Facial Expression Recognition based on Electroencephalography

Raheel, Aasim; Majid, Muhammad; Anwar, Syed Muhammad

doi:10.1109/icomet.2019.8673408

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…The brain activity due to the changes in facial expression can be used either separately or combined with EEG in the purpose of BCI applications. Raheel et al [11] have been classified five facial expressions using brain activity and the classified facial expressions are smile, wink, looking up, looking down the eye. The brain activity has been captured through a 14-channel Emotiv EPOC EEG headset.…”

Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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Facial expression may establish communication between physically disabled people and assistive devices. Different types of facial expression including eye wink, smile, eye blink, looking up and looking down can be extracted from the brain signal. In this study, the possibility of controlling assistive devices using the individual's wink has been investigated. Brain signals from the five subjects have been captured to recognize the left wink, right wink, and no wink. The brain signals have been captured using Emotiv Insight which consists of five channels. Fast Fourier transform and the sample range have been computed to extract the features. The extracted features have been classified with the help of different machine learning algorithms. Here, support vector machine (SVM), linear discriminant analysis (LDA) and K-nearest neighbor (K-NN) have been employed to classify the features sets. The performance of the classifier in terms of accuracy, confusion matrix, true positive and false positive rate and the area under curve (AUC)-receiver operating characteristics (ROC) have been evaluated. In the case of sample range, the highest training and testing accuracies are 98.9% and 96.7% respectively which have been achieved by two classifiers namely, SVM and K-NN. The achieved results indicate that the person's wink can be utilized in controlling assistive devices.

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Section: Introductionmentioning

confidence: 99%

Wink based facial expression classification using machine learning approach

et al. 2020

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“…The decomposition is performed with multiple traditional time-frequency decomposition methods, such as empirical mode decomposition (EMD) and discrete wavelet transform (DWT) to fragment the signals. Similar techniques, such as naive bayes, k-nearest neighbors, and multi-layers perceptron are applied to extract 13 different facial expressions [ 27 ]. Various feature extraction methods were used in the literature to improve the performance on emotion detection [ 28 , 29 , 30 ].…”

Section: Related Orkmentioning

confidence: 99%

A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition

Haq

Yao

Rahmaniar

et al. 2022

Sensors

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Human emotions are variant with time, non-stationary, complex in nature, and are invoked as a result of human reactions during our daily lives. Continuously detecting human emotions from one-dimensional EEG signals is an arduous task. This paper proposes an advanced signal processing mechanism for emotion detection from EEG signals using continuous wavelet transform. The space and time components of the raw EEG signals are converted into 2D spectrograms followed by feature extraction. A hybrid spatio-temporal deep neural network is implemented to extract rich features. A differential-based entropy feature selection technique adaptively differentiates features based on entropy, based on low and high information regions. Bag of Deep Features (BoDF) is applied to create clusters of similar features and computes the features vocabularies for reduction of feature dimensionality. Extensive experiments are performed on the SEED dataset, which shows the significance of the proposed method compared to state-of-the-art methods. Specifically, the proposed model achieved 96.7%, 96.2%, 95.8%, and 95.3% accuracy with the SJTU SEED dataset, for SVM, ensemble, tree, and KNN classifiers, respectively.

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“…In this method, another trained network is needed to obtain the secondary information. A. Raheel et al employed brain signals of individuals to recognize their facial expressions [37]. Subjects were asked to express their facial expressions while watching a video clip and EEG data were recorded using a 14-channel Emotiv/EPOC EEG headset.…”

Section: Deep Learning-based Approachesmentioning

confidence: 99%

Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition

Jeong

Kim

Dong

2020

Sensors

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Understanding a person’s feelings is a very important process for the affective computing. People express their emotions in various ways. Among them, facial expression is the most effective way to present human emotional status. We propose efficient deep joint spatiotemporal features for facial expression recognition based on the deep appearance and geometric neural networks. We apply three-dimensional (3D) convolution to extract spatial and temporal features at the same time. For the geometric network, 23 dominant facial landmarks are selected to express the movement of facial muscle through the analysis of energy distribution of whole facial landmarks.We combine these features by the designed joint fusion classifier to complement each other. From the experimental results, we verify the recognition accuracy of 99.21%, 87.88%, and 91.83% for CK+, MMI, and FERA datasets, respectively. Through the comparative analysis, we show that the proposed scheme is able to improve the recognition accuracy by 4% at least.

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Facial Expression Recognition based on Electroencephalography

Cited by 19 publications

References 11 publications

Wink based facial expression classification using machine learning approach

Wink based facial expression classification using machine learning approach

A Hybrid Hand-Crafted and Deep Neural Spatio-Temporal EEG Features Clustering Framework for Precise Emotional Status Recognition

Deep Joint Spatiotemporal Network (DJSTN) for Efficient Facial Expression Recognition

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