Results 231 to 240 of about 151,138 (275)

Self-Supervised Learning for Electroencephalography

IEEE Transactions on Neural Networks and Learning Systems, 2022
Decades of research have shown machine learning superiority in discovering highly nonlinear patterns embedded in electroencephalography (EEG) records compared with conventional statistical techniques.
M. Rafiei   +3 more
semanticscholar   +1 more source

Attention-Inception and Long- Short-Term Memory-Based Electroencephalography Classification for Motor Imagery Tasks in Rehabilitation

IEEE Transactions on Industrial Informatics, 2022
In recent years, the contributions of deep learning have had a phenomenal impact on electroencephalography-based brain-computer interfaces. While the decoding accuracy of electroencephalography signals has continued to increase, the process has caused ...
S. Amin   +4 more
semanticscholar   +1 more source

Electroencephalography and video-electroencephalography

2012
Abstract Electroencephalography (EEG) is a specific investigation to support the diagnosis of epilepsy, demonstrating interictal epileptiform activity in the majority of individuals with epilepsy. The EEG can also help classify the epilepsy as focal or generalized, and can suggest certain epileptic syndromes.
Antonio, Gil-Nagel, Bassel, Abou-Khalil
openaire   +2 more sources

Stereotactic electroencephalography

Clinical Neurology and Neurosurgery, 2020
Stereotactic implantation of depth electrodes for surgical evaluation of drug-resistant epilepsy is the technique of choice in many centers across the world. Historically, the choice of depth versus subdural electrodes has been largely dependent on the training of epileptologists and epilepsy surgeons in light of their comfort level with implantation ...
Taha Gholipour   +2 more
openaire   +2 more sources

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