Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System

Kwon, Yea Hoon; Shin, Sae Byuk; Kim, Shin Dug

doi:10.3390/s18051383

Cited by 166 publications

(90 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All experiments are carried out within the two emotion dimensions of arousal and valence. (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45) bands. In addition, the frequency-domain features also include the difference of average PSD (4 power differences × 14 channel pairs) in theta, alpha, beta, and gamma bands for 14 EEG channel pairs (Fp2-Fp1, AF4-AF3, F4-F3, F8-F7, FC6-FC5, FC2-FC1, C4-C3, T8-T7, CP6-CP5, CP2-CP1, P4-P3, P8-P7, PO4-PO3, and O2-O1) between the right and left scalps.…”

Section: Resultsmentioning

confidence: 99%

“…Wang and Shang [31] presented an emotion recognition method based on deep belief networks (DBNs). Kwon et al [32] used fusion features extracted from EEG signals and galvanic skin response signals and convolution neural networks (CNNs) for emotion recognition. In our previous work, an emotion recognition method based on an improved deep belief network with glia chains (DBN-GC) and multiple domain EEG features was proposed [23].…”

Section: Introductionmentioning

confidence: 99%

“…Compared with traditional machine learning methods, deep learning models have shown excellent performance and the potential ability for multichannel EEG-based emotion recognition tasks [31][32][33][34][35][36]. However, two challenges remain in multichannel EEG-based emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

et al. 2020

View full text Add to dashboard Cite

Emotion recognition based on multichannel electroencephalogram (EEG) signals is a key research area in the field of affective computing. Traditional methods extract EEG features from each channel based on extensive domain knowledge and ignore the spatial characteristics and global synchronization information across all channels. This paper proposes a global feature extraction method that encapsulates the multichannel EEG signals into gray images. The maximal information coefficient (MIC) for all channels was first measured. Subsequently, an MIC matrix was constructed according to the electrode arrangement rules and represented by an MIC gray image. Finally, a deep learning model designed with two principal component analysis convolutional layers and a nonlinear transformation operation extracted the spatial characteristics and global interchannel synchronization features from the constructed feature images, which were then input to support vector machines to perform the emotion recognition tasks. Experiments were conducted on the benchmark dataset for emotion analysis using EEG, physiological, and video signals. The experimental results demonstrated that the global synchronization features and spatial characteristics are beneficial for recognizing emotions and the proposed deep learning model effectively mines and utilizes the two salient features.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

et al. 2020

View full text Add to dashboard Cite

show abstract

“…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

171

View full text Add to dashboard Cite

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.

show abstract

“…The impurity of this algorithm is when having traces of one class division into others, therefore the classification rate is low. In [20] was used as an electroencephalogram (EEG) and galvanic skin response (GSR) signals to emotion recognition. A zero-crossing rate is used for pre-processing the GSR single.…”

Section: Introductionmentioning

confidence: 99%

Human Motion Detection through Wall Based on Micro-Doppler Using Kalman Filter Combined with Convolutional Neural Network

Saeed¹,

Abbas²,

Abed³

et al. 2019

IJIES

View full text Add to dashboard Cite

Human motion detection, classification, and perceiving, the dynamics of moving objects in the environment, are crucial in many applications. Many sensors have been used to this detection; the radar represents one of the promising sensors. Kalman filter (KF) and convolutional neural network (CNN) represents a powerful tool for estimation and classification respectively. In this paper, a combination between the KF and CNN have been proposed to detect and classify human behavior. This proposal presents two important points, the precise features map from the combination of Kalman Filter and CNN, as well as the use of the radar, which is working under all circumstances and does not break the privacy. Twenty different experiments with three scenarios for different motion with and without glass wall have been studied, and they are classified. The results show that the overperform of the proposed algorithm and the classification accuracy can reach 98.7%. This advancement of the proposed algorithm depends on the efficient Wigner-Ville short time Fourier transform (STFT) which is used as a feature extractor and make Range-Doppler (RD) map.

show abstract

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System

Cited by 166 publications

References 37 publications

Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

Improved Deep Feature Learning by Synchronization Measurements for Multi-Channel EEG Emotion Recognition

Deep Learning in Physiological Signal Data: A Survey

Human Motion Detection through Wall Based on Micro-Doppler Using Kalman Filter Combined with Convolutional Neural Network

Contact Info

Product

Resources

About