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Electroencephalography (EEG) is a major tool for clinical diagnosis of neurological diseases and brain research. EEGs are often collected over numerous channels and trials, providing large data sets that require efficient collection and accurate compression. Compressive sensing (CS) emphasizing signal sparseness enables the reconstruction of signals from a small set of measurements, at the expense...
Recently, information technology and microelectronics have enabled implanting miniature and highly intelligent devices within the brain for in-vitro diagnostic and therapeutic functions. Power and physical size constraints of these devices necessitate novel signal processing methods. In this paper we investigate an effective data acquisition and reconstruction method for brain implants based on asynchronous...
We extend the signal space separation (SSS) method to decompose multichannel magnetoencephalographic (MEG) data into regions of interest inside the head. It has been shown that the SSS method can transform MEG data into a signal component generated by neurobiological sources and a noise component generated by external sources outside the head. In this paper, we show that the signal component obtained...
Video transmission plays a critical role in robotic telesurgery because of the required high bandwidth and quality requirement. We propose an adaptive video preprocessing technique to accelerate the transmission of telesurgical video. Using this technique, the bandwidth can be reallocated adaptively from non-essential surrounding regions to the region of interest when preprocessed image sequences...
Electrical impedance tomography (EIT) is a new functional imaging technique. This paper presents the development of a new electrical impedance tomography system with 128 electrodes for impedance change detection and 3D imaging of the human thorax. The system consists of several modules, including multi-frequency current source, driving, measuring, data acquisition, and controlling and signal processing...
Currently, a significant obstacle in identifying the most important factors that contribute to obesity is the lack of appropriate tools to evaluate food consumption and physical activity on a daily basis. This paper presents a novel video-based approach to estimating the energy intake and expenditure of individuals who are over-weight or obese. We propose to utilize a miniature video camera to chronically...
We suggest an iterative method for the decomposition of MEG signals into some user-specified parts. It is based on a technique called morphological component analysis (MCA), which seeks sparse representations. A numerical simulation is carried out to reveal the performance characteristics of this method.
In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a...
The recently proposed signal space separation (SSS) method can transform the multichannel magnetic measurements of brain (MEG) into parts that correspond to inner sources and undesired external interferences. In this paper, we extend this method by decomposing the signal into deep and superficial regions. This is realized by manipulating the SSS coefficients using a scheme that exploits beamspace...
An epileptic seizure detector's performance definitely depends on features extraction and selection. In this study, we present the short-time average magnitude difference function (sAMDF) as a computationally efficient feature to distinguish seizures from EEG and it is compared with the frequently used curve length. We also suggest using a subspace based approach for feature selection that exploits...
In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a...
Evoked potentials are defined as potentials that result from electrical activity in the central nervous system after a stimulation. In analysis of evoked potentials the main problem is to extract the waveform from measurements that also contain on-going background electroencephalographic (EEG) activity. The conventional tool for the analysis of evoked potentials has been averaging of the measurements...
Many experiments demonstrate that the synchrony of neurons is a hallmark in epileptic seizure and the dynamical process of the epilepsy is complex with new oscillations born. In fact, epileptic seizure is very complicated relating to many factors so that it can't be understood thoroughly only in some special aspect. Based on the previous work on synchronous oscillations of electrically coupled abnormal...
The back-propagation neural network is utilized to classify sleep stages in humans. A single-channel EEG is segmented into equally spaced intervals, each interval corresponds to one-minute in time. Measurements of the time, frequency, and energy characteristics are carried out in each interval to construct the sleep pattern vector. An adaptive training algorithm is utilized to accelerate the training...
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