Cross-Subject Emotion Recognition From Multichannel EEG Signals Using Multivariate Decomposition and Ensemble Learning

• Published in: IEEE transactions on cognitive and developmental systems Vol. 17; no. 1; pp. 77 - 88
• Main Authors: Vempati, Raveendrababu, Sharma, Lakhan Dev, Tripathy, Rajesh Kumar
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.02.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Automation; Binary atom search optimization (BASO); Decomposition; electroencephalogram (EEG) signal; Electroencephalography; Emotion recognition; emotion recognition (ER); Emotions; Ensemble learning; Entropy; Feature extraction; Filters; Human-computer interface; Iterative methods; leave-one-subject-out cross validation (LOSOCV); light gradient boosting machine (LGBM); Machine learning; Multivariate analysis; multivariate fast iterative filtering (MvFIF); Permutations; Recommender systems

Emotions are mental states that determine the behavior of a person in society. Automated identification of a person's emotion is vital in different applications such as brain-computer interfaces (BCIs), recommender systems (RSs), and cognitive neuroscience. This article proposes an automated ap...