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Author

Reda A. El-Khoribi

Bio: Reda A. El-Khoribi is an academic researcher from Cairo University. The author has contributed to research in topics: Hidden Markov model & Deep learning. The author has an hindex of 7, co-authored 29 publications receiving 381 citations.

Papers
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Journal ArticleDOI
TL;DR: A deep learning method is proposed to recognize emotion from raw EEG signals using Long-Short Term Memory (LSTM) and the dense layer classifies these features into low/high arousal, valence, and liking.
Abstract: Emotion is the most important component in daily interaction between people. Nowadays, it is important to make the computers understand user’s emotion who interacts with it in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals are the main source of emotion in our body. Recently, emotion recognition based on EEG signals have attracted many researchers and many methods were reported. Different types of features were extracted from EEG signals then different types of classifiers were applied to these features. In this paper, a deep learning method is proposed to recognize emotion from raw EEG signals. Long-Short Term Memory (LSTM) is used to learn features from EEG signals then the dense layer classifies these features into low/high arousal, valence, and liking. DEAP dataset is used to verify this method which gives an average accuracy of 85.65%, 85.45%, and 87.99% with arousal, valence, and liking classes, respectively. The proposed method introduced high average accuracy in comparison with the traditional techniques.

384 citations

Journal ArticleDOI
TL;DR: In this approach, the use of the 3-Dimensional Convolutional Neural Networks (3D-CNN) is investigated using a multi-channel EEG data for emotion recognition and it is found that the proposed method is able to achieve recognition accuracies outperforming the state of the art methods.
Abstract: Emotion recognition is a crucial problem in Human-Computer Interaction (HCI) Various techniques were applied to enhance the robustness of the emotion recognition systems using electroencephalogram (EEG) signals especially the problem of spatiotemporal features learning In this paper, a novel EEG-based emotion recognition approach is proposed In this approach, the use of the 3-Dimensional Convolutional Neural Networks (3D-CNN) is investigated using a multi-channel EEG data for emotion recognition A data augmentation phase is developed to enhance the performance of the proposed 3D-CNN approach And, a 3D data representation is formulated from the multi-channel EEG signals, which is used as data input for the proposed 3D-CNN model Extensive experimental works are conducted using the DEAP (Dataset of Emotion Analysis using the EEG and Physiological and Video Signals) data It is found that the proposed method is able to achieve recognition accuracies 8744% and 8849% for valence and arousal classes respectively, which is outperforming the state of the art methods

158 citations

Journal ArticleDOI
TL;DR: The proposed approach outperforms recent works in multi-modal emotion recognition field and achieves recognition accuracies 96.13%, and 96.79% for valence, and arousal classes respectively.

48 citations

Journal ArticleDOI
TL;DR: In this article, the adaptive mask region-based convolutional network (mask-RCNN) was utilized for multi-class object detection in remote sensing images, where transfer learning, data augmentation and fine-tuning were adopted to overcome objects scale variability, small size, the density of objects, and the scarcity of annotated remote sensing image.
Abstract: Fast and automatic object detection in remote sensing images is a critical and challenging task for civilian and military applications. Recently, deep learning approaches were introduced to overcome the limitation of traditional object detection methods. In this paper, adaptive mask Region-based Convolutional Network (mask-RCNN) is utilized for multi-class object detection in remote sensing images. Transfer learning, data augmentation, and fine-tuning were adopted to overcome objects scale variability, small size, the density of objects, and the scarcity of annotated remote sensing image. Also, five optimization methods were investigated namely: Adaptive Moment Estimation (Adam), stochastic gradient decent (SGD), adaptive learning rate method (Adelta), Root Mean Square Propagation (RMSprop) and hybrid optimization. In hybrid optimization, the training process begins Adam then switches to SGD when appropriate and vice versa. Also, the behaviour of adaptive mask RCNN was compared to baseline deep object detection methods. Several experiments were conducted on the challenging NWPU-VHR-10 dataset. The hybrid method Adam_SGD acheived the highest Accuracy precision, with 95%. Experimental results showed detection performance in terms of accuracy and intersection over union (IOU) boost of performance up to 6%.

