EEG (electroencephalogram) has a lot of advantages compared to other methods in the analysis of Alzheimer’s disease such as diagnosing Alzheimer’s disease in an early stage. Traditional EEG analysis method needs a lot of artificial works such as calculating coherence between different pair of electrodes. In our work we applied deep learning network in the analysis of EEG data of Alzheimer’s dis...

While promising for many applications, Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are still scarcely used outside laboratories, due to a poor reliability. It is thus necessary to study and fix this reliability issue. Doing so requires to use appropriate reliability metrics to quantify both signal processing and user learning performances. So far, Classification Accuracy...

We propose an algorithm targeting the identification of more sources than channels for electroencephalography (EEG). Our overcomplete source identification algorithm, Cov-DL, leverages dictionary learning methods applied in the covariancedomain. Assuming that EEG sources are uncorrelated within moving time-windows and the scalp mixing is linear, the forward problem can be transferred to the cov...

Electroencephalography (EEG) has identified human brain potentials elicited by Artificial Grammar (AG) learning paradigms, which present participants with rule-based sequences of stimuli. Nonhuman animals are sensitive to certain AGs; therefore, evaluating which EEG Event Related Potentials (ERPs) are associated with AG learning in nonhuman animals could identify evolutionarily conserved proces...

The right and left hand Motor Imagery (MI) analysis based on the electroencephalogram (EEG) signal can directly link the central nervous system to a computer or a device. This study aims to identify a set of robust and nonlinear effective brain connectivity features quantified by transfer entropy (TE) to characterize the relationship between brain regions from EEG signals and create a hierarchi...

Knowledge tracing (KT) is widely used in Intelligent Tutoring Systems (ITS) to measure student learning. Inexpensive portable electroencephalography (EEG) devices are viable as a way to help detect a number of student mental states relevant to learning, e.g. engagement or attention. This paper reports a first attempt to improve KT estimates of the student’s hidden knowledge state by adding EEG-...

The involvement of different oscillating neuronal systems activated during intentional learning was investigated by measuring ongoing EEG activity. In 17 subjects, the EEG was recorded while learning pairs of words and faces. Subjective task difficulty was rated and a control condition of mental relaxation was also run. Spontaneous EEG activity during epochs which subsequently resulted in effic...

Recent EEG studies have shown that implicit learning involving specific cortical circuits results in an enduring local trace manifested as local changes in spectral power. Here we used a well characterized visual sequence learning task and high density-(hd-)EEG recording to determine whether also declarative learning leaves a post-task, local change in the resting state oscillatory activity in ...

OBJECTIVE 
 Most current Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) are based on machine learning algorithms. There is a large diversity of classifier types that are used in this field, as described in our 2007 review paper. Now, approximately 10 years after this review publication, many new algorithms have been developed and tested to classify EEG signals in BCIs. ...

abstract background and aims: paper-pencil tests have always its own problems in the mental disorders evaluation, including learning questions, bad or good blazon are the problems with this methodology. this study aimed to propose a new alternative method of measuring mental disorders without paper-pencil test using eeg. methods: the research society involved depressed patients referred the psy...