EEG-Based Emotion Recognition using 3D Convolutional Neural Networks

Salama, Elham S.; El-Khoribi, Reda A.; Shoman, Mahmoud; Shalaby, Mohamed

doi:10.14569/ijacsa.2018.090843

Cited by 160 publications

(112 citation statements)

References 18 publications

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“…In the deep learning context, DBN (Deep Belief Network) [34] and AE (Auto Encoders) [35] are studied with promising results. Besides DBN and AE, Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM) structures are widely used [36][37][38][39][40]. Most of these models have shown good results for subject dependent analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In EEG data channels, typical frequency domain analysis is used. In the frequency domain, the most important frequency bands are delta (1-3 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12)(13), beta (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma (31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49)(50) [26]. Fast Fourier Transform (FFT), Wavelet Transform (WT), eigenvector and autoregressive are the methods which transform EEG signal from time domain to frequency domain [27].…”

Section: Introductionmentioning

confidence: 99%

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Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

Çimtay

Ekmekcioǧlu

2020

Sensors

196

108

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The electroencephalogram (EEG) has great attraction in emotion recognition studies due to its resistance to deceptive actions of humans. This is one of the most significant advantages of brain signals in comparison to visual or speech signals in the emotion recognition context. A major challenge in EEG-based emotion recognition is that EEG recordings exhibit varying distributions for different people as well as for the same person at different time instances. This nonstationary nature of EEG limits the accuracy of it when subject independency is the priority. The aim of this study is to increase the subject-independent recognition accuracy by exploiting pretrained state-of-the-art Convolutional Neural Network (CNN) architectures. Unlike similar studies that extract spectral band power features from the EEG readings, raw EEG data is used in our study after applying windowing, pre-adjustments and normalization. Removing manual feature extraction from the training system overcomes the risk of eliminating hidden features in the raw data and helps leverage the deep neural network’s power in uncovering unknown features. To improve the classification accuracy further, a median filter is used to eliminate the false detections along a prediction interval of emotions. This method yields a mean cross-subject accuracy of 86.56% and 78.34% on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED) for two and three emotion classes, respectively. It also yields a mean cross-subject accuracy of 72.81% on the Database for Emotion Analysis using Physiological Signals (DEAP) and 81.8% on the Loughborough University Multimodal Emotion Dataset (LUMED) for two emotion classes. Furthermore, the recognition model that has been trained using the SEED dataset was tested with the DEAP dataset, which yields a mean prediction accuracy of 58.1% across all subjects and emotion classes. Results show that in terms of classification accuracy, the proposed approach is superior to, or on par with, the reference subject-independent EEG emotion recognition studies identified in literature and has limited complexity due to the elimination of the need for feature extraction.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

Çimtay

Ekmekcioǧlu

2020

Sensors

196

108

View full text Add to dashboard Cite

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“…Salama et al [14] proposed a 3D CNN model to recognize emotions from EEG signals of multi-channel structure. EEG signals have a spatio-temporal aspect, hence 3D CNN is appropriate to train with the signals.…”

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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“…Inputs were Dataset of emotion analysis and psychological (DEAP) and video data. This method outperformed other state of the art technologies of the period providing an accuracy of classification around 88%[11].…”

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confidence: 86%

Electroencephalogram Classification Using Various Artificial Neural Networks

2020

jcr

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Electroencephalography (EEG) is the oldest diagnostic tool used in the field of neurosciences. When a neurologist sees an EEG report, he can point out important neural defects in a person but many a times diagnoses were missed and it is just impossible for human brain to process all the data in EEG. Nowadays many deep machine learning architectures are developed to understand the information contained in EEG signals. This study reviews various literatures on classification of EEG signals using various artificial neural networks like Convoluted neural network, Recurrent Neural Network, Deep Belief neural networks and hybrids.

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EEG-Based Emotion Recognition using 3D Convolutional Neural Networks

Cited by 160 publications

References 18 publications

Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

Investigating the Use of Pretrained Convolutional Neural Network on Cross-Subject and Cross-Dataset EEG Emotion Recognition

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

Electroencephalogram Classification Using Various Artificial Neural Networks

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