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Self-Supervised Learning for Electroencephalography
IEEE Transactions on Neural Networks and Learning Systems, 2022Decades 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
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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
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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
2012Abstract 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
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Stereotactic electroencephalography
Clinical Neurology and Neurosurgery, 2020Stereotactic 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
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