The target paper aims at recognizing emotions using miscellaneous stimulus domains such as Electroencephalography (EEG) Images. The present study focuses on the recognition of emotions and extracting active regions, by using image processing and classification techniques. The study was performed using the data from 10 volunteers experiencing three emotional states relax, happy and sad. By apply...

Simultaneously recorded electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be used to non-invasively measure the spatiotemporal dynamics of the human brain. One challenge is dealing with the artifacts that each modality introduces into the other when the two are recorded concurrently, for example the ballistocardiogram (BCG). We conducted a preliminary comparison ...

The International Classification of Epileptic Seizures is the most widely used, but an alternative system based purely on ictal symptoms and signs has been proposed: the semiological classification. Our objective was to compare the two in a sample of patients evaluated at epilepsy centers. We collected 78 consecutive patients evaluated in outpatient epilepsy clinics who subsequently underwent n...

Upper limb movement classification, which maps input signals to the target activities, is a key building block in control of rehabilitative robotics. Classifiers are trained for system comprehend desires patient whose upper limbs do not function properly. Electromyography (EMG) and Electroencephalography (EEG) used widely classification. By analysing classification results real-time EEG EMG sig...

OBJECTIVE Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) combines advantages of both methods, namely high temporal resolution of EEG and high spatial resolution of fMRI. However, EEG quality is limited due to severe artifacts caused by fMRI scanners. APPROACH To improve EEG data quality substantially, we introduce methods that use a reusable referen...

OBJECTIVE It has been shown that a new procedure (implicit function as squashing time, IFAST) based on artificial neural networks (ANNs) is able to compress eyes-closed resting electroencephalographic (EEG) data into spatial invariants of the instant voltage distributions for an automatic classification of mild cognitive impairment (MCI) and Alzheimer's disease (AD) subjects with classification...

This paper investigates the feasibility of using neural network (NN) and late gamma band (LGB) electroencephalogram (EEG) features extracted from the brain to identify the individuality of subjects. The EEG signals were recorded using 61 active electrodes located on the scalp while the subjects perceived a single picture. LGB EEG signals occur with jittering latency of above 280 ms and are not ...

Brain computer interface systems are capable to detect and interpret the mental activity into computer interpretable signals giving opportunity for performing computer controlled activities without muscular movement. An challenging area in Brain Computer Interface research is the classification of EEG signals using the raw signals captured which has to undergo some preprocessing, so that the ri...

Introduction: Tinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation of the brain...

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...