An unsupervised EEG decoding system for human emotion recognition

Liang, Zhen; Oba, Shigeyuki; Ishii, Shin

doi:10.1016/j.neunet.2019.04.003

Cited by 92 publications

(74 citation statements)

References 35 publications

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“…Whereas, the corresponding accuracies of valence and arousal increased to 81.41% and 73.36% respectively when the convolutional neural network was adopted. Liang et al [86] proposed an EEG‐based emotion decoding system using the hypergraph theory and verified the effectiveness of emotion recognition on DEAP database. More, it’s an unsupervised learning method for emotion‐related EEG features extraction and multi‐dimensional emotion recognition.…”

Section: Video‐triggered Emotion Recognition With Eeg Signalsmentioning

confidence: 99%

Video‐triggered EEG‐emotion public databases and current methods: A survey

Huang

et al. 2020

Brain Science Advances

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Emotions, formed in the process of perceiving external environment, directly affect human daily life, such as social interaction, work efficiency, physical wellness, and mental health. In recent decades, emotion recognition has become a promising research direction with significant application values. Taking the advantages of electroencephalogram (EEG) signals (i.e., high time resolution) and video‐based external emotion evoking (i.e., rich media information), video‐triggered emotion recognition with EEG signals has been proven as a useful tool to conduct emotion‐related studies in a laboratory environment, which provides constructive technical supports for establishing real‐time emotion interaction systems. In this paper, we will focus on video‐triggered EEG‐based emotion recognition and present a systematical introduction of the current available video‐triggered EEG‐based emotion databases with the corresponding analysis methods. First, current video‐triggered EEG databases for emotion recognition (e.g., DEAP, MAHNOB‐HCI, SEED series databases) will be presented with full details. Then, the commonly used EEG feature extraction, feature selection, and modeling methods in video‐triggered EEG‐based emotion recognition will be systematically summarized and a brief review of current situation about video‐triggered EEG‐based emotion studies will be provided. Finally, the limitations and possible prospects of the existing video‐triggered EEG‐emotion databases will be fully discussed.

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Section: Video‐triggered Emotion Recognition With Eeg Signalsmentioning

confidence: 99%

Video‐triggered EEG‐emotion public databases and current methods: A survey

Huang

et al. 2020

Brain Science Advances

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“…Unlike study 1, an important task while driving was to measure the driver’s emotional state. As an important branch of affective computing, emotion recognition patterns based on electroencephalogram (EEG) [ 50 ], electrocardiogram (ECG) [ 51 ], galvanic skin response (GSR) [ 52 ], respiration (RSP) [ 53 ], facial expression [ 54 ], and speech [ 55 ] have been the popular human emotion recognition methods. While we investigated eight kinds of driving emotions in this paper, and it seems impossible to find a measurement that effectively works for all of them with the mentioned methods.…”

Section: Study 2-driving Intention Prediction Models Adapting To Mmentioning

confidence: 99%

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

Liu

Wang

2020

IJERPH

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Joining worldwide efforts to understand the relationship between driving emotion and behavior, the current study aimed at examining the influence of emotions on driving intention transition. In Study 1, taking a car-following scene as an example, we designed the driving experiments to obtain the driving data in drivers’ natural states, and a driving intention prediction model was constructed based on the HMM. Then, we analyzed the probability distribution and transition probability of driving intentions. In Study 2, we designed a series of emotion-induction experiments for eight typical driving emotions, and the drivers with induced emotion participated in the driving experiments similar to Study 1. Then, we obtained the driving data of the drivers in eight typical emotional states, and the driving intention prediction models adapted to the driver’s different emotional states were constructed based on the HMM severally. Finally, we analyzed the probabilistic differences of driving intention in divers’ natural states and different emotional states, and the findings showed the changing law of driving intention probability distribution and transfer probability caused by emotion evolution. The findings of this study can promote the development of driving behavior prediction technology and an active safety early warning system.

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“…Choosing appropriate PPs is of great deal of importance. Thanks to suitable PPs, maximum information about system dynamics and changes is transferred and also down sampled [45]. Having reviewed previous studies, we came to the conclusion to employ five suggested PPs [46].…”

Section: Feature Extraction Based On Ap and Poincare Planes (Pps) 24mentioning

confidence: 99%

EEG Source Identification through Phase Space Reconstruction and Complex Networks

Soroush¹

2020

Preprint

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Artifact elimination has become an inseparable part while processing electroencephalogram (EEG) in most brain computer interface (BCI) applications. Scientists have tried to introduce effective and efficient methods which can remove artifacts and also reserve desire information pertaining to brain activity. Blind source separation (BSS) methods have been receiving a great deal of attention in recent decades since they are considered routine and standard signal processing tools and are commonly used to eliminate artifacts and noise. Most studies, mainly EEG-related ones, apply BSS methods in preprocessing sections to achieve better results. On the other hand, BSS methods should be followed by a classifier in order to identify artifactual sources and remove them in next steps. Therefore, artifact identification is always a challenging problem while employing BSS methods. Additionally, removing all detected artifactual components leads to loss of information since some desire information related to neural activity leaks to these sources. So, an approach should be employed to suppress the artifacts and reserve neural activity. In this study, a new hybrid method is proposed to automatically separate and identify electroencephalogram (EEG) sources with the aim of classifying and removing artifacts. Automated source identification is still a challenge. Researchers have always made efforts to propose precise, fast and automated source verification methods. Reliable source identification has always been of great importance. This paper addresses blind source separation based on second order blind identification (SOBI) as it is reportedly one of the most effective methods in EEG source separation problems. Then a new method for source verification is introduced which takes advantage of components phase spaces and their dynamics. A new state space called angle space (AS) is introduced and features are extracted based on the angle plot (AP) and Poincare planes. Identified artifactual sources are eliminated using stationary wavelet transform (SWT). Simulated, semi-simulated and real EEG signals are employed to evaluate the proposed method. Different simulations are performed and performance indices are reported. Results show that the proposed method outperforms most recent studies in this subject.

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An unsupervised EEG decoding system for human emotion recognition

Cited by 92 publications

References 35 publications

Video‐triggered EEG‐emotion public databases and current methods: A survey

Video‐triggered EEG‐emotion public databases and current methods: A survey

Differences in Driving Intention Transitions Caused by Driver’s Emotion Evolutions

EEG Source Identification through Phase Space Reconstruction and Complex Networks

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