The electroencephalogram (EEG) is a complex signal and an important brain state indicator (e.g. waking, sleep, seizure). Modern brain research is intimately linked to the feasibility to record the EEG and to its quantitative analysis. EEG spectral analysis (decomposing a signal into its constituent frequency components) is an important method to investigate brain activity. Basic principals of s...

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...

Since its discovery by Hans Berger in 1929, the electroencephalogram (EEG) has been the most utilized signal to clinically assess brain function. The enormous complexity of the EEG signal, both in time and space, should not surprise us since the EEG is a direct correlate of brain function. If the system to be probed is complex and our signal is a reliable descriptor of its function, then we can...

Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals. Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data...

We present a new method for separation of the rhythms of the electroencephalogram (EEG) signal. The proposed method is based on the Hilbert-Huang transform (HHT). The HHT consists two steps namely empirical mode decomposition (EMD) and the Hilbert transform (HT). The EMD decomposes EEG signal into set of narrow-band intrinsic mode functions (IMFs), and the Hilbert transformation of these IMFs p...

Analysis of Electroencephalograms (EEG) recordings is becoming an important research area. However, if the signal is contaminated with noises or artifacts then it could mislead the diagnosis result. Therefore, it is important to remove artifacts from the EEG signal. This paper presents a classification approach to detect ocular artifact in the EEG signal. The proposed approach combines several ...

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...

The paper presents a comprehensive survey on International system for EEG (Electroencephalography) signal acquisition. The paper also explored various neuro-imaging techniques and EEG based neurological phenomenon applied for the development of BCI systems extremely useful for able bodied and disabled people. From the survey it is concluded that P300 signal are the most appropriate signal for c...

Electroencephalogram (EEG) signals are having very small amplitudes and because of that they can be easily contaminated by different Artifacts. The presence of artifacts makes the analysis of EEG difficult for clinical evaluation. The major types of artifacts that affect the EEG are Power Line noise, eye movements, Electromyogram (EMG), and Electrocardiogram (ECG). Out of these artifacts Power ...

The automated segmentation of magnetic resonance (MR) images of the human head is an active area of research in the field of neuroimaging. The resulting segmentation yields a patient-specific labeling of individual tissues and makes possible quantitative characterization of these tissues (e.g. in the study of Alzheimers disease and multiple sclerosis). The segmentation is also useful for assign...