Results 61 to 70 of about 1,230,009 (250)
DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network
SensorsElectroencephalogram (EEG) signals are important bioelectrical signals widely used in brain activity studies, cognitive mechanism research, and the diagnosis and treatment of neurological disorders.
Yinan Cai, Zhao Meng, Dian Huang
doaj +1 more source
Design and Characterization of an EEG-Hat for Reliable EEG Measurements
Micromachines, 2020 In this study, a new hat-type electroencephalogram (EEG) device with candle-like microneedle electrodes (CMEs), called an EEG-Hat, was designed and fabricated.
Takumi Kawana +4 more
doaj +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background We aimed to identify the proportion of individuals with a confirmed diagnosis of childhood absence epilepsy (CAE) or juvenile absence epilepsy (JAE) who show a negative routine EEG (rEEG), and to determine the main factors associated with this finding.
Francesco Fortunato +7 more
wiley +1 more source
Epileptic seizure detection from EEG signals using logistic model trees
, 2016 Reliable analysis of electroencephalogram (EEG) signals is crucial that could lead the way to correct diagnostic and therapeutic methods for the treatment of patients with neurological abnormalities, especially epilepsy.
Kabir, Enamul, Siuly, ., Zhang, Yanchun
core +1 more source
A first attempt at constructing genetic programming expressions for EEG classification [PDF]
, 2005 Proceeding of: 15th International Conference on Artificial Neural Networks ICANN 2005, Poland, 11-15 September, 2005In BCI (Brain Computer Interface) research, the classification of EEG signals is a domain where raw data has to undergo some preprocessing,
Estébanez Tascón, César +8 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective The aim of this study was to characterize intellectual and motor function, neurological features including epilepsy, treatment response, and adaptive behavior in patients with pyruvate dehydrogenase complex deficiency (PDCD) in Sweden.
Antri Savvidou +6 more
wiley +1 more source
Identification Of Electrode Enchepalo Graph Signal To Move The Cursor Using Back Propagation Method [PDF]
, 2012 In this paper the researchers describe the application of back propagation neural networks as classification and sampling technique (TS) for feature extraction of waveform signals Electro Enchepalo Graph (EEG).
Mauridhi Hery , Purnomo +2 more
core +1 more source
BELT: Bootstrapped EEG-to-Language Training by Natural Language Supervision
IEEE Transactions on Neural Systems and Rehabilitation EngineeringDecoding natural language from noninvasive brain signals has been an exciting topic with the potential to expand the applications of brain-computer interface (BCI) systems.
Jinzhao Zhou +4 more
doaj +1 more source
Ultra-low Noise EEG at LSBB: New results
E3S Web of Conferences, 2016 In this study, we investigate functional correlates of gamma band oscillations in low-noise EEG signals acquired in the LSBB shielded capsule and compare them to signals acquired in a typical hospital environment.
Hamzei Nazanin +6 more
doaj +1 more source
Automatic artefact removal in a self-paced hybrid brain- computer interface system
Journal of NeuroEngineering and Rehabilitation, 2012 Background A novel artefact removal algorithm is proposed for a self-paced hybrid brain-computer interface (BCI) system. This hybrid system combines a self-paced BCI with an eye-tracker to operate a virtual keyboard.
Yong Xinyi +3 more
doaj +1 more source