Like/dislike analysis using EEG: determination of most discriminative channels and frequencies.

In this study, we have analyzed electroencephalography (EEG) signals to investigate the following issues, (i) which frequencies and EEG channels could be relatively better indicators of preference (like or dislike decisions) of consumer products, (ii) timing characteristic of "like" decisions during such mental processes. For this purpose, we have obtained multichannel EEG recordings from 15 subjects, during total of 16 epochs of 10 s long, while they were presented with some shoe photographs. When they liked a specific shoe, they pressed on a button and marked the time of this activity and the particular epoch was labeled as a LIKE case.

Effect of chewing on dental patients with total denture: an experimental study.

In this study, we have explored the prospect of assessing and following level of total denture adaptation by use of EMG signals recorded during gum chewing. Total of 14 edentulous patients, 6 women and 8 men, with an average age of 63±9 years, were recruited. Separate EMG recordings were obtained from left and right temporalis and masseter muscles of the patients for a period of 10 seconds, while they were chewing a sugar-free gum on their left and right sides.

Analysis of coronary angiography related psychophysiological responses.

Background: Coronary angiography is an important tool in diagnosis of cardiovascular diseases. However, it is the administration is relatively stressful and emotionally traumatic for the subjects. The aim of this study is to evaluate psychophysiological responses induced by the coronary angiography instead of subjective methods such as a questionnaire.

Classification of root canal microorganisms using electronic-nose and discriminant analysis.

Background: Root canal treatment is a debridement process which disrupts and removes entire microorganisms from the root canal system. Identification of microorganisms may help clinicians decide on treatment alternatives such as using different irrigants, intracanal medicaments and antibiotics. However, the difficulty in cultivation and the complexity in isolation of predominant anaerobic microorganisms make clinicians resort to empirical medical treatments.

Sleep stage and obstructive apneaic epoch classification using single-lead ECG.

Background: Polysomnography (PSG) is used to define physiological sleep and different physiological sleep stages, to assess sleep quality and diagnose many types of sleep disorders such as obstructive sleep apnea. However, PSG requires not only the connection of various sensors and electrodes to the subject but also spending the night in a bed that is different from the subject's own bed. This study is designed to investigate the feasibility of automatic classification of sleep stages and obstructive apneaic epochs using only the features derived from a single-lead electrocardiography (ECG) signal.

Gene expression profile class prediction using linear Bayesian classifiers.

Due to recent advances in DNA microarray technology, using gene expression profiles, diagnostic category of tissue samples can be predicted with high accuracy. In this study, we discuss shortcomings of some existing gene expression profile classification methods and propose a new approach based on linear Bayesian classifiers. In our approach, we first construct gene-level linear classifiers to identify genes that provide high class-prediction accuracies, i.

Determining a continuous marker for sleep depth.

Detection and quantification of sleep arousals is an important issue, as the frequent arousals are known to reduce the quality of sleep and cause daytime sleepiness. In typical sleep staging, electroencephalograph (EEG) is the core signal and based on the visual inspection of the frequency content of EEG, non-rapid eye movement sleep is staged into four somewhat rough categories. In this study, we aimed at developing a continuous marker based on a more rigorous spectral analysis of EEG to measure or quantify the depth of sleep.

Feedback network controls photoreceptor output at the layer of first visual synapses in Drosophila.

At the layer of first visual synapses, information from photoreceptors is processed and transmitted towards the brain. In fly compound eye, output from photoreceptors (R1-R6) that share the same visual field is pooled and transmitted via histaminergic synapses to two classes of interneuron, large monopolar cells (LMCs) and amacrine cells (ACs). The interneurons also feed back to photoreceptor terminals via numerous ligand-gated synapses, yet the significance of these connections has remained a mystery.

Segmentation of cDNA microarray spots using markov random field modeling.

Motivation: Spot segmentation is a critical step in microarray gene expression data analysis. Therefore, the performance of segmentation may substantially affect the results of subsequent stages of the analysis, such as the detection of differentially expressed genes. Several methods have been developed to segment microarray spots from the surrounding background.

Use of meixner functions in estimation of Volterra kernels of nonlinear systems with delay.

Volterra series representation of nonlinear systems is a mathematical analysis tool that has been successfully applied in many areas of biological sciences, especially in the area of modeling of hemodynamic response. In this study, we explored the possibility of using discrete time Meixner basis functions (MBFs) in estimating Volterra kernels of nonlinear systems. The problem of estimation of Volterra kernels can be formulated as a multiple regression problem and solved using least squares estimation.

Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods.

Motivation: A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data.

Assessment of reliability of microarray data and estimation of signal thresholds using mixture modeling.

DNA microarray is an important tool for the study of gene activities but the resultant data consisting of thousands of points are error-prone. A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks.

Assessment of closed-loop ventilatory stability in obstructive sleep apnea.

Previous studies on ventilatory control in obstructive sleep apnea (OSA) have generally indicated depressed chemosensitivity, implying greater stability of the chemical control of breathing in these subjects. However, these results were based on tests involving steady-state or quasi-steady measurements obtained in wakefulness. We have developed a method for assessing the dynamic stability characteristics of chemoreflex control in OSA patients during sleep.