EEG-based affective computing in virtual reality with a balancing of the computational efficiency and recognition accuracy

Pei, Guanxiong; Shang, Qian; Hua, Shizhen; Li, Taihao; Jin, Jia

doi:10.1016/j.chb.2023.108085

Cited by 3 publications

(1 citation statement)

References 60 publications

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“…Human action recognition is an important aspect in the creation of artificially intelligent machines that better comprehend and interact with the natural world. Ongoing research in this topic tries to solve www.ijacsa.thesai.org issues relating to immediate analysis, scalability, and reliability [10] [11] [12].…”

Section: Introductionmentioning

confidence: 99%

Advancing Human Action Recognition and Medical Image Segmentation using GRU Networks with V-Net Architecture

Rao,

Bhagyaraju

et al. 2024

IJACSA

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Section: Introductionmentioning

confidence: 99%

Advancing Human Action Recognition and Medical Image Segmentation using GRU Networks with V-Net Architecture

Rao,

Bhagyaraju

et al. 2024

IJACSA

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EmoSynth Real Time Emotion-Driven Sound Texture Synthesis via Brain-Computer Interface

Colafiglio,

Lofù,

Sorino

et al. 2024

Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization

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The Impact of Light Conditions on Neural Affect Classification: A Deep Learning Approach

Zentner,

Barradas Chacon,

Wriessnegger

2024

MAKE

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Understanding and detecting human emotions is crucial for enhancing mental health, cognitive performance and human–computer interactions. This field in affective computing is relatively unexplored, and gaining knowledge about which external factors impact emotions could enhance communication between users and machines. Furthermore, it could also help us to manage affective disorders or understand affective physiological responses to human spatial and digital environments. The main objective of the current study was to investigate the influence of external stimulation, specifically the influence of different light conditions, on brain activity while observing affect-eliciting pictures and their classification. In this context, a multichannel electroencephalography (EEG) was recorded in 30 participants as they observed images from the Nencki Affective Picture System (NAPS) database in an art-gallery-style Virtual Reality (VR) environment. The elicited affect states were classified into three affect classes within the two-dimensional valence–arousal plane. Valence (positive/negative) and arousal (high/low) values were reported by participants on continuous scales. The experiment was conducted in two experimental conditions: a warm light condition and a cold light condition. Thus, three classification tasks arose with regard to the recorded brain data: classification of an affect state within a warm-light condition, classification of an affect state within a cold light condition, and warm light vs. cold light classification during observation of affect-eliciting images. For all classification tasks, Linear Discriminant Analysis, a Spatial Filter Model, a Convolutional Neural Network, the EEGNet, and the SincNet were compared. The EEGNet architecture performed best in all tasks. It could significantly classify three affect states with 43.12% accuracy under the influence of warm light. Under the influence of cold light, no model could achieve significant results. The classification between visual stimulus with warm light vs. cold light could be classified significantly with 76.65% accuracy from the EEGNet, well above any other machine learning or deep learning model. No significant differences could be detected between affect recognition in different light conditions, but the results point towards the advantage of gradient-based learning methods for data-driven experimental designs for the problem of affect decoding from EEG, providing modern tools for affective computing in digital spaces. Moreover, the ability to discern externally driven affective states through deep learning not only advances our understanding of the human mind but also opens avenues for developing innovative therapeutic interventions and improving human–computer interaction.

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EEG-based affective computing in virtual reality with a balancing of the computational efficiency and recognition accuracy

Cited by 3 publications

References 60 publications

Advancing Human Action Recognition and Medical Image Segmentation using GRU Networks with V-Net Architecture

Advancing Human Action Recognition and Medical Image Segmentation using GRU Networks with V-Net Architecture

EmoSynth Real Time Emotion-Driven Sound Texture Synthesis via Brain-Computer Interface

The Impact of Light Conditions on Neural Affect Classification: A Deep Learning Approach

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