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Emotion recognition will be prosperious in multifarious applications, like distance education, healthcare, and human-computer interactions, etc. Emotions can be recognized from the behavior signals such as speech, facial expressions, gestures or the physiological signals such as electroencephalogram and electrocardiogram. Contrast to other methods, the physiological signals based emotion recognition can achieve more objective and effective results because it is almost impossible to be disguised. This paper introduces recent advancements in emotion research using physiological signals, specified to its emotion model, elicitation stimuli, feature extraction and classification methods. Finally the paper also discusses some research challenges and future developments.
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Humans , Electrocardiography , Electroencephalography , Emotions , Physiology , Facial Expression , Gestures , Models, TheoreticalABSTRACT
Fatigue is an exhaustion state caused by prolonged physical work and mental work, which can reduce working efficiency and even cause industrial accidents. Fatigue is a complex concept involving both physiological and psychological factors. Fatigue can cause a decline of concentration and work performance and induce chronic diseases. Prolonged fatigue may endanger life safety. In most of the scenarios, physical and mental workloads co-lead operator into fatigue state. Thus, it is very important to study the interaction influence and its neural mechanisms between physical and mental fatigues. This paper introduces recent progresses on the interaction effects and discusses some research challenges and future development directions. It is believed that mutual influence between physical fatigue and mental fatigue may occur in the central nervous system. Revealing the basal ganglia function and dopamine release may be important to explore the neural mechanisms between physical fatigue and mental fatigue. Future effort is to optimize fatigue models, to evaluate parameters and to explore the neural mechanisms so as to provide scientific basis and theoretical guidance for complex task designs and fatigue monitoring.
Subject(s)
Humans , Attention , Brain , Physiology , Fatigue , Mental Fatigue , WorkloadABSTRACT
Objective To explore the nonlinear complexity characteristics of electroencephalogram (EEG) in ischemic stroke patients with different course. Methods Sample entropy of all bands of EEG signals in 20 ischemic stroke patients and 10 healthy controls was extracted and analyzed using statistical analysis methods. Results The full-band EEG in sample entropy of stroke patients was significantly lower than that of healthy controls in most locations. Theα-band sample entropy of different course had significant differences in the frontal, temporal and occipital lobe (P<0.05), and the parameters had significant negative linear correlation with the post-stroke time in some locations. Conclusions There is an abnormal neural electrical activity in post-stroke patients. It is feasible to detect the aberrant EEG complexity using sample entropy, which is worth of further research.
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Objective To investigate the cognitive difference between uni-modal (V,A) and bi-modal (VA)target stimuli from both vision and audition,and then to study the neural mechanisms of bi-modal enhancement.Methods This experiment adopted a speeded target stimuli detection task, both behavioral and electroencephalographic responses to uni-modal and bi-modal target stimuli which were combined from visual and auditory target stimuli,were recorded from 14 normal subjects using a 64-channel EEG NeuroScan system.The differences of cognitive between uni-modal and bi-modal stimulus were tested from both behavioral (reaction time (RT) and error rate (ER)) and event-related potentials (ERPs) (P2 latency and amplitude,P3 latency and amplitude)data,and the correlation between behavioral and ERPs results were analyzed.Results As a result,the RT,ER and P3 latency has significant difference between uni-modal and bi-modal target stimuli.In addition,there were significant correlation between behavioral data and P3 latency,especially from the RT and P3 latency.Conclusion By comparing the difference between uni-modal and bi-modal from both behavioral and ERPs results,we could reached the conclusion that the neural mechanism of bi-modal target detection was predominant over that of vision and audition uni-modal target detection,the enhancement take place not only involved in early ERP components (such as P1 and N1),but engaged at the late ERP components (such as P2 and P3).
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Brain computer interface (BCI) is an information channel independent of routine brain output ways such as peripheral nerves and muscle organization. As a special human-computer interface mode, it provides a direct communication pathway between the brain and external devices so as to exert control over those devices by ways other than primitive human communication. Controlling over mobile peripheral devices such as intelligent wheelchairs or nursing robots is a very important application of BCI technology in the future. This paper describes the newest progress of the above mentioned technology, analyzes and compares key techniques involved, and forecasts future development in this field.
