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Recently we have studied the effects of extremely low frequency pulsed electromagnetic fields (ELF-PEMF) on the human biosignals. Electrocardiogram (ECG) and electroencephalogram (EEG) of seventeen healthy volunteers before and after the electromagnetic (EMF) exposure were recorded and analyzed. The root mean square (RMS) values of the recoded data were considered as comparison criteria. EEG results...
Evoked potentials are usually embedded in the ongoing electroencephalogram with a very low signal-to-noise ratio. The neural network filtering technique which has the advantage of complex mapping is one of the applicable methods for evoked potentials estimation. The backpropagation algorithm based on second order statistics is commonly used to adapt neural network filters. However it is easily influenced...
This paper presented a method, termed MVCCDFD (multivariate coherence decomposition), for mapping coherent brain sources at given frequencies. By calculating averaged coherence over all pairs of channels, we can know at which frequencies there are strong coherence. And then, by utilizing MVCCDFD to corresponding frequencies we can get the 2D distributions of coherent sources at given frequencies....
A finite difference method (FDM) has been implemented to solve the electroencephalogram (EEG) forward problem. This method has been evaluated by means of computer simulations, by comparing with analytic solutions in a three-sphere concentric head model. The effects of dipole eccentricity, spacing of finite difference model and number of grid nodes on solution accuracy are also addressed in the simulations...
Electroencephalogram (EEG) might be the most predictive and reliable physiological indicator of mental fatigue. However, the extraction of key features from massive EEG data for mental fatigue identification remains a challenge. The objective of this study is to identify the key EEG features in relationship to mental fatigue, from a broad pool of EEG features generated by quantitative EEG (qEEG) techniques,...
The artifacts caused by various factors, EOG (electrooculogram), blink and EMG (electromyogram), in EEG (electroencephalogram) signals increase the difficulty in analyzing them. In addition, EEG signals containing artifacts often cannot be used in analyzing them. So, it is useful and indispensable to eliminate the artifacts from EEG signals. In this paper, a neural network with non-recursive IIR (infinite...
Blind source separation (BSS) methods such as independent component analysis (ICA) are increasingly being used in biomedical signal processing for decomposition of multivariate time-series, such as the multichannel electroencephalogram (EEG), into a set of underlying sources, some of which may reflect clinically relevant neurophysiological activity such as epileptic seizures or spikes. Tracking and...
This paper reports on the use of electroencephalogram (EEG)-based phase desynchronization networks for the recognition of imagined movements. Features derived solely from these networks are classified using linear support vector machine. An average accuracy of 73% is achieved for the single-trial imagined hand versus foot movements. The results demonstrate that phase desynchronizations provide relevant...
Nucleus accumbens is used to be considered as the interface to motor nerve system. In this paper, our object is to study the relationship between the electro-activity of neurons in nucleus accumbens and the rats' behavior. We recorded neurons action potentials with multichannel microelectrodes, which were chronically implanted in a rat's nucleus accumbens, during rats' chewing behavior. Through digital...
Most hospitals in the world do not perform electroencephalograms in the emergency departments due to space, cost, training, and complexity of the equipment and the test. New miniature, low-cost, simple, digital, wireless EEG machines have been developed that solve all four of these inhibiting factors to allow EEG, to be used in emergency departments to evaluate patients presenting with altered mental...
This paper illustrates the use of mixture of experts (ME) network structure to guide model selection for classification of electroencephalogram (EEG) signals. Expectation-maximization (EM) algorithm was used for training the ME so that the learning process is decoupled in a manner that fits well with the modular structure. The EEG signals were decomposed into time-frequency representations using discrete...
Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is inconvenient and time-consuming work. The purpose of this study was to develop an automatic algorithm to detect the arousal events. We proposed the automatic method to detect arousals based on time-frequency analysis and the support...
Non-negative matrix factorization (NMF) is an algorithm that is able to learn a parts-based representation. The paper proposes a new spontaneous EEG classification method for attention-related tasks. NMF was employed as feature extraction tool, which leads to more localized and sparse features than other two reference methods: power spectrum method and principal component analysis. With conventional...
In this paper, we propose a method to visualize fNIRS (functional near infrared spectroscopy) data on a realistic head model. In order to illustrate the success of this study, we used fNIRS data obtained during the Stroop task. For the realistic head model, high resolution T1 weighted MR images are used. Preliminary results show that proposed method will be useful for visualization of fNIRS data and...
The alpha wave (8~13 Hz) phase resetting from pre-stimulus to post-stimulus is investigated for eight healthy subjects in a visual and auditory synchronously oddball stimuli experiment. Six parameters are considered: pre-stimulus amplitude and phase angle, post-stimulus amplitude and latency of first positive and negative peak. The results show that the amplitude relationship between pre- and two...
The principal component analysis (PCA) is proposed as feature selection method in choosing a subset of channels for visual evoked potentials (VEP). The selected channels are to preserve as much information present as compared to the full set of 61 channels as possible. The method is applied to classify two categories of subjects: alcoholics and non-alcoholics. The electroencephalogram (EEG) was recorded...
About 25% epilepsy patients are suffering from medically intractable epileptic seizure. Many studies have shown that electroencephalogram (EEG) biofeedback therapy has the exciting potential for seizure control. In this paper, five patients with intractable epilepsy were trained to increase the production of sensorimotor (12~15 Hz) activity and decrease the production of slow theta (4~7 Hz) activity...
Electroencephalogram (EEG) reveals brain state, i.e. awake, sleep, rather than the content of thinking. We calculated fast/slow power spectrum ratio and approximate entropy on the EEG of three brain states, normal awake, sleep and coma. Based on the above EEG quantities, the EEG in different brain states can be clearly separated in the following way: (1) fast and complex for normal awake, (2) slow...
Evaluation of biomedical signals is important in the diagnosis of neurology diseases, such as dementia, in neurology through the use of electroencephalograms (EEG). While automated techniques exist for EEG analysis, it is likely that additional information can be extracted from EEG signal through the use of new methods. We describe a method for identifying inert region from EEG. This method uses EEG...
An auditory vigilance task (AVT) as a validation criterion for monitoring mental fatigue was proposed in this study. The biological basis of this task design is on the understanding that mental fatigue is a cortical deactivation. This AVT is simple to perform, free of learning curve and independent on acquired skills (aptitude, knowledge). The validity and sensitivity of this task was verified by...
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