A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition

Li, Yang; Wang, Lei; Zheng, Wenming; Zong, Yuan; Qi, Lei; Cui, Zhen; Zhang, Tong; Song, Tengfei

doi:10.1109/tcds.2020.2999337

Cited by 197 publications

(91 citation statements)

References 39 publications

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“…With the features of all bands, the SOGNN achieved averaged accuracy of 86.81% on the SEED dataset and 75.27% on the SEED-IV dataset, which are higher than the performances of the state-of-the-art methods, i. e. the BiHDM (Li et al, 2020 ) and RGNN (Zhong et al, 2020 ) models. The proposed SOGNN achieved a macro-F1 score of 0.8669 and an AUC score of 0.9685 on the SEED dataset.…”

Section: Resultsmentioning

confidence: 84%

Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network

Pan

et al. 2021

Front. Neurosci.

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As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.

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Section: Resultsmentioning

confidence: 84%

Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network

Pan

et al. 2021

Front. Neurosci.

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“…In recent years, many studies have been conducted to learn the asymmetric differences between the two hemispheres in EEG data. For instance, Li et al [43] proposed a novel bi-hemispheric discrepancy model in emotion recognition and obtained deep representations of all the EEG channels' signals. This inspired us to investigate the efficiency of our method on the left and right hemispheres separately.…”

Section: Eeg Hemispheric Asymmetrymentioning

confidence: 99%

EEG Classification by Factoring in Sensor Spatial Configuration

et al. 2021

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Electroencephalography (EEG) serves as an effective diagnostic tool for mental disorders and neurological abnormalities. Enhanced analysis and classification of EEG signals can help improve performance in classifying the disorders and abnormalities. A new approach is examined here for enhancing EEG classification performance using a novel model of data representation that leverages knowledge of spatial layout of EEG sensors. An investigation of the performance of the proposed data representation model provides evidence of consistently higher classification accuracy of the proposed model compared with a model that ignores the sensor layout. The performance is assessed for models that represent the information content of the EEG signals in two different ways: a one-dimensional concatenation of the channels of the frequency bands and a proposed image-like two-dimensional representation of the EEG channel locations. The models are used in conjunction with different machine learning techniques. Performance of these models is examined on two tasks: social anxiety disorder classification, and emotion recognition using a dataset, DEAP, for emotion analysis using physiological signals. We hypothesize that the proposed two-dimensional model will significantly outperform the one-dimensional model and this is validated in our results as this model consistently yields 5-8% higher accuracy in all machine learning algorithms investigated. Among the algorithms investigated, Convolutional Neural Networks provide the best performance, far exceeding that of Support Vector Machine and k-Nearest Neighbors algorithms.

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“…Their method resulted in an accuracy of 69.08% with a standard deviation of 16.66 for subject independent classification. Authors from "A Novel Bi-hemispheric Discrepancy Model for EEG Emotion Recognition" [6] proposed a bihemispheric discrepancy model (BiHDM) that presents a study for asymmetric differences between two hemispheres for electroencephalograph (EEG) emotion recognition. Their model achieved an accuracy of 74.35% with a standard deviation of 14.09 for subject independent classification and 69.03% accuracy with a standard deviation of 8.66 for subject independent classification.…”

Section: Relatedworkmentioning

confidence: 99%

“…For subject dependent classification, we have followed the experimental setup used by [6], [7] and [4] i.e. to use the first 16 trials for the training set and remaining 8 trials containing all emotions (two trials per emotion class) for testing.…”

Section: Subject Dependentclassificationmentioning

confidence: 99%

Application of Deep Learning for Emotion Recognition

Toshniwal¹,

Puri²

2021

YMER

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The electroencephalogram (EEG) gained a lot of importance in recent years because of its property to depict the nature and actions of human perception. EEG signals are good at capturing the emotional state of a person by measuring the neuronal activities in different regions of the brain. Lots of EEG-based brain-computer interfaces with a different number of channels ( 62 channels, 32 channels, etc.) are being used to capture neuronal activities which can be segmented into different frequency ranges (delta, theta, alpha. beta and gamma). This paper puts forward a neural network architecture for the recognition of emotion from EEG signals and a study providing the set of brain regions and the frequency type associated with the corresponding brain region which contributes most for the detection of emotion though EEG signals. For experimentation, SEED-IV dataset has been used

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A Novel Bi-Hemispheric Discrepancy Model for EEG Emotion Recognition

Cited by 197 publications

References 39 publications

Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network

Cross-Subject EEG Emotion Recognition With Self-Organized Graph Neural Network

EEG Classification by Factoring in Sensor Spatial Configuration

Application of Deep Learning for Emotion Recognition

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