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...

The electroencephalogram (EEG) is the most common tool used in sleep research. This unit describes the methods for recording and analyzing the EEG. Detailed protocols describe recorder calibration, electrode application, EEG recording, and computer EEG analysis with power spectral analysis. Computer digitization of an analog EEG signal is discussed, along with EEG filtering and the parameters o...

The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and...

Electroencephalogram (EEG) is an important clinical tool for diagnosing, monitoring, and managing neurological disorders related to epilepsy. Neural networks provide intriguing possibilities for the analysis of the EEG. In this paper we propose a neural network based system to detect epileptic activity. The system comprises of three main components: feature extraction, feature quantization and ...

Introduction: Mental arithmetic analysis based on Electroencephalogram (EEG) signal for monitoring the state of the user’s brain functioning can be helpful for understanding some psychological disorders such as attention deficit hyperactivity disorder, autism spectrum disorder, or dyscalculia where the difficulty in learning or understanding the arithmetic exists. Most mental arithmetic recogni...

Feature reduction is common in biosignal analysis, especially in case of quantitative EEG analysis. Mostly, summation in the spectral domain is applied to reduce the number of estimated power spectral density values, which gains between four and twelve band power values. Depending on the problem, on signals under analysis and on methods used for further processing it is an open question if such...

OBJECTIVE To investigate the reliability of several well-known quantitative EEG (qEEG) features in the elderly in the resting, eyes closed condition and study the effects of epoch length and channel derivations on reliability. METHODS Fifteen healthy adults, over 50 years of age, underwent 10 EEG recordings over a 2-month period. Various qEEG features derived from power spectral, coherence, e...

OBJECTIVE The objective of this study is to find the best set of characteristics of polysomnographic signals for the automatic classification of sleep stages. METHODS A selection was made from 74 measures, including linear spectral measures, interdependency measures, and nonlinear measures of complexity that were computed for the all-night polysomnographic recordings of 20 healthy subjects. T...

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to interand intra-subject differences, as well as to inherent noise associated with EEG data collection. Herein, we propose a novel approach for learning such representations from multichannel EEG time-series, and demonstrate its advantages in the context of ment...

In this paper, we present a novel epileptic seizure detection algorithm based on analysis of electroencephalogram (EEG) and electrocardiogram (ECG) signals to detect seizure onsets that are not associated with rhythmic EEG activity. In this algorithm, spectral and spatial features are extracted from seizure and non-seizure EEG signals by Gabor functions and combined with four extracted features...