Search results for "Electroencephalography"
showing 10 items of 779 documents
Hybrid Deep Shallow Network for Assessment of Depression Using Electroencephalogram Signals
2020
Depression is a mental health disorder characterised by persistently depressed mood or loss of interest in activities resulting impairment in daily life significantly. Electroencephalography (EEG) can assist with the accurate diagnosis of depression. In this paper, we present two different hybrid deep learning models for classification and assessment of patient suffering with depression. We have combined convolutional neural network with Gated recurrent units (RGUs), thus the proposed network is shallow and much smaller in size in comparison to its counter LSTM network. In addition to this, proposed approach is less sensitive to parameter settings. Extensive experiments on EEG dataset shows…
On the Influence of Affect in EEG-Based Subject Identification
2021
Biometric signals have been extensively used for user identification and authentication due to their inherent characteristics that are unique to each person. The variation exhibited between the brain signals (EEG) of different people makes such signals especially suitable for biometric user identification. However, the characteristics of these signals are also influenced by the user’s current condition, including his/her affective state. In this paper, we analyze the significance of the affect-related component of brain signals within the subject identification context. Consistent results are obtained across three different public datasets, suggesting that the dominant component of the sign…
Image-Evoked Affect and its Impact on Eeg-Based Biometrics
2019
Electroencephalography (EEG) signals provide a representation of the brain’s activity patterns and have been recently exploited for user identification and authentication due to their uniqueness and their robustness to interception and artificial replication. Nevertheless, such signals are commonly affected by the individual’s emotional state. In this work, we examine the use of images as stimulus for acquiring EEG signals and study whether the use of images that evoke similar emotional responses leads to higher identification accuracy compared to images that evoke different emotional responses. Results show that identification accuracy increases when the system is trained with EEG recordin…
ES1D: A Deep Network for EEG-Based Subject Identification
2017
Security systems are starting to meet new technologies and new machine learning techniques, and a variety of methods to identify individuals from physiological signals have been developed. In this paper, we present ESID, a deep learning approach to identify subjects from electroencephalogram (EEG) signals captured by using a low cost device. The system consists of a Convolutional Neural Network (CNN), which is fed with the power spectral density of different EEG recordings belonging to different individuals. The network is trained for a period of one million iterations, in order to learn features related to local patterns in the spectral domain of the original signal. The performance of the…
Group analysis of ongoing EEG data based on fast double-coupled nonnegative tensor decomposition
2019
Abstract Background Ongoing EEG data are recorded as mixtures of stimulus-elicited EEG, spontaneous EEG and noises, which require advanced signal processing techniques for separation and analysis. Existing methods cannot simultaneously consider common and individual characteristics among/within subjects when extracting stimulus-elicited brain activities from ongoing EEG elicited by 512-s long modern tango music. New method Aiming to discover the commonly music-elicited brain activities among subjects, we provide a comprehensive framework based on fast double-coupled nonnegative tensor decomposition (FDC-NTD) algorithm. The proposed algorithm with a generalized model is capable of simultaneo…
Cognitive network hyperactivation and motor cortex decline correlate with ALS prognosis.
2021
We aimed to quantitatively characterize progressive brain network disruption in Amyotrophic Lateral Sclerosis (ALS) during cognition using the mismatch negativity (MMN), an electrophysiological index of attention switching. We measured the MMN using 128-channel EEG longitudinally (2-5 timepoints) in 60 ALS patients and cross-sectionally in 62 healthy controls. Using dipole fitting and linearly constrained minimum variance beamforming we investigated cortical source activity changes over time. In ALS, the inferior frontal gyri (IFG) show significantly lower baseline activity compared to controls. The right IFG and both superior temporal gyri (STG) become progressively hyperactive longitudina…
Long-term physical activity modulates brain processing of somatosensory stimuli: Evidence from young male twins.
2016
Leisure-time physical activity is a key contributor to physical and mental health. Yet the role of physical activity in modulating cortical function is poorly known. We investigated whether precognitive sensory brain functions are associated with the level of physical activity. Physical activity history (3-yr-LTMET), physiological measures and somatosensory mismatch response (sMMR) in EEG were recorded in 32 young healthy twins. In all participants, 3-yr-LTMET correlated negatively with body fat%, r = −0.77 and positively with VO2max, r = 0.82. The fat% and VO2max differed between 15 physically active and 17 inactive participants. Trend toward larger sMMR was seen in inactive compared to ac…
Antiepileptic drug reduction and increased risk of stimulation-evoked focal to bilateral tonic-clonic seizure during cortical stimulation in patients…
2017
Introduction: Stimulation-evoked focal to bilateral tonic-clonic seizure (FBTCS) can be a stressful and possibly harmful adverse event for patients during cortical stimulation (CS). We evaluated if drug load reduction of anti epileptic drugs (AEDs) during CS increases the risk of stimulation-evoked FBTCS. Material and methods: In this retrospective cohort study, we searched our local database for patients with drug resistant epilepsy who underwent invasive video-EEG monitoring and CS in the University Hospital la Fe Valencia from January 2006 to November 2016. The AED drug load was calculated with the defined daily dose. We applied a uni- and multivariate logistic regression model to estima…
Models for preterm cortical development using non invasive clinical EEG
2017
AbstractThe objective of this study was to evaluate the piglet and the mouse as model systems for preterm cortical development. According to the clinical context, we used non invasive EEG recordings. As a prerequisite, we developed miniaturized Ag/AgCl electrodes for full band EEG recordings in mice and verified that Urethane had no effect on EEG band power. Since mice are born with a “preterm” brain, we evaluated three age groups: P0/P1, P3/P4 and P13/P14. Our aim was to identify EEG patterns in the somatosensory cortex which are distinguishable between developmental stages and represent a physiologic brain development. In mice, we were able to find clear differences between age groups wit…
Shank3 Mice Carrying the Human Q321R Mutation Display Enhanced Self-Grooming, Abnormal Electroencephalogram Patterns, and Suppressed Neuronal Excitab…
2019
Shank3, a postsynaptic scaffolding protein involved in regulating excitatory synapse assembly and function, has been implicated in several brain disorders, including autism spectrum disorders (ASD), Phelan-McDermid syndrome, schizophrenia, intellectual disability, and mania. Here we generated and characterized a Shank3 knock-in mouse line carrying the Q321R mutation (Shank3Q321R mice) identified in a human individual with ASD that affects the ankyrin repeat region (ARR) domain of the Shank3 protein. Homozygous Shank3Q321R/Q321R mice show a selective decrease in the level of Shank3a, an ARR-containing protein variant, but not other variants. CA1 pyramidal neurons in the Shank3Q321R/Q321R hip…