There is ongoing research on using electroencephalography (EEG) to predict depression. In particular, the deep learning method in which brain waves are used as inputs of a convolutional neural network (CNN) being widely researched and has shown remarkable performance. We built regression model severity score (Beck Depression Inventory [BDI]) depressed patients an extension deep-asymmetry method...

Electroencephalography (EEG) is the reference tool for analysis of brain function, reflecting normal and pathological neuronal network activity. During neonatal period, EEG patterns evolve weekly, according to gestational age. The first analytical criteria various maturational stages standardized terminology were published by a group French neurophysiologists training in Paris (France) 1999. Th...

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. In this paper, we combine such EEG measures with KT to improve estimates of the students’ hidden knowledge state....

In recent years, strides made in the development of BrainComputer Interface (BCI) technology have foreseen a contemporary evolution in the way we create music with Electroencephalography (EEG). The development of new BCI technology has given musicians the freedom to take their work into new domains for music and art making. However, a fundamental challenge for artists using EEG in their work ha...

The annotation of a large corpus of Electroencephalography (EEG) reports is a crucial step in the development of an EEG-specific patient cohort retrieval system. The annotation of multiple types of EEG-specific medical concepts, along with their polarity and modality, is challenging, especially when automatically performed on Big Data. To address this challenge, we present a novel framework whi...