An EEG-based subject-independent emotion recognition model using a differential-evolution-based feature selection algorithm

Kannadasan, K; Veerasingam, Sridevi; Begum, B. Shameedha; Ramasubramanian, N.

doi:10.1007/s10115-022-01762-w

Cited by 10 publications

(2 citation statements)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lokesh, S. et al [ 33 ]proposed Fractional Chimpanzee Optimization Algorithm (FrChOA) which combined the Chimpanzee Optimization Algorithm (COA) with fractional order calculus, and its classification accuracy was 88.48 % with 20 channels selected. Kannadasan K et al [ 34 ] proposed a differential-evolution-based feature selection algorithm (DEFS) to obtain an optimal feature set for effective subject-independent for emotion recognition with SVM (DEFS-SVM). It got the classification accuracies of 73.60 % and 74.23 % to detect valence arousal and valence on the DEAP dataset, and they were higher than the Particle Swarm Optimization(PSO) feature selection algorithm by 9.55 % and 9.3 %, respectively.…”

Section: Introductionmentioning

confidence: 99%

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Zhang,

Xu,

Zhang

et al. 2024

Heliyon

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Zhang,

Xu,

Zhang

et al. 2024

Heliyon

View full text Add to dashboard Cite

“…Particularly, the capability of ML to find out patterns in data makes this approach suitable for the study of the brain's activity, both in terms of emotions and affective indicators. Support Vector Machines (SVM) [23][24][25][26][27], K-Nearest Neighbor [28], Naïve Bayes [29], Linear Discriminant Analysis (LDA) [30], Quadratic Discriminant Analysis (QDA) [28], and Decision Tree (DT) [31] have been commonly used. Moreover, solutions based on ensemble learning such as Random Forest (RF) [31], Bagged Tree (BT) [32], AdaBoost [33] and Extreme Gradient Boosting [34] have been proposed in the same context for the classification of EEG processed data.…”

Section: Introductionmentioning

confidence: 99%

Exploring User Engagement in Museum Scenario with EEG—A Case Study in MAV Craftsmanship Museum in Valle d’Aosta Region, Italy

Castiblanco Jimenez,

Nonis,

Olivetti

et al. 2023

Electronics

View full text Add to dashboard Cite

In the last decade, museums and exhibitions have benefited from the advances in Virtual Reality technologies to create complementary virtual elements to the traditional visit. The aim is to make the collections more engaging, interactive, comprehensible and accessible. Also, the studies regarding users’ and visitors’ engagement suggest that the real affective state cannot be fully assessed with self-assessment techniques and that other physiological techniques, such as EEG, should be adopted to gain a more unbiased and mature understanding of their feelings. With the aim of contributing to bridging this knowledge gap, this work proposes to adopt literature EEG-based indicators (valence, arousal, engagement) to analyze the affective state of 95 visitors interacting physically or virtually (in a VR environment) with five handicraft objects belonging to the permanent collection of the Museo dell’Artigianato Valdostano di Tradizione, which is a traditional craftsmanship museum in the Valle d’Aosta region. Extreme Gradient Boosting (XGBoost) was adopted to classify the obtained engagement measures, which were labeled according to questionnaire replies. EEG analysis played a fundamental role in understanding the cognitive and emotional processes underlying immersive experiences, highlighting the potential of VR technologies in enhancing participants’ cognitive engagement. The results indicate that EEG-based indicators have common trends with self-assessment, suggesting that their use as ‘the ground truth of emotion’ is a viable option.

show abstract

Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning

Castiblanco Jimenez,

Olivetti,

Vezzetti

et al. 2024

Neural Comput & Applic

View full text Add to dashboard Cite

This study investigates the use of electroencephalography (EEG) to characterize emotions and provides insights into the consistency between self-reported and machine learning outcomes. Thirty participants engaged in five virtual reality environments designed to elicit specific emotions, while their brain activity was recorded. The participants self-assessed their ground truth emotional state in terms of Arousal and Valence through a Self-Assessment Manikin. Gradient Boosted Decision Tree was adopted as a classification algorithm to test the EEG feasibility in the characterization of emotional states. Distinctive patterns of neural activation corresponding to different levels of Valence and Arousal emerged, and a noteworthy correspondence between the outcomes of the self-assessments and the classifier suggested that EEG-based affective indicators can be successfully applied in emotional characterization, shedding light on the possibility of using them as ground truth measurements. These findings provide compelling evidence for the validity of EEG as a tool for emotion characterization and its contribution to a better understanding of emotional activation.

show abstract

An EEG-based subject-independent emotion recognition model using a differential-evolution-based feature selection algorithm

Cited by 10 publications

References 58 publications

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Optimal channel dynamic selection for Constructing lightweight Data EEG-based emotion recognition

Exploring User Engagement in Museum Scenario with EEG—A Case Study in MAV Craftsmanship Museum in Valle d’Aosta Region, Italy

Effective affective EEG-based indicators in emotion-evoking VR environments: an evidence from machine learning

Contact Info

Product

Resources

About