Introduction: Clinicians use several computer-aided diagnostic systems for depression to authorize their diagnosis. An electroencephalogram  (EEG) may be used as an objective tool for early diagnosis of depression and controlling it from reaching a severe and permanent state. However, artifact contamination reduces the accuracy in EEG signal processing systems. Methods: This work proposes a no...

Feature extraction and classification of EEG signals is core issues on EEG-based brain computer interface (BCI). Typically, such classification has been performed using signals from a set of selected EEG sensors. Because EEG sensor signals are mixtures of effective signals and noise, which has low signal-tonoise ratio, motor imagery EEG signals can be difficult to classification. In this paper,...

Electroencephalogram (EEG) pattern classification plays an important role in the domain of brain computer interface (BCI). Hidden Markov model (HMM) might be a useful tool in EEG pattern classification since EEG data is a multivariate time series data which contains noise and artifacts. In this paper we present methods for EEG pattern classification which jointly employ principal component anal...

The article describes the classification of simple movements using a system based on Hidden Markov Models (HMM). Brisk extensions and flexions of the index finger, and movements of the proximal arm (shoulder) and distal arm (finger) were classified using scalp EEG signals. The aim of our study was to develop a system for the classification of movements which show EEG changes at identical scalp ...

In this paper, we present a new method based on empirical mode decomposition (EMD) for classification of seizure and seizure-free EEG signals. The EMD method decomposes the EEG signal into a set of narrow-band amplitude and frequency modulated (AM-FM) components known as intrinsic mode functions (IMFs). The method proposes the use of the area parameter and mean frequency estimation of IMFs in t...

Objective in this paper, we have done Fisher discriminant analysis to Electroencephalogram (EEG) data of experiment objects which are recorded impersonally, come up with a relatively accurate method used in feature extraction and classification decisions. The present study is the groundwork analysis for other analysis in EEG study. Methods In accordance with the strength of  wave, the head ele...

Classification of electroencephalograph (EEG) signals is the common denominator in EEG-based recognition systems that are relevant to many applications ranging from medical diagnosis to EEGcontrolled devices such as web browsers or typing tools for paralyzed patients. Here, we propose a new method for the classification of EEG signals. One of its core components projects EEG signals into a vect...

Epilepsy is the most common brain diseases that cause many problems in the daily life of the patient. In most attempts to automatic detection, the attack used an EEG. In this paper, The complete data set consists of five sets recorded from normal and epileptic patients. Each set containing 100 single-channel EEG segments. Here we used first and last sets (A and E). Set A consisted of segments r...