A Review on the Computational Methods for Emotional State Estimation from the Human EEG

By Min-Ki Kim, Miyoung Kim, Eunmi L. Oh, Sung-Phil Kim. Comput. Math. Methods Medicine, 2013

315 citations recorded by the source corpus.

A growing number of affective computing researches recently developed a computer system that can recognize an emotional state of the human user to establish affective human-computer interactions. Various measures have been used to estimate emotional states, including self-report, startle response, behavioral response, autonomic measurement, and neurophysiologic measurement. Among them, inferring emotional states from electroencephalography (EEG) has received considerable attention as EEG could directly reflect emotional states with relatively low costs and simplicity. Yet, EEG-based emotional state estimation requires well-designed computational methods to extract information from complex and noisy multichannel EEG data. In this paper, we review the computational methods that have been developed to deduct EEG indices of emotion, to extract emotion-related features, or to classify EEG signals into one of many emotional states. We also propose using sequential Bayesian inference to estimate the continuous emotional state in real time. We present current challenges for building an EEG-based emotion recognition system and suggest some future directions.

Subfields: Cognitive Neuroscience, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/3f3603eafc27c467551018325acba7d850928b9e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Wei-Long Zheng
2. Zhen Cui
3. Wenming Zheng
4. Bao-Liang Lu
5. Jia-Yi Zhu
6. Yuan Zong
7. Yang Li
8. Tong Zhang

In Experimental and Cognitive Psychology

1. Wei-Long Zheng
2. Zhen Cui
3. Wenming Zheng
4. Bao-Liang Lu
5. Jia-Yi Zhu
6. Yuan Zong
7. Yang Li
8. Tong Zhang

Citations in the corpus

• A Bi-Hemisphere Domain Adversarial Neural Network Model for EEG Emotion Recognition (2021, background): “For dealing with the classification problem, many EEG emotion recognition models and methods were proposed over the past several years [9], [10].”
• Identifying Stable Patterns over Time for Emotion Recognition from EEG (2016, methodology): “presented a review on the computational methods for EEGbased emotion estimation [22].”
• Emotions Recognition Using EEG Signals: A Survey (2019, methodology): “Finally, the ERP components of short (N100 and P100) to middle (N200 and P200) latencies have been shown to correlate with valence, whereas the components of middle to long (P300 and SCP) latencies have been shown to correlate with arousal [36].”