A novel feature extraction method based on late positive potential for emotion recognition in human brain signal patterns. (July 2016)

• Record Type: Journal Article
• Authors: Mehmood, Raja Majid; Lee, Hyo Jong
• Is Part Of: Computers & electrical engineering. Volume 53(2016)
• Page Start: 444
• Page End: 457
• Publication Date: 2016-07
• Subjects: EEG pattern recognition -- Late positive potential -- EEG feature extraction -- EEG emotion recognition; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2016.04.009
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: The use of stimulation strategy may help to enhance the emotion recognition from human brain signals. The late positive potential (LPP) was analyzed in order to select the features for emotion classification. The LPP based electroencephalography (EEG) features were selected under multiple frequency bands. The emotion classification was performed by using support vector machine (SVM) and k nearest neighbors (KNN). These findings offer experimental evidence that the LPP components may be possible features for emotion recognition. Abstract: Several methods for collecting psychophysiological data from humans have been developed, including galvanic skin response (GSR), electromyography (EMG), electroencephalography (EEG), and the electrocardiogram (ECG). This paper proposes a feature extraction method for emotion recognition in EEG-based human brain signals. In this research, emotions were elicited from subjects using emotion-related stimuli from the International Affective Picture System (IAPS) database. We selected four kinds of emotional stimuli in the arousal-valence domain. Raw brain signals were preprocessed using independent component analysis (ICA) to remove artifacts. We introduced a feature extraction method using LPP, and implemented a benchmark based on statistical and frequency domain features. The LPP-based results show the highest accuracy when using SVM in the all-selected feature set. The results also provide evidence and suggest a way for further developing a more specialized emotion recognition system using brain signals . Graphical abstract: