Review of Studies on Emotion Recognition and Judgment Based on Physiological Signals

Abstract

People’s emotions play an important part in our daily life and can not only reflect psychological and physical states, but also play a vital role in people’s communication, cognition and decision-making. Variations in people’s emotions induced by external conditions are accompanied by variations in physiological signals that can be measured and identified. People’s psychological signals are mainly measured with electroencephalograms (EEGs), electrodermal activity (EDA), electrocardiograms (ECGs), electromyography (EMG), pulse waves, etc. EEG signals are a comprehensive embodiment of the operation of numerous neurons in the cerebral cortex and can immediately express brain activity. EDA measures the electrical features of skin through skin conductance response, skin potential, skin conductance level or skin potential response. ECG technology uses an electrocardiograph to record changes in electrical activity in each cardiac cycle of the heart from the body surface. EMG is a technique that uses electronic instruments to evaluate and record the electrical activity of muscles, which is usually referred to as myoelectric activity. EEG, EDA, ECG and EMG have been widely used to recognize and judge people’s emotions in various situations. Different physiological signals have their own characteristics and are suitable for different occasions. Therefore, a review of the research work and application of emotion recognition and judgment based on the four physiological signals mentioned above is offered. The content covers the technologies adopted, the objects of application and the effects achieved. Finally, the application scenarios for different physiological signals are compared, and issues for attention are explored to provide reference and a basis for further investigation.

Review of Studies on User Research Based on EEG and Eye Tracking

“…Due to the potential differences between electrodes, some artifacts may be generated, such as eye movement artifacts and ECG artifacts. Artifact removal methods include independent component analysis (ICA), average reference, and potential difference reference [16].…”

“…We can interpret the brain and psychological activities of users when they browse specific advertisement information on a page, including attention level, memory, and emotional response. The combination of these tools and methods combines the intuitiveness of eye-tracking data, with the depth, accuracy, and comprehensiveness of EEG data, to provide almost all of the real idea information that users use when using products and demonstrating behaviors [16]. This approach can accurately locate the brain activity of users when observing and using experiences, and explore users' attitudes, emotions, and cognitive levels deep in their hearts [17].…”

In-the-Wild Affect Analysis of Children with ASD Using Heart Rate

“…Many techniques have been proposed in the last few decades to recognize human emotions employing different modalities such as facial expressions [ 21 , 22 , 23 ], speech signals [ 24 , 25 ], and physiological signals [ 26 ]. Here, the primary objective of the application of emotion recognition is to aid either an automated system or a human-in-the-loop with regard to a participant’s emotions during interactions within some scenario (typically a learning environment).…”

Ultralow-cost piezoelectric sensor constructed by thermal compression bonding for long-term biomechanical signal monitoring in chronic mental disorders

“…30,31 Unfortunately, their reliance solely on electrophysiological signals is inaccurate due to the diversity of symptoms in psychiatric disorders. Since multiple physiological features can provide additional discriminative information, 32 it is critical to integrate multiple devices into a single system. For instance, a wearable system integrating fabric electrodes and resistive sensors is designed to diagnose bipolar disorders, which poses a high accuracy of 95.81%.…”