AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification

Muhammad Adeel Asghar; Muhammad Jamil Khan; Muhammad Rizwan; Mohammad Shorfuzzaman; Raja Majid Mehmood

doi:10.1007/s00530-021-00782-w

AI inspired EEG-based spatial feature selection method using multivariate empirical mode decomposition for emotion classification

Multimed Syst. 2022;28(4):1275-1288. doi: 10.1007/s00530-021-00782-w. Epub 2021 Apr 21.

Authors

Muhammad Adeel Asghar¹, Muhammad Jamil Khan¹, Muhammad Rizwan², Mohammad Shorfuzzaman³, Raja Majid Mehmood⁴

Affiliations

¹ Telecommunication Engineering Department, University of Engineering and Technology, Taxila, Pakistan.
² Computer Engineering Department, University of Engineering and Technology, Taxila, Pakistan.
³ Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944 Saudi Arabia.
⁴ Information and Communication Technology Department, School of Electrical and Computer Engineering, Xiamen University Malaysia, Sepang, 43900 Malaysia.

Abstract

Classification of human emotions based on electroencephalography (EEG) is a very popular topic nowadays in the provision of human health care and well-being. Fast and effective emotion recognition can play an important role in understanding a patient's emotions and in monitoring stress levels in real-time. Due to the noisy and non-linear nature of the EEG signal, it is still difficult to understand emotions and can generate large feature vectors. In this article, we have proposed an efficient spatial feature extraction and feature selection method with a short processing time. The raw EEG signal is first divided into a smaller set of eigenmode functions called (IMF) using the empirical model-based decomposition proposed in our work, known as intensive multivariate empirical mode decomposition (iMEMD). The Spatio-temporal analysis is performed with Complex Continuous Wavelet Transform (CCWT) to collect all the information in the time and frequency domains. The multiple model extraction method uses three deep neural networks (DNNs) to extract features and dissect them together to have a combined feature vector. To overcome the computational curse, we propose a method of differential entropy and mutual information, which further reduces feature size by selecting high-quality features and pooling the k-means results to produce less dimensional qualitative feature vectors. The system seems complex, but once the network is trained with this model, real-time application testing and validation with good classification performance is fast. The proposed method for selecting attributes for benchmarking is validated with two publicly available data sets, SEED, and DEAP. This method is less expensive to calculate than more modern sentiment recognition methods, provides real-time sentiment analysis, and offers good classification accuracy.

Keywords: Artificial intelligence; EEG-based emotion classification; Feature extraction; Health care; Signal processing.