An EEG is a recording of the electrical signals produced by activity within the brain. A variety of cognitive and pathologies yield specific EEG signatures, which are diagnostic of the condition. As a clinical EEG may contain non-stationary signals, we have employed a Daubechies wavelet to automatically detect embedded signals that vary both in their frequency and magnitude from a clinical EEG ...

BrainResonance is an Electroencephalogram (EEG)-based system proposed to explore the feasibility of using EEG signals for human interaction. EEG signals that measure human brain activities were captured from two users simultaneously in realtime. The correlation values of their EEG alpha and beta rhythms were computed and used to drive a visual feedback system that was in the form of a simple ga...

In 1942 Gibbs and Reid described a slowing of the electroencephalogram (EEG) at the end of a normal pregnancy. To the best of our knowledge this is the only report that addresses the modification of the EEG in normal pregnancy. We performed a spectral multichannel EEG analysis and revealed no differences during third trimester pregnancy and six months postpartum. Therefore EEG changes seen duri...

The elimination of artefacts from Electroencephalogram(EEG) has an important role many signal and image processing applications. The artefacts are the noises that appears during the acquisition of signals from the patient body. With the presence of these artefacts it become difficult for doctors and technicians to analyse the Electroencephalogram signals efficiently. The aim of this research wo...

Neurological disorders can directly affect basic functions of the upper limbs, leading to an alteration in or loss motor capacity and hindering performance activities daily living. Transcranial direct current stimulation (tDCS) is a noninvasive technique capable modulating cortical excitability, favoring both brain activity neuroplasticity. The objective was systematize discuss effects transcra...

The high cost of acquiring training data in the field emotion recognition based on electroencephalogram (EEG) is a problem, making it difficult to establish high-precision model from EEG signals for tasks. Given outstanding performance generative adversarial networks (GANs) augmentation recent years, this paper proposes task-driven method CWGAN generate high-quality artificial data. generated a...

conclusions these open-label data show trends toward normalization of eeg and concomitant clinical improvement using magnetic stimulation for ptsd. results of the 21 patients who initiated therapy, 16 completed treatment. clinical improvements on the pcl-m were seen in these 16 patients, with an average pre-treatment score of 54.9 and post-treatment score of 31.8 (p < 0.001). in addition, relat...

In this paper, we will consider the problem of classifying electroencephalogram (EEG) signals of normal subjects, and subjects suffering from psychiatric disorder, e.g., obsessive compulsive disorder, schizophrenia, using a class of artificial neural networks, viz., multi-layer perceptron. It is shown that the multilayer perceptron is capable of classifying unseen test EEG signals to a high deg...

Electroencephalogram (EEG) is a common technique used to monitor the electrical activities of the human brain. In some applications, EEG is recorded for a long time. Since it is time consuming to analyze this EEG, further processing requires a lossless compression of this long term EEG. This paper addresses effective compression of EEG using EBCOT algorithm. EBCOT is an advance technique for im...

The goal of proposed work is the development of an electroencephalogram (EEG) based BCI system. The overview of this work is, the user thought is extracted from the brain activity of a healthy person. Pre-processing is performed using filters and wavelet transforms to extract the features and classify them to their respective class. The intention of this work is to enhance human interaction wit...