In neuroscience, phase is assumed to contain more important information about the neural activity than amplitude. However, the most exploited feature in electroencephalogram (EEG) based brain computer interface (BCI) is the amplitude change, phase has been largely ignored, and only phase locking values (PLV) has been introduced in EEG classification recently. In this paper, we define phase inte...

Objective in this paper, we have done Mahalanobis Discriminant analysis to EEG data of experiment objects which are recorded impersonally come up with a relatively accurate method used in feature extraction and classification decisions. Methods In accordance with the strength of  wave, the head electrodes are divided into four species. In use of part of 21 electrodes EEG data of 63 people, we ...

Epilepsy is the second most common serious brain disorder after stroke. Worldwide, at least 40 million people or 1% of population currently suffer from epilepsy. Approximately 25-30% of epileptic patients remain unresponsive to antiepileptic drug treatment, which is the standard therapy for epilepsy. There is a growing interest in predicting epileptic seizures using intracranial electroencephal...

The neurons in the brain show different firing characteristics during different motor actions and also during motor imagery. The present study tries to observe and classify the variation in neural activity during a motor action and its imagination. Electroencephalogram (EEG) signals from 90 different subjects were used for the same. The data included 3 trials – a base case with eyes opened, a m...

Automatic epileptic seizure detection has important research significance in clinical medicine. Feature extraction method for epileptic EEG occupies core position in detection algorithm, since it seriously affects the performance of algorithm. In this paper, we propose a novel epileptic EEG feature extraction method based on the statistical property of complex networks theory. EEG signal is fir...

Motor imagery classification provides an important basis for designing Brain Machine Interfaces [BMI]. A BMI captures and decodes brain EEG signals and transforms human thought into actions. The ability of an individual to control his EEG through imaginary mental tasks enables him to control devices through the BMI. This paper presents a method to design a four state BMI using EEG signals recor...

Electroencephalogram (EEG) recording systems have been frequently used as the sources of information in diagnosis of epilepsy by several researchers. In this study, rearranged EEG signals were classified by Multilayer Perceptron -eural -etwork (MLP--) model. Used data consists of five groups (A, B, C, D, and E) each containing 100 EEG segments. In this study, center points with equal interval w...

Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated i...

Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...

brain-computer interface systems are a new mode of communication which provides a new path between brain and its surrounding by processing eeg signals measured in different mental states.  therefore, choosing suitable features is demanded for a good bci communication. in this regard, one of the points to be considered is feature vector dimensionality. we present a method of feature reduction us...