Childhood bilingualism has long been associated with enhanced creative performance. The neural mechanisms underlying this phenomenon, however, have yet to be characterized. Research suggests bilingualism modifies neural networks for executive control. Such changes, which can be assessed with estimation of neural signal complexity, are thought to increase information processing capacity. We thus...

The study of the electroencephalographic (EEG) signal contributes to sleep analysis. In the microstructure of the sleep EEG signal, transient patterns are characterized by their frequency content and their time duration. The Time– Frequency Representations (TFR) take into account these time – frequency characteristics but the lower energy transient signals are masked by higher energy ones. In o...

Independent Component Analysis (ICA) has emerged as a necessary preprocessing step when analyzing Electroencephalographic (EEG) data. While many studies reported on the use of ICA for EEG, most of these studies rely on visual inspection of the signal to detect those components that need to be removed from the signal. Little has been done on how to process EEG data in real-time, autonomously, an...

This study is aimed to the comparison of the sensitivity of linear spectral asymmetry index (SASI) and nonlinear Higuchi’s fractal dimension (HFD) methods for detecting modulated microwave effect on human electroencephalographic (EEG) signal at non-thermal level of exposure. The experiments were carried out on a group of 14 healthy volunteers exposed to 450 MHz microwave radiation modulated at ...

We propose the doubly sparsifying network (DSN), by drawing inspirations from the double sparsity model for dictionary learning. DSN emphasizes the joint utilization of both the problem structure and the parameter structure. It simultaneously sparsifies the output features and the learned model parameters, under one unified framework. DSN enjoys intuitive model interpretation, compact model siz...

This paper presents a review of the state of the art regarding wheelchairs driven by a brain-computer interface. Using a brain-controlled wheelchair (BCW), disabled users could handle a wheelchair through their brain activity, granting autonomy to move through an experimental environment. A classification is established, based on the characteristics of the BCW, such as the type of electroenceph...

Fragmented sleep is commonly caused by arousals that can be detected with the observation of electroencephalographic (EEG) signals. As this is a time consuming task, automatization processes are required. A method using signal processing and machine learning models, for arousal detection, is presented. Relevant events are identified in the EEG signals and in the electromyography, during the sig...

Despite a substantial body of work comprising theoretical modeling, the effects of medial temporal lobe lesions, and electrophysiological signal analysis, the role of the hippocampus in recognition memory remains controversial. In particular, it is not known whether the hippocampus exclusively supports recollection or both recollection and familiarity--the two latent cognitive processes theoriz...

Independent Component Analysis (ICA) is a statistical based method, which goal is to find a linear transformation to apply to an observed multidimensional random vector such that its components become as statistically independent from each other as possible. Usually the Electroencephalographic (EEG) signal is hard to interpret and analyse since it is corrupted by some artifacts which originates...

Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or 'internal' noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required informati...