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EEG-Based BCI Emotion Recognition: A Survey
by
Hernández-Álvarez, Myriam
, Torres, Edgar P.
, Torres, Edgar A.
, Yoo, Sang Guun
in
Affect (Psychology)
/ Algorithms
/ Artificial Intelligence
/ Asymmetry
/ Brain
/ Brain research
/ Brain-Computer Interfaces
/ dataset
/ Electrodes
/ Electroencephalography
/ emotion elicitation
/ emotion recognition
/ emotion representation
/ Emotions
/ feature extraction
/ feature selection
/ Humans
/ Researchers
/ Review
/ Signal Processing, Computer-Assisted
/ Trends
2020
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EEG-Based BCI Emotion Recognition: A Survey
by
Hernández-Álvarez, Myriam
, Torres, Edgar P.
, Torres, Edgar A.
, Yoo, Sang Guun
in
Affect (Psychology)
/ Algorithms
/ Artificial Intelligence
/ Asymmetry
/ Brain
/ Brain research
/ Brain-Computer Interfaces
/ dataset
/ Electrodes
/ Electroencephalography
/ emotion elicitation
/ emotion recognition
/ emotion representation
/ Emotions
/ feature extraction
/ feature selection
/ Humans
/ Researchers
/ Review
/ Signal Processing, Computer-Assisted
/ Trends
2020
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Do you wish to request the book?
EEG-Based BCI Emotion Recognition: A Survey
by
Hernández-Álvarez, Myriam
, Torres, Edgar P.
, Torres, Edgar A.
, Yoo, Sang Guun
in
Affect (Psychology)
/ Algorithms
/ Artificial Intelligence
/ Asymmetry
/ Brain
/ Brain research
/ Brain-Computer Interfaces
/ dataset
/ Electrodes
/ Electroencephalography
/ emotion elicitation
/ emotion recognition
/ emotion representation
/ Emotions
/ feature extraction
/ feature selection
/ Humans
/ Researchers
/ Review
/ Signal Processing, Computer-Assisted
/ Trends
2020
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Journal Article
EEG-Based BCI Emotion Recognition: A Survey
2020
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Overview
Affecting computing is an artificial intelligence area of study that recognizes, interprets, processes, and simulates human affects. The user’s emotional states can be sensed through electroencephalography (EEG)-based Brain Computer Interfaces (BCI) devices. Research in emotion recognition using these tools is a rapidly growing field with multiple inter-disciplinary applications. This article performs a survey of the pertinent scientific literature from 2015 to 2020. It presents trends and a comparative analysis of algorithm applications in new implementations from a computer science perspective. Our survey gives an overview of datasets, emotion elicitation methods, feature extraction and selection, classification algorithms, and performance evaluation. Lastly, we provide insights for future developments.
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