Biosignal-based user-independent recognition of emotion and personality with importance weighting

Katada, Shun; Okada, Shogo

doi:10.1007/s11042-022-12711-8

Cited by 4 publications

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pusarla et al [16] used a new local mean decomposition algorithm to decompose EEG signals into product functions and even capture the underlying nonlinear characteristics of EEG; an emotion recognition system based on the presented algorithm outperformed state-of-the-art methods and achieved high accuracy. Katada and Okada [17] improved the performance of biosignal-based emotion and personality estimations by considering individual physiological differences as a covariate shift. They pointed out that importance weighting in machine learning models could reduce the effects of individual physiological differences in peripheral physiological responses.…”

Section: Introductionmentioning

confidence: 99%

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

Lin

2023

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%