EEG-Based Emotion Recognition With Haptic Vibration by a Feature Fusion Method

Li, Dahua; Yang, Zhiyi; Hou, Fazheng; Kang, Qiaoju; Liu, Shuang; Song, Yu; Gao, Qiang; Dong, Enzeng

doi:10.1109/tim.2022.3147882

Cited by 11 publications

(10 citation statements)

References 38 publications

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“…The larger values indicate that the synergy between different brain regions is enhanced during negative emotion, which results in synchronous oscillations. This information is consistent with the study [80], [81]. The author of the study [80] found that the phase-locking value in the pleasant state is lower than in the sad state, so the state of pleasure is relatively less active in the brain area.…”

Section: Observation Of Cfm With Different Methodssupporting

confidence: 89%

“…The author of the study [80] found that the phase-locking value in the pleasant state is lower than in the sad state, so the state of pleasure is relatively less active in the brain area. The author of the study [81] found that sadness (which is a negative emotion) has more synchronous nodes and the highest synchronization. This may be because sadness evoked more memories and associations.…”

Section: Observation Of Cfm With Different Methodsmentioning

confidence: 99%

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A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

et al. 2023

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Classifying distinct human emotions, the fundamental purpose of brain-computer interface research, is essential for providing instant personalized services and assistance to individuals. With such emerging applications for individuals, several techniques have been proposed recently to explore interactions between brain regions, such as correlation, synchronization, and dependence. Notably, functional and effective connectivity methods are applied to assess the relationships between different brain areas. The primary objective of this study is to compare the frequently used functional and effective connectivity methods to recognize emotion using Electroencephalogram (EEG) signals. This paper uses a benchmark emotional EEG dataset consisting of 32 channels of EEG signals collected from 32 subjects while they were watching 40 inspirational music videos. Specifically, correlation, phase synchronization, and mutual information are used to measure functional brain connectivity, and transfer entropy is used to acquire effective brain connectivity. After extracting the features, they are represented in a two-dimensional connectivity feature map (CFM). The CFMs are then used to classify emotions by a convolutional neural network model. The results of classified emotions are analyzed regarding compatible EEG bands, accuracy, and time. Notably, the Gamma band is found as the most compatible band. The comparative study has demonstrated that though the connectivity method named Pearson correlation coefficient requires less time, the normalized mutual information is the most accurate method with advantageous detecting capability of nonlinear dependencies.

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Section: Observation Of Cfm With Different Methodssupporting

confidence: 89%

Section: Observation Of Cfm With Different Methodsmentioning

confidence: 99%

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

et al. 2023

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“…These facts open up the door for EEG as a potential low cost and portable alternative to neuroimaging techniques as far as brain mapping [ 16 ]. EEG is currently used in many applications, including medical [ 17 ], emotion recognition [ 18 ], computer–brain interface (CBI) [ 19 ], and neuromarketing [ 20 ]. Conceivably, an EEG system may be used either alone or in combination with neuroimaging tests to provide a high level of accuracy in detecting TBI.…”

Section: Brief Historical Reviewmentioning

confidence: 99%

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

Alouani

Elfouly

2022

Biomedicines

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Traumatic brain injury (TBI) can produce temporary biochemical imbalance due to leaks through cell membranes or disruption of the axoplasmic flow due to the misalignment of intracellular neurofilaments. If untreated, TBI can lead to Alzheimer’s, Parkinson’s, or total disability. Mild TBI (mTBI) accounts for about about 90 percent of all TBI cases. The detection of TBI as soon as it happens is crucial for successful treatment management. Neuroimaging-based tests provide only a structural and functional mapping of the brain with poor temporal resolution. Such tests may not detect mTBI. On the other hand, the electroencephalogram (EEG) provides good spatial resolution and excellent temporal resolution of the brain activities beside its portability and low cost. The objective of this paper is to provide clinicians and scientists with a one-stop source of information to quickly learn about the different technologies used for TBI detection, their advantages and limitations. Our research led us to conclude that even though EEG-based TBI detection is potentially a powerful technology, it is currently not able to detect the presence of a mTBI with high confidence. The focus of the paper is to review existing approaches and provide the reason for the unsuccessful state of EEG-based detection of mTBI.

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“…Therefore, the strategy that combines different kinds of stimulation materials for the purpose of enhancing the individuals' emotions is developed. Both Raheel et al [21] and Li et al [22] adopted the strategy of the tactile enhanced video to simultaneously stimulate subjects' visual, auditory, and tactile senses, to evoke subjects' dimensional or discrete emotions, respectively. Ranasinghe et al [23] developed an olfaction enhanced game that stimulate both subjects' visual, auditory, and olfactory senses to enhance their discrete emotions.…”

Section: Related Workmentioning

confidence: 99%

An Investigation of Olfactory-Enhanced Video on EEG-Based Emotion Recognition

Teng

Fan

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Collecting emotional physiological signals is significant in building affective Human-Computer Interactions (HCI). However, how to evoke subjects' emotions efficiently in EEG-related emotional experiments is still a challenge. In this work, we developed a novel experimental paradigm that allows odors dynamically participate in different stages of video-evoked emotions, to investigate the efficiency of olfactory-enhanced videos in inducing subjects' emotions; According to the period that the odors participated in, the stimuli were divided into four patterns, i.e., the olfactory-enhanced video in early/ later stimulus periods (OVEP/ OVLP), and the traditional videos in early/ later stimulus periods (TVEP/ TVLP). The differential entropy (DE) feature and four classifiers were employed to test the efficiency of emotion recognition. The best average accuracies of the OVEP, OVLP, TVEP, and TVLP were 50.54%, 51.49%, 40.22%, and 57.55%, respectively. The experimental results indicated that the OVEP significantly outperformed the TVEP on classification performance, while there was no significant difference between the OVLP and TVLP. Besides, olfactory-enhanced videos achieved higher efficiency in evoking negative emotions than traditional videos. Moreover, we found that the neural patterns in response to emotions under different stimulus methods were stable, and for Fp1, FP2, and F7, there existed significant differences in whether adopt the odors.

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EEG-Based Emotion Recognition With Haptic Vibration by a Feature Fusion Method

Cited by 11 publications

References 38 publications

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

A Comparative Study on Prominent Connectivity Features for Emotion Recognition From EEG

Traumatic Brain Injury (TBI) Detection: Past, Present, and Future

An Investigation of Olfactory-Enhanced Video on EEG-Based Emotion Recognition

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