sci Rep

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In the study of brain functional connectivity networks, it is assumed that a network is built from a data window in which activity is stationary. However, brain activity is non-stationary over sufficiently large time periods. Addressing the analysis electroencephalograph (EEG) data, we propose a data segmentation method based on functional connectivity network structure. The goal of segmentation is to ensure that within a window of analysis, there is similar network structure. We designed an intuitive and flexible graph distance measure to quantify the difference in network structure between two analysis windows. This measure is modular: a variety of node importance indices can be plugged into it. We use a reference window versus sliding window comparison approach to detect changes, as indicated by outliers in the distribution of graph distance values. Performance of our segmentation method was tested in simulated EEG data and real EEG data from a drone piloting experiment (using correlation or phase-locking value as the functional connectivity strength metric). We compared our method under various node importance measures and against matrix-based dissimilarity metrics that use singular value decomposition on the connectivity matrix. The results show the graph distance approach worked better than matrix-based approaches; graph distance based on partial node centrality was most sensitive to network structural changes, especially when connectivity matrix values change little. The proposed method provides EEG data segmentation tailored for detecting changes in terms of functional connectivity networks. Our study provides a new perspective on EEG segmentation, one that is based on functional connectivity network structure differences.

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Correlation Analysis of Emotional EEG In Alpha, Beta and Gamma Frequency Bands

Cited by 12 publications

References 12 publications

Development of Driving Simulation Experiment Protocol for the Study of Drivers’ Emotions by using EEG Signal

Development of Driving Simulation Experiment Protocol for the Study of Drivers’ Emotions by using EEG Signal

A Novel Three-dimensional Multilayer Electroencephalography Paradigm

Temporal segmentation of EEG based on functional connectivity network structure

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