Emotion Recognition from Multiband EEG Signals Using CapsNet

Hao, Chao; Dong, Liang; Liu, Yongli; Lu, Bao-Yun

doi:10.3390/s19092212

Cited by 238 publications

(119 citation statements)

References 37 publications

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“…On the DEAP dataset, the performances of the strength feature for two binary classification tasks (arousal-level and valence-level) are 73.42 ± 4.67% and 76.10 ± 4.49%, respectively. These results considerably outperform those of 62.0% and 57.6% [24] achieved by the PSD feature, as well as those of 68.28% and 66.73% [69] attained using the capsule network.…”

Section: Experimental Results On Seed and Deap Datasetsmentioning

confidence: 56%

Identifying Functional Brain Connectivity Patterns for EEG-Based Emotion Recognition

Zheng

2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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Compared with the rich studies on the motor brain-computer interface (BCI), the recently emerging affective BCI presents distinct challenges since the brain functional connectivity networks involving emotion are not well investigated. Previous studies on emotion recognition based on electroencephalography (EEG) signals mainly rely on single-channel-based feature extraction methods. In this paper, we propose a novel emotion-relevant critical subnetwork selection algorithm and investigate three EEG functional connectivity network features: strength, clustering coefficient, and eigenvector centrality. The discrimination ability of the EEG connectivity features in emotion recognition is evaluated on three public emotion EEG datasets: SEED, SEED-V, and DEAP. The strength feature achieves the best classification performance and outperforms the state-of-the-art differential entropy feature based on single-channel analysis for the EEG signals. The experimental results reveal that distinct functional connectivity patterns are exhibited for the five emotions of disgust, fear, sadness, happiness, and neutrality. Furthermore, we construct a multimodal emotion recognition model by combining the functional connectivity features from EEG and the features from eye movements or physiological signals using deep canonical correlation analysis. The classification accuracies of multimodal emotion recognition are 95.08 ± 6.42% on the SEED dataset, 84.51 ± 5.11% on the SEED-V dataset, and 85.34 ± 2.90% and 86.61 ± 3.76% for arousal and valence on the DEAP dataset, respectively. The results demonstrate the complementary representation properties of the EEG functional connectivity network features with eye movement data. In addition, we find that the brain networks constructed with fewer channels, i.e., 18 channels, achieve comparable performance with that of the 62-channel network with respect to multimodal emotion recognition and enable easier setups for BCI systems in real scenarios.Index Terms-Affective brain-computer interface, EEG, eye movement, brain functional connectivity network, multimodal emotion recognition. ! •

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Section: Experimental Results On Seed and Deap Datasetsmentioning

confidence: 56%

Identifying Functional Brain Connectivity Patterns for EEG-Based Emotion Recognition

Zheng

2019

2019 9th International IEEE/EMBS Conference on Neural Engineering (NER)

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“…The DEAP dataset was used [59] New cross-subject emotion recognition model based on the newly designed multiple transferable recursive feature elimination are developed High/low arousal, valence and dominance 32 channel data from DEAP dataset was used to validate the proposed method [60] Presented novel approach based on the multiscale information analysis (MIA) of EEG signals for distinguishing emotional.…”

Section: High/low Arousal Valence and Dominancementioning

confidence: 99%

Human Emotion Recognition: Review of Sensors and Methods

Dzedzickis

Kaklauskas

Bučinskas

2020

Sensors

376

170

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Automated emotion recognition (AEE) is an important issue in various fields of activities which use human emotional reactions as a signal for marketing, technical equipment, or human–robot interaction. This paper analyzes scientific research and technical papers for sensor use analysis, among various methods implemented or researched. This paper covers a few classes of sensors, using contactless methods as well as contact and skin-penetrating electrodes for human emotion detection and the measurement of their intensity. The results of the analysis performed in this paper present applicable methods for each type of emotion and their intensity and propose their classification. The classification of emotion sensors is presented to reveal area of application and expected outcomes from each method, as well as their limitations. This paper should be relevant for researchers using human emotion evaluation and analysis, when there is a need to choose a proper method for their purposes or to find alternative decisions. Based on the analyzed human emotion recognition sensors and methods, we developed some practical applications for humanizing the Internet of Things (IoT) and affective computing systems.

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“…Hand motion recognition [9][10][11][12][13][14][15][16][17], Muscle activity recognition [18][19][20][21][22][23] ECG Heartbeat signal classification , Heart disease classification [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], Sleep-stage classification [64][65][66][67][68], Emotion classification [69], age and gender prediction [70] EEG Brain functionality classification , Brain disease classification , Emotion classification [122][123][124][125][126][127][128][129], Sleep-stage classification [130][131][132][133]…”

Section: Emgmentioning

confidence: 99%

Deep Learning in Physiological Signal Data: A Survey

Rim

Sung

Min

et al. 2020

Sensors

160

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Deep Learning (DL), a successful promising approach for discriminative and generative tasks, has recently proved its high potential in 2D medical imaging analysis; however, physiological data in the form of 1D signals have yet to be beneficially exploited from this novel approach to fulfil the desired medical tasks. Therefore, in this paper we survey the latest scientific research on deep learning in physiological signal data such as electromyogram (EMG), electrocardiogram (ECG), electroencephalogram (EEG), and electrooculogram (EOG). We found 147 papers published between January 2018 and October 2019 inclusive from various journals and publishers. The objective of this paper is to conduct a detailed study to comprehend, categorize, and compare the key parameters of the deep-learning approaches that have been used in physiological signal analysis for various medical applications. The key parameters of deep-learning approach that we review are the input data type, deep-learning task, deep-learning model, training architecture, and dataset sources. Those are the main key parameters that affect system performance. We taxonomize the research works using deep-learning method in physiological signal analysis based on: (1) physiological signal data perspective, such as data modality and medical application; and (2) deep-learning concept perspective such as training architecture and dataset sources.

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Emotion Recognition from Multiband EEG Signals Using CapsNet

Cited by 238 publications

References 37 publications

Identifying Functional Brain Connectivity Patterns for EEG-Based Emotion Recognition

Identifying Functional Brain Connectivity Patterns for EEG-Based Emotion Recognition

Human Emotion Recognition: Review of Sensors and Methods

Deep Learning in Physiological Signal Data: A Survey

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