Convolutional Neural Network Approach for Eeg-Based Emotion Recognition Using Brain Connectivity and its Spatial Information

Moon, Seong-Eun; Jang, Sungil; Lee, Jong-Seok

doi:10.1109/icassp.2018.8461315

Cited by 108 publications

(64 citation statements)

References 17 publications

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“…Moon et al [15] presented a CNN-based emotion recognition model for EEG signals. A major difference of their approach is its brain connectivity features, used to account for synchronous activation of many different brain regions.…”

Section: Cnn-based Modelsmentioning

confidence: 99%

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Yang

Han

Min

2019

Sensors

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Visual stimuli from photographs and artworks raise corresponding emotional responses. It is a long process to prove whether the emotions that arise from photographs and artworks are different or not. We answer this question by employing electroencephalogram (EEG)-based biosignals and a deep convolutional neural network (CNN)-based emotion recognition model. We employ Russell’s emotion model, which matches emotion keywords such as happy, calm or sad to a coordinate system whose axes are valence and arousal, respectively. We collect photographs and artwork images that match the emotion keywords and build eighteen one-minute video clips for nine emotion keywords for photographs and artwork. We hired forty subjects and executed tests about the emotional responses from the video clips. From the t-test on the results, we concluded that the valence shows difference, while the arousal does not.

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Section: Cnn-based Modelsmentioning

confidence: 99%

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Yang

Han

Min

2019

Sensors

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“…With the recent explosion of deep learning techniques, there are studies to apply deep learning to brain signal analysis. It was shown that several deep neural networks such as deep belief networks, stacked denoising autoencoders, and convolutional neural networks can extract effective application-driven features for EEG signals in spatial and spectral domains [10] [11][12] [13]. Deep learning models on graph signals, especially graph convolutional neural networks (GCNN) [5] [6] have been considered as competitive approaches for analyzing MEG [14] and fMRI [15] signals.…”

Section: Related Workmentioning

confidence: 99%

Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network

Jang

Moon

Lee

2018

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes a novel graph signal-based deep learning method for electroencephalography (EEG) and its application to EEG-based video identification. We present new methods to effectively represent EEG data as signals on graphs, and learn them using graph convolutional neural networks. Experimental results for video identification using EEG responses obtained while watching videos show the effectiveness of the proposed approach in comparison to existing methods. Effective schemes for graph signal representation of EEG are also discussed.

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“…For example, ICU monitoring tools are able to perform live diagnosis based on some ECG recordings. Unfortunately, the efficiency of this tool remains a problem (Kim, 2018) In the literature, a number of studies have been placed to find automatic diagnostic tools for ECG signals (Acharya et al, 2017a;Kiranyaz et al, 2016;Moon et al, 2018). (Acharya et al, 2018a(Acharya et al, , 2017a From the scientists' point of view, the heart is very completed and different and new types of arrhythmia can affect the heart.…”

Section: Application Of Cnn For Ecg Analysismentioning

confidence: 99%

Convolutional Neural Network Application in Biomedical Signals

Alaskar¹

2018

JCSIT

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Recent improvements in big data and machine learning have enhanced the importance of biomedical signal and image-processing research. One part of machine learning evolution is deep learning networks. Deep learning networks are designed for the task of exploiting compositional structure in data. The golden age of the deep learning network in particular convolutional neural networks (CNNs) began in 2012. CNNs have rapidly become a methodology of optimal choice for analysing biomedical signals. CNNs have been successful in detecting and diagnosing an abnormality in biomedical signals. This paper has three distinct aims. The key primary aim is to provide state of the art knowledge about how deep learning evolved and revolutionized machine learning in the past few years. Second, to critically review the application of deep learning for different biomedical signals analysis and provide a holistic overview of current works of literature. Finally, to discuss the research opportunities with deep learning algorithms in the field of study that can serve as a starting point for new researchers to identify the future research direction in a concise manner.

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Convolutional Neural Network Approach for Eeg-Based Emotion Recognition Using Brain Connectivity and its Spatial Information

Cited by 108 publications

References 17 publications

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Distinguishing Emotional Responses to Photographs and Artwork Using a Deep Learning-Based Approach

Eeg-Based Video Identification Using Graph Signal Modeling and Graph Convolutional Neural Network

Convolutional Neural Network Application in Biomedical Signals

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