Anytime multipurpose emotion recognition from EEG data using a Liquid State Machine based framework

• Published in: Artificial intelligence in medicine Vol. 86; pp. 1 - 8
• Main Authors: Al Zoubi, Obada, Awad, Mariette, Kasabov, Nikola K.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.03.2018
• Subjects: Brain - physiology; Brain Waves; Computer Simulation; EEG; Electroencephalography - methods; Emotion recognition; Emotions; Feature extraction; Humans; Liquid State Machine; Machine Learning; Pattern recognition; Pattern Recognition, Automated - methods; Photic Stimulation; Predictive Value of Tests; Reproducibility of Results; Signal Processing, Computer-Assisted; Time Factors; Emotion recognition; Feature extraction; Pattern recognition; Liquid State Machine; EEG; Machine learning
• ISSN: 0933-3657; 1873-2860; 1873-2860
• DOI: 10.1016/j.artmed.2018.01.001

Recent technological advances in machine learning offer the possibility of decoding complex datasets and discern latent patterns. In this study, we adopt Liquid State Machines (LSM) to recognize the emotional state of an individual based on EEG data. LSM were applied to a previously validated EEG dataset where subjects view a battery of emotional film clips and then rate their degree of emotion during each film based on valence, arousal, and liking levels. We introduce LSM as a model for an automatic feature extraction and prediction from raw EEG with potential extension to a wider range of applications. We also elaborate on how to exploit the separation property in LSM to build a multipurpose and anytime recognition framework, where we used one trained model to predict valence, arousal and liking levels at different durations of the input. Our simulations showed that the LSM-based framework achieve outstanding results in comparison with other works using different emotion prediction scenarios with cross validation.