Emotion Recognition using AutoEncoders and Convolutional Neural Networks

Prieto, Luis Antonio Beltrán; Oplatková, Zuzana Komínková

doi:10.13164/mendel.2018.1.113

Cited by 8 publications

(3 citation statements)

References 25 publications

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“…For the classification of EEG signals, many machine learning methods such as K-Nearest Neighbor (KNN) [28], Support Vector Machine (SVM) [28][29][30], Decision Tree (DT) [31], Random Forest (RF) [32] and Linear Discriminant Analysis (LDA) [33] are applied. 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].…”

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

show abstract

“…Multimodal fusion model can obtain emotion recognition results by fusing different physiological signals. [27] is aimed to analyze the performance of a Convolutional Neural Network which uses AutoEncoder Units for emotion recognition in human faces. The combination of two Deep Learning techniques boosts the performance of the classification system.…”

Section: (3)multimodal Emotion Recognition Methodsmentioning

confidence: 99%

Expression-EEG Based Collaborative Multimodal Emotion Recognition Using Deep AutoEncoder

Zhang

2020

IEEE Access

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Emotion recognition has shown many valuable roles in people's lives under the background of artificial intelligence technology. However, most existing emotion recognition methods have poor recognition performance, which prevents their promotion in practical applications. To alleviate this problem, we proposed an expression-EEG interaction multi-modal emotion recognition method using a deep automatic encoder. Firstly, decision tree is applied as objective feature selection method. Then, based on the facial expression features recognized by sparse representation, the solution vector coefficients are analyzed to determine the facial expression category of the test samples. After that, the bimodal deep automatic encoder is adopted to fuse the EEG signals and facial expression signals. The third layer of BDAE extracts features for training of supervised learning. Finally, LIBSVM classifier is used to complete classification task. We carried out experiments on a constructed video library to verify the proposed emotion recognition method. The results show that the proposed method can effectively extract and integrate high-level emotion-related features in EEG and facial expression signals. The recognition rate of discrete emotion state type and the average emotion recognition rate have been improved relatively, in which the average emotion recognition rate is 85.71%. Overall, the emotion recognition ability has been greatly improved.

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Emotion recognition framework using multiple modalities for an effective human–computer interaction

et al. 2023

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Emotion Recognition using AutoEncoders and Convolutional Neural Networks

Cited by 8 publications

References 25 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

Expression-EEG Based Collaborative Multimodal Emotion Recognition Using Deep AutoEncoder

Emotion recognition framework using multiple modalities for an effective human–computer interaction

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