Epilepsy is a common neurological disorder that is characterized by the recurrence of seizures. Electroencephalogram (EEG) signals are widely used to diagnose seizures. Because of the non-linear and dynamic nature of the EEG signals, it is difficult to effectively decipher the subtle changes in these signals by visual inspection and by using linear techniques. Therefore, non-linear methods are ...

Computer-aided diagnosis of neural diseases from EEG signals (or other physiological signals that can be treated as time series, e.g., MEG) is an emerging field that has gained much attention in past years. Extracting features is a key component in the analysis of EEG signals. In our previous works, we have implemented many EEG feature extraction functions in the Python programming language. As...

Brain Computer Interface (BCI) is a robot/machine communication based on brain activity. The main purpose of BCIs are facilitate the living of disables which has hearing, vision or mobility disabilities. The first and most basic step of the implementation of this system is obtain the recordings of brain signals known as Electroencephalography (EEG). In this study, we aimed to obtain regular EEG...

Electroencephalograph (EEG) signals feature extraction and processing is one of the most difficult and important part in the brain-computer interface (BCI) research field. EEG signals are generally unstable, complex and have low signal-noise ratio, which is difficult to be analyzed and processed. To solve this problem, this paper disassembles EEG signals with the empirical mode decomposition (E...

Assessing the stability of biological system models has aided in uncovering a plethora new insights genetics, neuroscience, and medicine. In this paper, we focus on analyzing neurological signals, including electroencephalogram (EEG) signals. Interestingly, spatiotemporal discrete-time linear fractional-order systems (DTLFOS) have been shown to accurately efficiently represent variety physiolog...

This research project is concerned with the use of mathematical techniques for processing EEG signals associated with varying states of alertness. It requires the development and implementation of advanced signal modeling and data processing techniques; especially designed for the representation and prediction of bioelectric signals useful as estimators of states of alertness. In particular, ou...

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...

Although electroencephalography (EEG) is widely used as a non-invasive technique for recording neural activities of the brain, our understanding of the neurogenesis of EEG is still very limited. Local field potentials (LFPs) recorded via a multi-laminar microelectrode can provide a more detailed account of simultaneous neural activity across different cortical layers in the neocortex, but the t...

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with...