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Humans , Algorithms , Brain Diseases , Rehabilitation , Communication Aids for Disabled , Computer Systems , Electroencephalography , Evoked Potentials , Physiology , Neuromuscular Diseases , Rehabilitation , Signal Processing, Computer-Assisted , User-Computer InterfaceABSTRACT
Recognition by gait is a new field for the biometric recognition technology. Its aim is to recognize people and detect physiological, pathological and mental characters by their walk style. The use of gait as a biometric for human identification is promising. The technique of gait recognition, as an attractive research area of biomedical information detection, attracts more and more attention. In this paper is introduced a survey of the basic theory, existing gait recognition methods and potential prospects. The latest progress and key factors of research difficulties are analyzed, and future researches are envisaged.
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Humans , Algorithms , Artificial Intelligence , Biometry , Methods , Gait , Physiology , Models, Biological , Pattern Recognition, Automated , MethodsABSTRACT
Acoustic analysis is one of the important branches of biometric recognition technology widely used now. The mainly aim of the technology is to recognize the identity of person and judge the content of speech or diagnose the illness automatically according to the features extracted from the speaker's waveforms. All these features are related with the characteristics of speaker's physiological, pathological and psychological action. Speaker recognition study has its 50-year old history already, but acoustic analysis in diagnosing disease has been founded since 1970s. This paper introduces the main concept and research background of this diagnosing system generally and discusses the problems generated during processing. At last the prospect for the applications of acoustic analysis is forecasted.
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Humans , Pattern Recognition, Physiological , Signal Detection, Psychological , Speech , Physiology , Speech Acoustics , Speech Disorders , DiagnosisABSTRACT
This paper suggested a new method of spacial risk-trend trace (SRTT) to assess and monitor the spacial balance condition during paraplegic walking assisted by functional electrical stimulation (FES), which main component was a measurement system of upper limb support based on a standard walker. With the support data, the spacial positions of moving center of gravity could be located through the upper body mechanical model and, combining with the definition of walker rolling index, transmitted into SRTT to describe the balance conditions at different axial space. The experimental and clinical results demonstrated the new SRTT method was reliable and real-time. Its potential clinical usefulness in evaluating and monitoring FES-assisted paraplegic walking ability may provide the foundation to enact the relevant national rehabilitation criterions for effective FES usage.
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Humans , Electric Stimulation Therapy , Methods , Monitoring, Physiologic , Methods , Paraplegia , Rehabilitation , Postural Balance , Physiology , Self-Help Devices , Therapy, Computer-Assisted , Methods , Walking , PhysiologyABSTRACT
Paraplegia is a severe disability of lower limbs resulting from spinal cord injury. Moreover, its incidence has been climbing over the recent years. The most important symptom of paraplegia is the loss of walking ability. Involved researches during recent 40 years have shown that functional electrical stimulation, which could successfully regain some movement function for paraplegic patients, is a new and promising technique in modern rehabilitation engineering. It has been drawing more attention. Here, aiming at this functional electrical stimulation technique for paraplegic walking, we introduce its relevant background knowledge and research progress.
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Humans , Electric Stimulation Therapy , Paraplegia , Rehabilitation , WalkingABSTRACT
In order to preprocess mammograms for diagnosing the early cases of breast cancer and improving the computational efficiency in the computer-aided detection of micro-calcifications in mammograms, we have advanced a novel processing technique for the extraction of micro-calcification region of interest (MROI). The proposed method is based on a three-step procedure: (1) the mammogram is divided into sub-images of the same size; (2) the wavelet multi-resolution method is conducted on the sub-images, and the parameters related to wavelet transform and threshold T are discussed according to rho; (3) the classification of sub-images is determined by T. It is tested with 20 mammograms and the results show that the method can achieve a true positive rate as high as 89.7% with a false positive rate as low as 2.1%.
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Humans , Breast Diseases , Diagnostic Imaging , Pathology , Breast Neoplasms , Diagnostic Imaging , Pathology , Calcinosis , Diagnostic Imaging , Diagnosis, Computer-Assisted , MammographyABSTRACT
A new method is developed to detect vascular edge-line in coronary angiograms.It firstly searches the path through vessel segment of interest,then detects vascular edges along profiles perpendicular to the path.The experiments demonstrate that the method performs well in coronary angiograms with less nearby or overlapping structures and branching vessels.
