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Emotion recognition using EEG signals has become a hot research topic in the last few years. This paper aims at providing a novel method for emotion recognition using less channels of frontal EEG signals. By employing the asymmetry theory of frontal brain, a new method fusing spatial and frequency features was presented, which only adopted two channels of frontal EEG signals at Fp1 and Fp2. In order...
Recently, Electroencephalogram (EEG) has become increasingly important in the role of psychiatric diagnosis and emotion recognition. However, many irrelevant features make it difficult to identify patterns accurately. Obtaining valid features from electroencephalogram can improve the classification and generalization performance. In this paper, an improved normalized mutual information feature selection...
Electroencephalogram (EEG) is a noninvasive method to record electrical activity of brain and it has been used extensively in research of brain function due to its high time resolution. However raw EEG is a mixture of signals, which contains noises such as Ocular Artifact (OA) that is irrelevant to the cognitive function of brain. To remove OAs from EEG, many methods have been proposed, such as Independent...
Automatic emotion recognition based on multi-channel neurophysiological signals, as a challenging pattern recognition task, is becoming an important computer-aided method for emotional disorder diagnoses in neurology and psychiatry. Traditional approaches require designing and extracting a range of features from single or multiple channel signals based on extensive domain knowledge. This may be an...
According to the World Health Organization, it is predicted that in 2020, depression will become the second largest illness threatening the health of mankind. In order to alleviate the worldwide damage caused by depression, a portable and accurate diagnosing technique is the most essential. This research uses three-electrode pervasive EEG collector to collect EEG data from Fp1, Fp2, and Fpz as locations...
It has been reported that chronic heroin intake induces changes in central nervous system of human brain; however, few studies investigate the carry-over adverse effects on brain after heroin withdrawal. In this work we examined the alpha rhythms of resting-state Electroencephalogram (EEG) signals to measure the neuroelectrical differences between the heroin addicts after heroin withdrawal and normal...
In this paper we propose a biometric solution for individual identification based on electroencephalography with classification using local probability centers. In our study, the electroencephalography signals of a subject are recorded from only one active channel Cz with eyes closed and without any external stimulations. The original signals are preprocessed by Haar wavelet transformation; then a...
Emotion is an important indicator of depressive conditions. Emotion recognition based on physiological signals such as electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) has gained significant attraction in healthcare domain research. Sharing of physiological signal data related to emotional response between different healthcare systems has the potential to benefit both laboratory-based...
With the development of the electronic health record, sharing biomedical and healthcare data among heterogeneous systems has great potential to benefit both clinical healthcare and scientific research. Standardizing the data presentation is the best solution to exchange them among heterogeneous systems. However, electroencephalogram (EEG) data has met problems since existing diverse data formats are...
Modeling and prediction of Electroencephalogram (EEG) signals is very important for Portable applications; EEG signals are however widely regarded as being chaotic in nature. An adaptive modeling technique that combines Discrete Wavelet Transformation (DWT) to predict contaminated EEG signals for removal of ocular artifacts (OAs) from EEG records is proposed as an effective a data processing tool...
In this research, we focus on detecting stress based on electroencephalogram (EEG) method. An experiment has been conducted with 59 subjects, the results show that three EEG features from Fpz point, LZ-complexity, alpha relative power and the ratio of alpha power to beta power, are effective respectively in the stress detection using K-Nearest-Neighbor classifier, however Naive Bayesian classifier...
With the booms of mobile communication, especially mobile smart phone, technologies to identify individuals for mobile security calls for some more strict requirements in user-friendly, real-time and ubiquitous aspects. In addition to traditional approaches (for example, password check), some advanced biometric methodologies have been applied in practice, such as fingerprint and iris based solutions,...
Attention recognition (AR) is an essential component in many applications, however the focus of current research into AR is on ‘face detection’, ‘eye center localization’ and ‘eye center tracking techniques’. This paper describes a research project conducted to investigate the use of electroencephalography (EEG) signals to extend the current approaches and enrich AR. EEG processing and classification...
There have been a number of research projects which have addressed depression, the focus often being on aspects of pharmacology and psychology. Relatively few of the investigations have tried to integrate depression and the related issues into a pervasive depression prevention system incorporating user-centered design. In this paper we propose an approach to provide relief for a user(s) depression...
Mental health care is becoming an increasing concern in home care projects. As an integral part of Telecare and Telehealth systems, portable EEG recording and real-time analysis are increasingly being used as non-intrusive monitoring techniques. In home environments without the supervision of a physician and absence of electromagnetic shielding, the raw EEG data, especially the most important alpha...
Nowadays, many people suffer from negative moods like sadness or anxiety. As an effective tool to relieve such moods, music therapy is widely embraced. Furthermore, researchers try to use newly developed bio-feedback technologies like electroencephalograms (EEG) to measure the effects of music therapy since it can reflect people's emotion sensitively and objectively. In this paper, we design a mobile...
Smart homes have been proposed for senior citizens aiming to improve their quality of life. In order to monitor the seniors ' sleep condition in smart homes to protect them from sleep disorder, an automatic sleep staging system is necessary. Furthermore, measure selection is a crucial step in an automatic sleep staging system. In this paper, we present three advanced combination schemes among 15 sleep...
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