Given the frequency of naming errors in aphasia, a common aim of speech and language rehabilitation is the improvement of naming. Based on evidence of significant word recall improvements in patients with memory impairments, errorless learning methods have been successfully applied to naming therapy in aphasia; however, other evidence suggests that although errorless learning can lead to better...

Flow is an optimal experience resulting in intense engagement in the activity. People achieved flow state when they perceived balance between challenge of the activity and their skill to the activity. The concept of flow can be used to explore students’ learning performance in e-learning environment. The current research aims to empirically explore the influence of challenge-skill balance on th...

The Recent modern techniques, communication between humans and computers is proven a tremendous achievement in the field medical science. Computer hardware and signal processing have made possible the use of EEG signals or “brain waves” for Human-computer communication. Electroencephalography (EEG) is the electrical activity recording along the scalp. EEG refers to the recording of the spontane...

This paper presents a first step in the development of a methodology to compare the ability of different sonifications to convey the fine temporal detail of the Electroencephalography (EEG) brainwave signal in real time. In EEG neurofeedback a person‟s EEG activity is monitored and presented back to them, to help them to learn how to modify their brain activity. Learning theory suggests that th...

There is considerable evidence to support the hypothesis of relationship between paradoxical sleep (PS) and learning–memory processing. It has been suggested that PS is important in memory retention at the specific time course called PS windows (PSW). The time of PSWs occurrence and duration of these PSWs following the training sessions and, the neurochemical nature of PSWs has not been well kn...

We use a machine-learning approach to extend existing averaging-based ERP research on brain representations of tonal expectation, particularly for cadential events. We introduce pertinent vocabulary and methodology, and then demonstrate the use of machine learning in a classification task on single trials of EEG in a tonal expectation paradigm. EEG was recorded while participants listened to tw...

OBJECTIVE Research in seizure prediction from intracranial EEG has highlighted the usefulness of bivariate measures of brainwave synchronization. Spatio-temporal bivariate features are very high-dimensional and cannot be analyzed with conventional statistical methods. Hence, we propose state-of-the-art machine learning methods that handle high-dimensional inputs. METHODS We computed bivariate...

Most of the current epileptic seizure prediction algorithms require much prior knowledge of a patient’s pre-seizure electroencephalogram (EEG) patterns. They are impractical to be applied to a wide range of patients due to a very high inter-individual variability of EEG patterns. This paper proposes an adaptive prediction framework, which is capable of accumulating knowledge of pre-seizure EEG ...

A large body of research has shown superior learning rates in variable practice compared to repetitive practice. More specifically, this has been demonstrated in the contextual interference (CI) and in the differential learning (DL) approach that are both representatives of variable practice. Behavioral studies have indicate different learning processes in CI and DL. Aim of the present study wa...

In recent medical research, tremendous progress has been made in the application of deep learning (DL) techniques. This article systematically reviews how DL techniques have applied to electroencephalogram (EEG) data for diagnostic and predictive purposes conducting research on mental disorders. EEG-studies psychiatric diseases based ICD-10 or DSM-V classification that used either convolutional...