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Hypertension is one of the most common diseases nowadays, and it is important to monitor the blood pressure in a patient's daily life. The goal of this work is to design and develop an intelligent and easy-to-use digital sphygmomanometer for convenient personal use. Due to the high speed offered by Field-Programmable Gate Array (FPGA) and because FPGA overcomes the disadvantages of a single-chip microcomputer's...
Much of the neural activity at the network scale occurs in both telencephalic and mes- and diencephalic tissue. Non-invasive functional imaging such activity has historically been limited to functional magnetic resonance imaging (fMRI) experiments, which has a minimum temporal resolution of four seconds. In this paper, we describe the use of the recently developed exSSS signal processing method to...
Although recent advances in neuroscience, information technology and microelectronics have enabled implanting miniature and highly intelligent devices within the brain for in vitro diagnostic and therapeutic functions, novel signal processing methods are required for energy efficient data acquisition due to power constraints accompanying these miniature devices. We present a new method for signal...
We introduce a spatial filtering method in the spherical harmonics domain for constraining magnetoencephalographic (MEG) multichannel measurements to any user-specified spherical region of interest (ROI) inside the head. The method relies on a linear transformation of the signal space separation inner coefficients that represent the MEG signal generated by sources located inside the head. The spatial...
Brain-computer interfaces (BCIs) provide a way to monitor and treat neurological diseases. An important application of BCIs is the monitoring and treatment of epilepsy, a neurological disorder characterized by recurrent unprovoked seizures, symptomatic of abnormal, excessive or synchronous neuronal activity in the brain. BCIs contain an array of sensors that gather and transmit data under the constrains...
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...
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...
Interictal spikes are important indicators of epileptic focus (foci). The spiky events in EEG waveforms recorded from different regions of the brain provide important information about the dynamic transitions of epileptic activity. We extract sequences of spikes, called spike trains, from individual EEG channels and evaluate them using the stochastic cross-correlogram, where the time of occurrence...
Detection of the onset of seizure-activity in the EEG record of an epileptic patient is an important step in the localization of seizure foci. We employ the use of three multilayer recurrent neural-network architectures to detect seizure-activity onset in multichannel subdural EEG (SEEG) data. Representing each architecture as (input layer-hidden layer-output layer), the three neural networks examined...
Event-related potentials (ERP) are brain signals in response to infrequent stimuli applied to a subject. These signals are usually small in amplitude and are embedded in background electroencephalographic (EEG) activity. To analyze them the most common method used is to perform a simple averaging of time aligned ERP segments. However, this method assumes that the ERP signal will not change from segment...
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