ArticleCognitive neurodynamics2022
Innovative Poincare's plot asymmetry descriptors for EEG emotion recognition.
Article in Cognitive neurodynamics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- The impact of the number of regions of interest and their configurations on EEG brain-connectivity in emotion recognition.Cognitive processing · 2026Article
- EEG-based emotion recognition using phase-space reconstruction with Poincaré sections: a study on the AMIGOS dataset.Frontiers in human neuroscience · 2026Article
- EEG emotion recognition based on an innovative information potential index.Cognitive neurodynamics · 2024Article
- Lemniscate of Bernoulli's map quantifiers: innovative measures for EEG emotion recognition.Cognitive neurodynamics · 2024Article
- Improved EEG-based emotion recognition through information enhancement in connectivity feature map.Scientific reports · 2023Article
- Emotion Recognition Using a Novel Granger Causality Quantifier and Combined Electrodes of EEG.Brain sciences · 2023Article
- Verhulst map measures: new biomarkers for heart rate classification.Physical and engineering sciences in medicine · 2022Article
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Abstract
Given the importance of emotion recognition in both medical and non-medical applications, designing an automatic system has captured the attention of several scholars. Currently, EEG-based emotion recognition has a special position, which has not fulfilled the desired accuracy rates yet. This experiment intended to provide novel EEG asymmetry measures to improve emotion recognition rates. Four emotional states have been classified using the k-nearest neighbor (kNN), support vector machine, and Naïve Bayes. Feature selection has been performed, and the role of employing a different number of top-ranked features on emotion recognition rates has been assessed. To validate the efficiency of the proposed scheme, two public databases, including the SJTU Emotion EEG Dataset-IV (SEED-IV) and a Database for Emotion Analysis using Physiological signals (DEAP) were evaluated. The experimental results indicated that kNN outperformed the other classifiers with a maximum accuracy of 95.49 and 98.63% using SEED-IV and DEAP datasets, respectively. In conclusion, the results of the proposed novel EEG-asymmetry measures make the framework a superior one compared to the state-of-art EEG emotion recognition approaches.
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