Classification of emotions using EEG activity associated with different areas of the brain

Agarwal, Rupal; Andujar, Marvin; Canavan, Shaun

doi:10.1016/j.patrec.2022.08.018

Cited by 24 publications

(5 citation statements)

References 21 publications

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“…Zhong et al [34] found that the frontal and parietal lobes were significantly more strongly activated compared to other brain regions in all frequency bands, suggesting that emotional processing is more closely correlated with these regions. Rupal et al [19] similarly concluded that emotional activity varies in different regions of the brain, and conducted a more detailed regional division of EEG signals, which showed large differences in emotion recognition results in different brain regions. To explore the correlation between frequency band features and emotion recognition, this paper conducted emotion recognition experiments on four EEG signal bands, respectively, and the results showed that Gamma band EEG features have the best emotion classification effect; the higher the frequency band, the stronger the correlation of the EEG signals with human emotions.…”

Section: Discussionmentioning

confidence: 99%

“…Khamis et al [18] utilized a multilayer perceptron (MLP) to perform emotion recognition experiments on different EEG bands, but the study did not address the application of combining multi-band EEG features for emotion recognition. Rupal et al [19] divided the brain into eight regions and used a 1D convolutional LSTM network for emotion recognition, but they only spliced the signals from different brain regions for the experiments, ignoring the different roles and importance of the different brain regions for emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Region and Multi-Band Electroencephalogram Emotion Recognition Based on Self-Attention and Capsule Network

Ke,

Ma,

et al. 2024

Applied Sciences

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Research on emotion recognition based on electroencephalogram (EEG) signals is important for human emotion detection and improvements in mental health. However, the importance of EEG signals from different brain regions and frequency bands for emotion recognition is different. For this problem, this paper proposes the Capsule–Transformer method for multi-region and multi-band EEG emotion recognition. First, the EEG features are extracted from different brain regions and frequency bands and combined into feature vectors which are input into the fully connected network for feature dimension alignment. Then, the feature vectors are inputted into the Transformer for calculating the self-attention of EEG features among different brain regions and frequency bands to obtain contextual information. Finally, utilizing capsule networks captures the intrinsic relationship between local and global features. It merges features from different brain regions and frequency bands, adaptively computing weights for each brain region and frequency band. Based on the DEAP dataset, experiments show that the Capsule–Transformer method achieves average classification accuracies of 96.75%, 96.88%, and 96.25% on the valence, arousal, and dominance dimensions, respectively. Furthermore, in emotion recognition experiments conducted on individual brain regions or frequency bands, it was observed that the frontal lobe exhibits the highest average classification accuracy, followed by the parietal, temporal, and occipital lobes. Additionally, emotion recognition performance is superior for high-frequency band EEG signals compared to low-frequency band signals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Region and Multi-Band Electroencephalogram Emotion Recognition Based on Self-Attention and Capsule Network

Ke,

Ma,

et al. 2024

Applied Sciences

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“…Observing how these spectral patterns evolve over time, in response to specific stimuli presented in the test material (in this case, a simulation video of CapitaSpring), in conjunction with insights from prior studies on EEG patterns associated with arousal-valence levels, facilitated the classification of the EEG data into six distinct emotions [67,68].…”

Section: Discussionmentioning

confidence: 99%

Spatial Cognition and Three-Dimensional Vertical Urban Design Guidelines—Cognitive Measurement and Modelling for Human Centre Design

Choi,

Bruyns,

Zhang

et al. 2023

Urban Science

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Numerous studies have shown that the emotional wellbeing of urban populations is influenced by various aspects of urban development, such as social factors and the presence of walkable areas and green spaces. However, there is a lack of research that closely integrates urban design and cognition, particularly in the context of vertical and volumetric urbanism. This disconnect between design and science disciplines is evident when reviewing the limited research on emotional and spatial cognition in this specific urban context. This paper seeks to address that disconnect by proposing a comprehensive framework for the cognitive measurement and modelling of the built environment. This will involve exploring and measuring neural mechanisms, employing electroencephalogram (EEG) equipment to measure user responses in vertical and volumetric public spaces. The aim is to create a foundation for further studies in this field that is consistent and rigorous and can facilitate collaboration with cognitive neuroscientists by establishing a shared conceptual basis. The goal of this research is to develop a human-centric approach for urban design that is scientific and measurable, producing a set of urban design guidelines that incorporate cognitive measurement and modelling, with the broader intention to prioritize human needs and wellbeing within urban environments to make them more liveable.

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“…[4] Electroencephalogram (EEG) signals are widely used in medicine, affective computing, and other related fields. [3] Using EEG signals, key emotional information can be obtained to make the judgment of anxiety more accurate, and the effect of emotional improvement can be improved by combining with virtual reality exposure therapy.…”

Section: Fig 1 a System Of Virtual Reality Exposure Therapymentioning

confidence: 99%

P‐4.9: A comprehensive study of VR exposure therapy combined with EEG signals in the treatment of psychiatric disorders

Li,

Wang

2024

Symp Digest of Tech Papers

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Virtual reality (VR) is an immersive technology that can be used to generate scenes of emotional responses. For people with psychological problems, negative emotions can be treated with the help of virtual reality exposure therapy (VRET). EEG signals can more accurately reflect the types of emotions, make up for the lack of emotion recognition, make the treatment more targeted, and the intervention treatment using virtual environment combined with the accurate recognition of emotions by EEG signals can more effectively achieve the therapeutic effect. Therefore, this paper comprehensively explores the research progress of EEG emotion recognition and exposure therapy, studies new psychotherapy regimens carried out in cooperation with therapists, and identifies some key technologies to create a more commercially effective VRET treatment.

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Classification of emotions using EEG activity associated with different areas of the brain

Cited by 24 publications

References 21 publications

Multi-Region and Multi-Band Electroencephalogram Emotion Recognition Based on Self-Attention and Capsule Network

Multi-Region and Multi-Band Electroencephalogram Emotion Recognition Based on Self-Attention and Capsule Network

Spatial Cognition and Three-Dimensional Vertical Urban Design Guidelines—Cognitive Measurement and Modelling for Human Centre Design

P‐4.9: A comprehensive study of VR exposure therapy combined with EEG signals in the treatment of psychiatric disorders

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