An epileptic seizure is a transient event of symptoms due to abnormal neuronal action in the brain. Electroencephalography (EEG) is the neuro physiological measurement of electrical activity in the brain as recorded by electrodes placed in the cerebral cortex. An epilepsy EEG is based on three approaches. First, a scaling and wavelet function of the Multi Wavelet Transform (MWT) offers orthogon...

This paper proposes the use of new target vectors for MLP learning in EEG signal classification. A large Euclidean distance provided by orthogonal bipolar vectors as new target ones is explored to improve the learning and generalization abilities of MLPs. The data set consisted of EEG signals captured from normal individuals and individuals under brain-death protocol. Experimental results are r...

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC...

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 this paper, we describe three neural network (NN) based EEG-Speech (NES) models that map the unspoken EEG signals to the corresponding phonemes. Instead of using conventional feature extraction techniques, the proposed NES models rely on graphic learning to project both EEG and speech signals into deep representation feature spaces. This NN based linear projection helps to realize multimodal...

There are several ways of recording psychophysiology data from humans, for example Galvanic Skin Response (GSR), Electromyography (EMG), Electrocardiogram (ECG) and Electroencephalography (EEG). In this paper we focus on emotion detection using EEG. Various machine learning techniques can be used on the recorded EEG data to classify emotional states. K-Nearest Neighbor (KNN), Bayesian Network (...

The technique of electroencephalographic neurofeedback (EEG NF) emerged in the 1970s and is a technique that measures a subject's EEG signal, processes it in real time, extracts a parameter of interest and presents this information in visual or auditory form. The goal is to effectuate a behavioural modification by modulating brain activity. The EEG NF opens new therapeutic possibilities in the ...