Electroencephalography (EEG) has been widely used in the research of stress detection recent years; yet, how to analyze an EEG is important issue for upgrading accuracy detection. This study aims collect table tennis players by a test and it with machine learning identify models optimal accuracy. The methods are collecting using Stroop color word mental arithmetic, extracting features data prep...

We use a machine-learning approach to extend existing averaging-based ERP research on brain representations of tonal expectation, particularly for cadential events. We introduce pertinent vocabulary and methodology, and then demonstrate the use of machine learning in a classification task on single trials of EEG in a tonal expectation paradigm. EEG was recorded while participants listened to tw...

Recently, many lines of investigation in neuroscience and statistical physics have converged to raise the hypothesis that the underlying pattern of neuronal activation which results in electroencephalography (EEG) signals is nonlinear, with self-affine dynamics, while scalp-recorded EEG signals themselves are nonstationary. Therefore, traditional methods of EEG analysis may miss many properties...

A novel approach is proposed for Electroencephalogram signal classification using Artificial Neural Network based on Independent Component Analysis and Short Time Fourier Transform. The source EEG signals contain the electrical activity of the brain produced in the background by the cerebral cortex nerve cells. EEG is one of the most utilized methods for effective analysis of the brain function...

In this study, detection of epileptic seizure has been done using EEG. EEG signal has been decomposed using wavelet transform. After that, features of signal like entropy, variance, maximum value and minimum value of the signal have been calculated. These feature are given to kNN classifier for classification. The accuracy between ICTAL and normal EEG signal (open eye) has been calculated as 10...

In this paper we present a method for continuous EEG classification, where we employ nonnegative tensor factorization (NTF) to determine discriminative spectral features and use the Viterbi algorithm to continuously classify multiple mental tasks. This is an extension of our previous work on the use of nonnegative matrix factorization (NMF) for EEG classification. Numerical experiments with two...

Recent electrophysiological studies support command-specific changes in the electroencephalography (EEG) that have promoted their intensive application in the noninvasive brain computer interfaces (BCI). However, EEG is plagued by a variety of interferences and noises, thereby demanding better accuracy and stability for its application in the neuroprosthetic devices. Here we investigate wavelet...

Neurologists typically study the brain activity through acquired biomarker signals such as Electroencephalograms (EEGs) which have been widely used to capture the interactions between neurons or groups of neurons. Detecting and identifying the abnormal patterns through visual inspection of EEG signals are extremely challenging and require constant attention for well trained and experienced spec...

The paper proposes an approach based on higher order statistics and phase synchronization for detection classification of relevant features in electroencephalographic (EEG) signals recorded during the subjects are performing motor tasks. method was tested two different datasets performance evaluated using k nearest neighbor classifier. results (classification rates than 90%) have shown that can...

Person identification based on spectral information extracted from the EEG is addressed in this work a problem that has not yet been seen in a signal processing framework. Spectral features are extracted non-parametrically from real EEG data recorded from healthy individuals. Neural network classification is applied on these features using a Learning Vector Quantizer in an attempt to experiment...