Sleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR); fast correlation based feature selection (FCBF); ReliefF; t-test; and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF); feed-forward neural network (FFNN); decision tree (DT); support vector machine (SVM); and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.

A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms

We introduce SCiFI, a system for Secure Computation of Face Identification. The system performs face identification which compares faces of subjects with a database of registered faces. The identification is done in a secure way which protects both the privacy of the subjects and the confidentiality of the database. A specific application of SCiFI is reducing the privacy impact of camera based surveillance. In that scenario, SCiFI would be used in a setting which contains a server which has a set of faces of suspects, and client machines which might be cameras acquiring images in public places. The system runs a secure computation of a face recognition algorithm, which identifies if an image acquired by a client matches one of the suspects, but otherwise reveals no information to neither of the parties. Our work includes multiple contributions in different areas: A new face identification algorithm which is unique in having been specifically designed for usage in secure computation. Nonetheless, the algorithm has face recognition performance comparable to that of state of the art algorithms. We ran experiments which show the algorithm to be robust to different viewing conditions, such as illumination, occlusions, and changes in appearance (like wearing glasses). A secure protocol for computing the new face recognition algorithm. In addition, since our goal is to run an actual system, considerable effort was made to optimize the protocol and minimize its online latency. A system - SCiFI, which implements a secure computation of the face identification protocol. Experiments which show that the entire system can run in near real-time: The secure computation protocol performs a preprocessing of all public-key cryptographic operations. Its online performance therefore mainly depends on the speed of data communication, and our experiments show it to be extremely efficient.

/pdf/scifi-a-system-for-secure-face-identification-1twkz240c1.pdf

SCiFI - A System for Secure Face Identification

Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced to recognize emotion states according to the input MFM. Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models. The experimental results demonstrate that the three characteristics contained in the MFM were complementary and the capsule network was more suitable for mining and utilizing the three correlation characteristics.

https://www.mdpi.com/1424-8220/19/9/2212/pdf

Emotion Recognition from Multiband EEG Signals Using CapsNet

A review of state-of-the-art feature extraction methods from electroencephalogram signals.A new framework using evolutionary algorithms to find the most optimal features set and channels.Comprehensive experimental results based on two public datasets and one newly collected dataset. There is currently no standard or widely accepted subset of features to effectively classify different emotions based on electroencephalogram (EEG) signals. While combining all possible EEG features may improve the classification performance, it can lead to high dimensionality and worse performance due to redundancy and inefficiency. To solve the high-dimensionality problem, this paper proposes a new framework to automatically search for the optimal subset of EEG features using evolutionary computation (EC) algorithms. The proposed framework has been extensively evaluated using two public datasets (MAHNOB, DEAP) and a new dataset acquired with a mobile EEG sensor. The results confirm that EC algorithms can effectively support feature selection to identify the best EEG features and the best channels to maximize performance over a four-quadrant emotion classification problem. These findings are significant for informing future development of EEG-based emotion classification because low-cost mobile EEG sensors with fewer electrodes are becoming popular for many new applications.

Evolutionary computation algorithms for feature selection of EEG-based emotion recognition using mobile sensors

A sample entropy (SampEn)-based emotion recognition approach was presented. The SampEn results of notable EEG channels screened by K-S test were fed to the support vector machine (SVM)-weight classifier for training, after which it was applied to two emotion recognition tasks. One is to distinguish positive and negative emotion with high arousal and the other genitive emotion with different arousal status. Results showed that channels related to emotions were mostly located on the prefrontal region, i.e., F3, CP5, FP2, FZ, and FC2. And they were applied to form the input vectors of SVM-weight classifier. The accuracies of the present algorithm for the two tasks were 80.43% and 79.11%, respectively indicated by the leave-one-person-out validation procedure, demonstrating that the present algorithm had a reasonable generalization capability.

Emotion recognition based on the sample entropy of EEG.

A statistical based system for human emotions classification by using electroencephalogram (EEG) is proposed in this paper. The data used in this study is acquired using EEG and the emotions are elicited from six human subjects under the effect of emotion stimuli. This paper also proposed an emotion stimulation experiment using visual stimuli. From the EEG data, a total of six statistical features are computed and back-propagation neural network is applied for the classification of human emotions. In the experiment of classifying five types of emotions: Anger, Sad, Surprise, Happy, and Neutral. As result the overall classification rate as high as 95% is achieved.

/pdf/classification-of-human-emotions-from-eeg-signals-using-26h9lc0f3o.pdf

Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network

Problem statement: Template matching had been a conventional method for object detection especially facial features detection at t he early stage of face recognition research. The appearance of moustache and beard had affected the performance of features detection and face recognition system since ages ago. Approach: The proposed algorithm aimed to reduce the effect of beard and moustache for facial features detection a nd introduce facial features based template matching as the classification method. An automated algorithm for face recognition system based on detected facial features, iris and mouth had been d eveloped. First, the face region was located using skin color information. Next, the algorithm compute d the costs for each pair of iris candidates from intensity valleys as references for iris selection. As for mouth detection, color space method was use d to allocate lips region, image processing methods t o eliminate unwanted noises and corner detection technique to refine the exact location of mouth. Fi nally, template matching was used to classify faces based on the extracted features. Results: The proposed method had shown a better features detection rate (iris = 93.06%, mouth = 95.83%) than conventio nal method. Template matching had achieved a recognition rate of 86.11% with acceptable processi ng time (0.36 sec). Conclusion: The results indicate that the elimination of moustache and bear d has not affected the performance of facial featur es detection. The proposed features based template mat ching has significantly improved the processing time of this method in face recognition research.

/pdf/facial-features-for-template-matching-based-face-recognition-5g0lzowt34.pdf

Facial Features for Template Matching Based Face Recognition

This study proposes a statistical features-based classification system for human emotions by using Electroencephalogram (EEG) bio-sensors. A total of six statistical features are computed from the EEG data and Artificial Neural Network is applied for the classification of emotions. The system is trained and tested with the statistical features extracted from the psychological signals acquired under emotions stimulation experiments. The effectiveness of each statistical feature and combinations of statistical features in classifying different types of emotions has been studied and evaluated. In the experiment of classifying four main types of emotions: Anger, Sad, Happy and Neutral, the overall classification rate as high as 90% is achieved.

Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors

An algorithm to automatically detect facial features from color images has been developed. First, face region is located using skin-color information. Then, the iris candidates are extracted from the intensity valleys created from the detected face. Next, the costs for each pair of iris candidates are computed to determine the real pair of irises. Mouth region and corners are detected using color space method, image processing and corners detection techniques. This algorithm has been tested to specifically reduce the effects of beard, moustache, hairstyle and facial expression in automated facial features detection.

http://www.wseas.us/e-library/conferences/2008/uk/ISPRA/ispra-38.pdf

Woo San San

Papers

Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network

Facial Features for Template Matching Based Face Recognition

Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors

Automatic detection of face and facial features