Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques

Abstract

Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-ofthe-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.

Advanced Bioelectrical Signal Processing Methods: Past, Present and Future Approach—Part II: Brain Signals

“…Besides medical purposes, deep learning techniques in the analysis of EEG data can be also applied for biometrics purposes [171] or real-time IoT (Internet of Things) applications [172], or for emotions' recognition purposes [173].…”

Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

“…Emotional interaction makes the human–computer interaction more intelligent—it makes the interaction natural, cordial, vivid, emotional [ 43 ]. Because automatic emotion recognition has many applications also in HCII, it has attracted the recent hype of AI-empowered HCI research [ 44 ]. Another essential and challenging task related to emotion recognition in HCII is speech emotion recognition [ 45 ], which has become the heart of most HCI applications in the modern world [ 46 ].…”

“…Emotions, for example, can trigger some minor changes in facial blood flow with an impact on skin temperature [ 42 ] and speech [ 105 ]. EEG-based emotion recognition, for example, has become crucial in enabling the HCII [ 44 ] and has been globally accepted in many applications, such as intelligent thinking, decision-making, social communication, feeling detection, affective computing, etc. [ 106 ].…”

“…Facial expression recognition can involve using sensors, such as cameras, eye-tracker, ECG, EMG, and EEG [ 107 ]. The emotion recognition process often includes sensors for detecting physiological signals, which are not visible in the human eye and immediately reflect the emotional changes [ 44 ]. Some of the current approaches to emotion recognition based on EEG mostly rely on various handcrafted features extracted over relatively long-time windows of EEG during the participants’ exposure to appropriate affective stimuli [ 103 ].…”

Emotion identification by dynamic entropy and ensemble learning from electroencephalogram signals

“…Udhayakumar et al 16 proposed complete entropy profile to enable more reliable and enhanced information extraction from physiological signals. The findings of the experiment showed that, based on the entropy profile process, improved description and classification efficiency were obtained.…”