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High intensity focused ultrasound (HIFU) has become a new developed micro-invasive surgical treatment of tumor, which is a hot topic in basic and applied research field. HIFU has shown its unique advantages including micro-invasion and curative effect during the treatment of several kinds of superficial tumor, e. g. mammary cancer. Based on the cellular thermo-necrotic theory, the expression of omega value was introduced to establish the mathematic model of the necrosed field formed in HIFU treatment progress. A non-interferential and self-focused system with multi-transducer was used to verify the theoretical model. Results of this study proved that the necrosed field mathematic model can simulate the actual heating progress. The model of this study may predict the practical value of the necrosed field as well as the time needed to take shape.
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Humans , Breast Neoplasms , Pathology , Therapeutics , Models, Biological , Necrosis , Neoplasms , Pathology , Therapeutics , Ultrasonic Therapy , Methods , Ultrasound, High-Intensity Focused, Transrectal , MethodsABSTRACT
Power noise is constantly found in EEG signals; thus the acquisition and analysis of EEG signals can be strongly influenced. Comparison of the efficiencies of four ICA algorithms (Fastica, Extended Infomax, EGLD, Pearson-ICA) and SVD methods in extracting power noise in the EEG signals showed that ICA algorithms appear insensitive to the noise disturbance, whereas the commonly used SVD method does not. By applying the Extended-Infomax ICA with better convergence in this paper, it was demonstrated that the power noise contained in the 16-channel EEG signals of one Alzheimer-disease patient were removed successfully(the lowest signal-noise-ratio for power noise is 0 dB). ICA has a possible important value and prospect in biomedical signal processing, especially in clinical medical engineering.
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Humans , Algorithms , Artifacts , Electroencephalography , Schizophrenia , Signal Processing, Computer-AssistedABSTRACT
Brain Computer Interface(BCI) is a direct information communication and control channel established between human and computer or other electronics devices and it is a wholly new communication system that does not depend on the brain's normal output pathways of peripheral nerves and muscles.The general constitutions and principles of BCI systems are introduced.In addition,research methods based on electroencephalograph are discussed and the existing problems and future trends of BCI are pointed out.
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Objective To explore a new method for assessing the walking efficiency of the paraplegic patients assisted by functional electrical stimulation (FES). Methods The measurement system based on a standard walker was developed. During FES assisted walking of the paraplegic, the real time of the upper limb support were obtained and transformed into a 3 D center of gravity (CG) motion map with a paraplegic upper body mechanical model to describe the CG motion locus. Then the FES efficiency indicated by walking balance condition was assessed objectively and quantitatively. Results In this design, the pilot study of a paraplegic patient undergoing walking training with FES showed that the force accuracy was better than 1.01%, nonlinearity was less than 0.8%, and crosstalk was less than 3.2%. Conclusion The results showed that this system may be used as 1) an evaluation index of FES assisted paraplegic walking efficiency, 2) a balance control indicator during FES assisted paraplegic walking training and 3) a feed back signal to choose an efficient FES pattern and sequence.
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A system design of brain-computer interface based on the alpha waves in human electroencephalography(EEG) is presented in this paper.With the effects on the alpha wave amplitudes of human eye's open and close involved in,the selection control of four direction targets can be performed on a computer screen.The system speed and accuracy rate are investigated through the experiments involving 5 subjects.It is shown that the system is easy to operate and needs no complex learning and biofeedback training.The studying results provide a good technical foundation for the development of BCI control panel and the realization of the system integration.It has the potential application for clinical engineering and is valuable for further research.
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This paper introduces a new method which can judge the degree of burn scar hypertrophy by analyzing chroma of the burn scar. Its technical schedule is as follows: Firstly, the image of the burn scar is captured by using a digital camera. Then the chroma emendation is performed by using an Artificial Neural Network(ANN). At last, the chroma of burn scar is analyzed and the classification of burn scar hypertrophy is given by using a Support Vector Machine(SVM). Compared with clinical evaluation, the result deduced from this method is proved to be effective.