Open AccessSocial spider optimisation algorithm for dimension reduction of electroencephalogram signals in human emotion recognition
Author(s) -
Abdullah Yousef Al-Qammaz,
Farzana Kabir Ahmad,
Yuhanis Yusof
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.15.11373
Subject(s) - computer science , heuristics , feature selection , artificial intelligence , dimensionality reduction , valence (chemistry) , pattern recognition (psychology) , heuristic , swarm behaviour , feature (linguistics) , emotion recognition , machine learning , philosophy , physics , quantum mechanics , operating system , linguistics
Due to some limitations of current heuristics and evolutionary algorithms, this paper proposed a new swarm based algorithm for feature selection method called Social Spider Optimization (SSO-FS). In this research, SSO-FS is used in the EEG-based emotion recognition model as searching method to find optimal feature set to maximize classification performance and mimics the cooperative behaviour and mechanism of social spiders in nature. This proposed feature selection method has been tested on DEAP EEG dataset with six subjects and compared with the most popular heuristic algorithms such as GA, PSO and ABC. The results show that the SSO-FS provides a remarkable and comparable performance compared to other existing methods. Whereby, the max accuracy obtained is 66.66% and 70.83%, the mean accuracy obtained is 55.51 7.17 and 60.97 8.38 for 3-level of valence emotions and 3-level of arousal emotions classification respectively.