EEG-based emotion recognition using tunable Q wavelet transform and rotation forest ensemble classifier. (July 2021)

• Record Type: Journal Article
• Authors: Subasi, Abdulhamit; Tuncer, Turker; Dogan, Sengul; Tanko, Dahiru; Sakoglu, Unal
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: Electroencephalogram (EEG) -- Emotion recognition (ER) -- Tunable Q wavelet transform (TQWT) -- Machine learning -- Rotation forest; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102648
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: Emotion recognition by artificial intelligence (AI) is a challenging task. This paper presents a new EEG-based automated emotion recognition framework. Multi scale principal component analysis (MSPCA) is utilized for noise reduction. A tunable Q wavelet transform (TQWT) is utilized as feature extractor. Rotation Forest Ensemble classifier is used for classification. Abstract: Emotion recognition by artificial intelligence (AI) is a challenging task. A wide variety of research has been done, which demonstrated the utility of audio, imagery, and electroencephalography (EEG) data for automatic emotion recognition. This paper presents a new automated emotion recognition framework, which utilizes electroencephalography (EEG) signals. The proposed method is lightweight, and it consists of four major phases, which include: a reprocessing phase, a feature extraction phase, a feature dimension reduction phase, and a classification phase. A discrete wavelet transforms (DWT) based noise reduction method, which is hereby named multi scale principal component analysis (MSPCA), is utilized during the pre-processing phase, where a Symlets-4 filter is utilized for noise reduction. A tunable Q wavelet transform (TQWT) is utilized as feature extractor. Six different statistical methods are used for dimension reduction. In the classification step, rotation forest ensemble (RFE) classifier is utilized with different classification algorithms such as k-Nearest Neighbor (k-NN), support vector machine (SVM), artificial neural network (ANN), random forest (RF), and four different types of the decision tree (DT) algorithms. The proposed framework achieves over 93 % classification accuracy with RFE + SVM. The results clearly show that the proposed TQWT and RFE based emotion recognition framework is an effective approach for emotion recognition using EEG signals.