Emotion Recognition in Valence-Arousal Space from Multi-channel EEG data and Wavelet based Deep Learning Framework

Garg, Divya; Verma, Gyanendra K.

doi:10.1016/j.procs.2020.04.093

Cited by 63 publications

(28 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, considering the better generalization ability of SVM in previous studies (Yao et al, 2019 ), we suggest that SVM is the best classifier for the binary classification of positive and negative emotions based on two-channel data. It’s worth noting that, the classification efficiency of multi-channel feature classification is better than two-channel feature classification regardless of the feature classification effect due to the fact that multi-channel has more information and can better represent the information (Lin et al, 2009 ; Garg and Verma, 2020 ). Therefore, to pursue higher classification accuracy, as many channels as possible should be selected to explore emotion classification.…”

Section: Discussionmentioning

confidence: 99%

Positive and Negative Emotion Classification Based on Multi-channel

Long

Zhao

Wei

et al. 2021

Front. Behav. Neurosci.

View full text Add to dashboard Cite

The EEG features of different emotions were extracted based on multi-channel and forehead channels in this study. The EEG signals of 26 subjects were collected by the emotional video evoked method. The results show that the energy ratio and differential entropy of the frequency band can be used to classify positive and negative emotions effectively, and the best effect can be achieved by using an SVM classifier. When only the forehead and forehead signals are used, the highest classification accuracy can reach 66%. When the data of all channels are used, the highest accuracy of the model can reach 82%. After channel selection, the best model of this study can be obtained. The accuracy is more than 86%.

show abstract

Section: Discussionmentioning

confidence: 99%

Positive and Negative Emotion Classification Based on Multi-channel

Long

Zhao

Wei

et al. 2021

Front. Behav. Neurosci.

View full text Add to dashboard Cite

show abstract

“…According to (Coan et al, 2001 ), the greater left frontal brain is in charge of positive emotions, while the right frontal brain electrodes report negative emotions. Machine learning techniques (i.e., Convolutional Neural Network (Garg & Verma, 2020 )) classify the valance-arousal features and recognise the emotional state (Bazgir et al, 2018 ).…”

Section: Related Workmentioning

confidence: 99%

A machine learning enabled affective E-learning system model

Liu

Ardakani

2022

Educ Inf Technol

View full text Add to dashboard Cite

“…Its various bands can reflect the internal activity state of the brain ( Table 2 ). EEGs have been widely used in emotion recognition [ 31 , 32 ], attention level measurement [ 33 ], cognitive workload measurement [ 34 , 35 ], thinking-state detection [ 36 , 37 ], academic stress detection [ 38 ], cognitive psychological disease detection [ 39 , 40 ], fatigue monitoring [ 41 ], mind control [ 42 ], and other areas. Since the biological nature of EEG information is difficult to disguise or mask, EEGs can more objectively reflect internal processes than behaviors, voices, facial expressions, and so on [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an EEG-Based Learning-Style Recognition Mechanism

et al. 2021

View full text Add to dashboard Cite

Existing methods for learning-style recognition are highly subjective and difficult to implement. Therefore, the present study aimed to develop a learning-style recognition mechanism based on EEG features. The process for the mechanism included labeling learners’ actual learning styles, designing a method to effectively stimulate different learners’ internal state differences regarding learning styles, designing the data-collection method, designing the preprocessing procedure, and constructing the recognition model. In this way, we designed and verified an experimental method that can effectively stimulate learning-style differences in the information-processing dimension. In addition, we verified the effectiveness of using EEG signals to recognize learning style. The recognition accuracy of the learning-style processing dimension was 71.2%. This result is highly significant for the further exploration of using EEG signals for effective learning-style recognition.

show abstract

Emotion Recognition in Valence-Arousal Space from Multi-channel EEG data and Wavelet based Deep Learning Framework

Cited by 63 publications

References 16 publications

Positive and Negative Emotion Classification Based on Multi-channel

Positive and Negative Emotion Classification Based on Multi-channel

A machine learning enabled affective E-learning system model

Design and Implementation of an EEG-Based Learning-Style Recognition Mechanism

Contact Info

Product

Resources

About