Electroencephalogram (EEG) signal classification is used in many applications. Typically, this implemented based on methods which consist of two steps. These steps are known as the step preprocessing and classification. The transforms initial into attributes. According to several studies, transformation can result loss some useful information and, consequently, formed attributes uncertain. This...

Purpose: The aim of this study was to characterize prolonged standing and its effect on postural control in elderly individuals in comparison to adults.Materials and Methods: The elderly individuals’ behavior during standing and how demanding such a task is for them, is still unknown. We recorded the center of pressure (COP) position of 12 elder and 15 young participants while they were standin...

Introduction Automatic human emotion recognition is one of the most interesting topics in the field of affective computing. However, development of a reliable approach with a reasonable recognition rate is a challenging task. The main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (HRV). In t...

background: various techniques are used in diagnosing cardiac diseases. the electrocardiogram is one of these tools in common use. in this study vectorcardiogram ) vcg ( signals are used as a tool for detection of cardiac ischemia. methods: vcg signals used in this study were obtained form 60 patients suspected to have ischemia disease and 10 normal candidates. verification of the ischemia had ...

In this paper, application of sparse representation (factorization) of signals over an overcomplete basis (dictionary) for signal classification is discussed. Searching for the sparse representation of a signal over an overcomplete dictionary is achieved by optimizing an objective function that includes two terms: one that measures the signal reconstruction error and another that measures the s...

In this position paper we describe the research scope, past and recent results, engineering challenges and the promising approaches the authors are investigating in their work on psychophysiological signal analysis and classification applied to automatic affect recognition, HCI, artistic performance and emotion research.

Audio signal classification consists of extracting physical and perceptual features from a sound, and of using these features to identify into which of a set of classes the sound is most likely to fit. The feature extraction and classification algorithms used can be quite diverse depending on the classification domain of the application. This paper presents an overview of the current state of t...

The Signal Transduction Classification Database (STCDB) is a database of information relative to the classification of signal transduction. It is based primarily on a proposed classification of signal transduction and it describes each type of characterized signal transduction for which a unique ST number has been provided. This document presents, in its first version, the classification of sig...

Previous works have resulted in some practical achievements for mechanomyogram (MMG) to control powered prostheses. This work presents the investigation of classifying the hand motion using MMG signals for multifunctional prosthetic control. MMG is thought to reflect the intrinsic mechanical activity of muscle from the lateral oscillations of fibers during contraction. However, external mechani...