The organization of behavior: A neuropsychological theory

Antimicrobial nanomaterials for water disinfection and microbial control: Potential applications and implications

The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation and Treatment of High Blood Pressure

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

/pdf/review-on-solving-the-inverse-problem-in-eeg-source-analysis-2j79a4u8wn.pdf

Review on solving the inverse problem in EEG source analysis

Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mutual information (PMI) in scalp channel pairs, the remaining PMI in component pairs, the overall mutual information reduction (MIR) effected by each decomposition, and decomposition 'dipolarity' defined as the number of component scalp maps matching the projection of a single equivalent dipole with less than a given residual variance. The least well-performing algorithm was principal component analysis (PCA); best performing were AMICA and other likelihood/mutual information based ICA methods. Though these and other commonly-used decomposition methods returned many similar components, across 18 ICA/BSS algorithms mean dipolarity varied linearly with both MIR and with PMI remaining between the resulting component time courses, a result compatible with an interpretation of many maximally independent EEG components as being volume-conducted projections of partially-synchronous local cortical field activity within single compact cortical domains. To encourage further method comparisons, the data and software used to prepare the results have been made available (http://sccn.ucsd.edu/wiki/BSSComparison).

/pdf/independent-eeg-sources-are-dipolar-44fcepkmlv.pdf

Independent EEG sources are dipolar.

Analysis of Attraction in the Magnetic Attachment for Dental Prosthesis by Finite Element Method

Dynamics of blood flow in vivo vary with cardiovascular disease. To examine dynamics change of blood flow is useful in the prevention of cardiovascular disease. This study is aimed to investigate the common carotid blood velocity (CCBV) in the cardiac patients. It was performed in 14 cardiac patients (mean ± SD age 68.5 ± 8.3) and 8 healthy subjects as control (age 22.8 ± 1.6). The variations of CCBV were measured using a telemetry blood flow velocimeter system in real time display that was completely developed in our laboratory. This measurement system is based on continuous wave (CW) Doppler ultrasound using two transducers which one is for transmitting and the other one is for receiving. In the report, data were measured for 90 seconds at the rest sitting posture with synchronized measurement of electrocardiogram (ECG) signal. Electromechanical delay (Δt) between ECG signal and CCBV were measured from time of R-wave (Tr) to time of the peak systolic blood velocity (T1). The Δt was 0.169 ± 0.008 s in the healthy subjects. While, the Δt was 0.131 ± 0.016 s in the hypertensive. The delay decreased relatively in the hypertensive. It was coincided with decreases of the magnitude of the peak of diastolic velocities. Furthermore, an acceleration index (AI) and a peak systolic velocity (S1) had correlation in the cardiac patient. The value of AI and S1 decreased particularly. The results suggest that the Δt is affected by arterial properties that may be related to arteriosclerosis.

Measurement and Analysis of Carotid Blood Flow and ECG in Cardiac

Environmental stimulation, e.g., sound, light and temperature, may produce biological signal sources in the brain, which show autonomous activities without the stimuli. The properties of the source are identified by using BP neural networks. Auditory sources and a circadian source are identified as an example from measured data. This method may be very useful for analyzing brain functions and medical diagnoses.

Identification of biological sources by neural networks

Department of Preventive Environment and Nutrition, Institute of Health Biosciences, 3-18-15, Kuramoto-cho, Tokushima city, Tokushima 770-8503, Japan Department of Electrical and Electronic Engineering, Institute of Socio Techno Sciences, 2-1, Miname Jyosanjima-cho, Tokushima city, 770-8506, Japan The University of Tokushima Graduate School Corresponding author: Akira Takahashi, M.D., Ph.D., Department of Preventive Environment and Nutrition, Institute of Health Biosciences, The University of Tokushima Graduate School, Kuromoto-cho 3-18-15, Tokushima City, Tokushima 770-8503, Japan; Phone: (81) 88-633-9428. Fax: (81) 88-633-7002; E-mail: akiratak@nutr.med.tokushimau.ac.jp

UVA-LED Air Disinfection

It is important in various fields of industrial instrumentation and bioinstrumentation to measure position and direction in dimension of object. It is preferable that bioinstrumentation is non-contact. But technique for making measurement handy and highly accurate is a little when measurement thing exists in the part of in vivo and is hidden. Because body tissue is percolation to magnetic field, it seems that method of using magnetic field is the most suitable for such a motion measurement. In this research, magnetic motion measurement system that uses magnetic field is examined. Mandibular motion measurement that uses magnetic field is examined as an application example of applying to these conditions. Because jawbone has been covered with skin, measurement is difficult. But mandibular motion can be measured because magnetic field is percolation to body tissue. Here, magnetic flux density can be measured from position and direction of magnet. But, problem to calculate position and direction of magnet from magnetic flux density is a kind of a reverseproblem. Generally, this problem cannot be solved. This reverse problem can be solved by BPNN. Therefore, position and direction of magnet can be presumed from magnetic flux density of magnet by using BPNN. BPNN is abbreviation of Back Propagation Neural Network. BPNN can approximate a complex nonlinear functions. Input of BPNN is magnetic flux density distribution, and output is a position and direction of magnet. Input-output relation of neural network can be trained by using training patterns of a lot of limited numbers.

Magnetic motion measurement using BPNN

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