Epilepsy seizure prediction with few-shot learning method.

Epileptic seizures prediction and timely alarms allow the patient to take effective and preventive actions. In this paper, a convolutional neural network (CNN) is proposed to diagnose the preictal period. Our goal is for those epileptic patients in whom seizures occur late and it is very challenging to record the preictal signal for them.

An EEG-Based Neuromarketing Approach for Analyzing the Preference of an Electric Car.

This study evaluates consumer preference from the perspective of neuroscience when a choice is made among a number of cars, one of which is an electric car. Consumer neuroscience contributes to a systematic understanding of the underlying information processing and cognitions involved in choosing or preferring a product. This study aims to evaluate whether neural measures, which were implicitly extracted from brain activities, can be reliable or consistent with self-reported measures such as preference or liking.

Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations.

An automated sleep staging based on analyzing long-range time correlations in EEG is proposed. These correlations, indicating time-scale invariant property or self-similarity at different time scales, are known to be salient dynamical characteristics of stage succession for a sleeping brain even when the subject suffers a destructive disorder such as Obstructive Sleep Apnea (OSA). The goal is to extract a set of complementary features from cerebral sources mapped onto the scalp electrodes or from a number of denoised EEG channels.

Modeling the interaction of navigational systems in a reward-based virtual navigation task.

Existence of allocentric and egocentric systems for human navigation, mediating spatial, and response learning, respectively, has so far been discussed. It is controversial whether navigational strategies and their underlying learning systems and, accordingly, the activation of their associated brain areas are independent/parallel or whether they functionally/causally interact in a competitive or in a cooperative manner to solve navigational tasks. The insights provided by neural networks involved in reward-based navigation attributed to individual involvement or interactions of learning systems have been surveyed.

Quantifying the dynamic of OSA brain using multifractal formalism: A novel measure for sleep fragmentation.

It is thought that the critical brain dynamics in sleep is modulated during frequent periods of wakefulness. This paper utilizes the capacity of EEG based scaling analysis to quantify sleep fragmentation in patients with obstructive sleep apnea. The scale-free (fractal) behavior refers to a state where no characteristic scale dominates the dynamics of the underlying process which is evident as long range correlations in a time series.

Pre-control characterization of hippocampal epileptic models.

This paper presents a dynamical characterization of epileptic seizures in animal models. Inter-hippocampal recordings of two animal models of seizures, kindling and pilocarpine, were analyzed by nonlinear analytic tools. The aim is to assess and differentiate pathophysiological states and behavioral phases of a status epilepticus.

On the discrimination of patho-physiological states in epilepsy by means of dynamical measures.

In the present paper a number of techniques were applied to determine the effects of epileptic seizure on spontaneous ongoing EEG. The idea is that seizure represents transitions of an epileptic brain from its normal (chaotic) state to an abnormal (more ordered) state. Some nonlinear measures including correlation dimension, maximum Lyapunov exponent and wavelet entropy and a graphical tool, named recurrence plot, as well as a novel technique that collects some statistics of the state space organization were used to characterize interictal, preictal and ictal states and derivate a phase transition.

Complexity evolution in epileptic seizure.

The present paper concentrates on neural complexity generated during the phase transition in epileptic seizure. Epileptic seizures represent a sudden and transient change in the synchronized firing of neuronal brain ensembles. The proposed model treats the brain as an excitable medium for the propagation of waves of electrical activity with a level of abstraction encompassing underlying variables into a number of discrete states.

Wavelet packet decomposition of a new filter -- based on underlying neural activity -- for ERP classification.

This paper introduces a wavelet packet algorithm based on a new wavelet like filter created by a neural mass model in place of wavelet. The hypothesis is that the performance of an ERP based BCI system can be improved by choosing an optimal wavelet derived from underlying mechanism of ERPs. The wavelet packet transform has been chosen for its generalization in comparison to wavelet.