The electroencephalogram (EEG) signal plays an important role in the detection of epilepsy. The EEG recordings of the ambulatory recording systems generate very lengthy data and the detection of the epileptic activity requires a timeconsuming analysis of the entire length of the EEG data by an expert. The aim of this work is to develop a new method for automatic detection of EEG patterns using ...

The EEG state classifier distinguishes different states and these information are used to understand the normal and abnormal states of users and to adapt their interfaces and add new functionalities. EEG classification is performed conventionally by extracting statistical parameters. But, this classification is affected more by artifacts and hence a better approach using image based is proposed...

A BCI system, using orthogonalized EEG data sets and multiple multilayer neural networks (MLNNs) in a parallel form, is proposed. In order to emphasize feature of multi-channel EEG data, Gram-Schmidt orthogonalization has been applied. Since there are many channel orders to be orthogonalized, many kinds of orthogonalized data sets can be generated for the same EEG data set by changing the chann...

In this paper Empirical Mode Decomposition (EMD)-based features from single-channel electroencephalographic (EEG) data are proposed for rat’s sleep state classification. The classification performances of the EMD-based features and some classical power spectrum density (PSD)-based features are compared. Supported by experiments on real EEG data, we demonstrate that classification performances c...

The electroencephalograph (EEG) signal is one of the most widely signal used in the bioinformatics field due to its rich information about human tasks. In this work EEG waves classification is achieved using the Discrete Wavelet Transform DWT with Fast Fourier Transform (FFT) by adopting the normalized EEG data. The DWT is used as a classifier of the EEG wave’s frequencies, while FFT is impleme...

OBJECTIVE Many researchers have studied automatic EEG classification and recently a lot of work has been done on artefact-removal from EEG data using independent component analyses (ICA). However, demonstrating that a ICA-processed multichannel EEG measurement becomes more interpretable compared to the raw data (as is usually done in work on ICA-processing of EEG data) does not yet prove that d...

Although it ranks among the oldest tools in neuroscientific research, electroencephalography (EEG) still forms the method of choice in a wide variety of clinical and research applications. In the context of brain-computer interfacing (BCI), EEG recently has become a tool to enhance human-machine interaction. EEG could be employed in a wider range of environments, especially for the use of BCI s...

This paper analyzes the underlying complexity and non-linearity of electroencephalogram (EEG) signals by computing a novel multi-scale entropy measure for the classification of seizure, seizure-free and normal EEG signals. The quality factor (Q) based multi-scale entropy measure is proposed to compute the entropy of the EEG signal in different frequency-bands of interest. The Q -based entropy (...

In this paper, we analyze the performance of Time Delay Neural Networks (TDNN) and Hidden Markov Models (HMM) for Electroencephalogram (EEG) signal classification. The specific focus of this study is Brain-Computer Interfacing (BCI), where near-real time detection of mental commands during a multi-channel EEG recording is desired. We argue that HMM and TDNN should be preferred over the rigid, o...

OF THESIS DIMENSIONALITY REDUCTION AND CLASSIFICATION OF TIME EMBEDDED EEG SIGNALS Electroencephalogram (EEG) is the measurement of the electrical activity of the brain measured by placing electrodes on the scalp. These EEG signals give the micro-voltage difference between different parts of the brain in a non-invasive manner. The brain activity measured in this way is being currently analyzed ...