29 citations

Journal ArticleDOI
TL;DR: An automatic speech recognition system for people with dysarthria speech disorder based on both speech and visual components is introduced and results indicate that visual features are highly effective and can improve the accuracy to reach 7.91% for speaker dependent experiments and 3% for Speaker independent experiments.
Abstract: recognition of disorder people is a difficult task due to the lack of motor-control of the speech articulators. Multimodal speech recognition can be used to enhance the robustness of disordered speech. This paper introduces an automatic speech recognition system for people with dysarthria speech disorder based on both speech and visual components. The Mel-Frequency Cepestral Coefficients (MFCC) is used as features representing the acoustic speech signal. For the visual counterpart, the Discrete Cosine Transform (DCT) Coefficients are extracted from the speaker's mouth region. Face and mouth regions are detected using the Viola-Jones algorithm. The acoustic and visual input features are then concatenated on one feature vector. Then, the Hidden Markov Model (HMM) classifier is applied on the combined feature vector of acoustic and visual components. The system is tested on isolated English words spoken by disorder speakers from UA-Speech data. Results of the proposed system indicate that visual features are highly effective and can improve the accuracy to reach 7.91% for speaker dependent experiments and 3% for speaker independent experiments.

22 citations


Cited by
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Journal ArticleDOI
TL;DR: Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.
Abstract: Objective Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? Approach A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture. Main results For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review. Significance This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.

777 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present a review of 154 studies that apply deep learning to EEG, published between 2010 and 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring.
Abstract: Context Electroencephalography (EEG) is a complex signal and can require several years of training, as well as advanced signal processing and feature extraction methodologies to be correctly interpreted. Recently, deep learning (DL) has shown great promise in helping make sense of EEG signals due to its capacity to learn good feature representations from raw data. Whether DL truly presents advantages as compared to more traditional EEG processing approaches, however, remains an open question. Objective In this work, we review 154 papers that apply DL to EEG, published between January 2010 and July 2018, and spanning different application domains such as epilepsy, sleep, brain-computer interfacing, and cognitive and affective monitoring. We extract trends and highlight interesting approaches from this large body of literature in order to inform future research and formulate recommendations. Methods Major databases spanning the fields of science and engineering were queried to identify relevant studies published in scientific journals, conferences, and electronic preprint repositories. Various data items were extracted for each study pertaining to (1) the data, (2) the preprocessing methodology, (3) the DL design choices, (4) the results, and (5) the reproducibility of the experiments. These items were then analyzed one by one to uncover trends. Results Our analysis reveals that the amount of EEG data used across studies varies from less than ten minutes to thousands of hours, while the number of samples seen during training by a network varies from a few dozens to several millions, depending on how epochs are extracted. Interestingly, we saw that more than half the studies used publicly available data and that there has also been a clear shift from intra-subject to inter-subject approaches over the last few years. About [Formula: see text] of the studies used convolutional neural networks (CNNs), while [Formula: see text] used recurrent neural networks (RNNs), most often with a total of 3-10 layers. Moreover, almost one-half of the studies trained their models on raw or preprocessed EEG time series. Finally, the median gain in accuracy of DL approaches over traditional baselines was [Formula: see text] across all relevant studies. More importantly, however, we noticed studies often suffer from poor reproducibility: a majority of papers would be hard or impossible to reproduce given the unavailability of their data and code. Significance To help the community progress and share work more effectively, we provide a list of recommendations for future studies and emphasize the need for more reproducible research. We also make our summary table of DL and EEG papers available and invite authors of published work to contribute to it directly. A planned follow-up to this work will be an online public benchmarking portal listing reproducible results.

699 citations

Journal ArticleDOI
28 Jun 2018-Sensors
TL;DR: A comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals is presented.
Abstract: Emotion recognition based on physiological signals has been a hot topic and applied in many areas such as safe driving, health care and social security. In this paper, we present a comprehensive review on physiological signal-based emotion recognition, including emotion models, emotion elicitation methods, the published emotional physiological datasets, features, classifiers, and the whole framework for emotion recognition based on the physiological signals. A summary and comparation among the recent studies has been conducted, which reveals the current existing problems and the future work has been discussed.

484 citations