Summary of over fifty years with brain-computer interfaces—a review
Over the last few decades, the Brain-Computer Interfaces have been gradually making their
way to the epicenter of scientific interest. Many scientists from all around the world have …
way to the epicenter of scientific interest. Many scientists from all around the world have …
[HTML][HTML] Epileptic multi-seizure type classification using electroencephalogram signals from the Temple University Hospital Seizure Corpus: A review
Epilepsy is one of the most paramount neurological diseases, affecting about 1% of the
world's population. Seizure detection and classification are difficult tasks and are ongoing …
world's population. Seizure detection and classification are difficult tasks and are ongoing …
Machine learning and deep learning approach for medical image analysis: diagnosis to detection
Computer-aided detection using Deep Learning (DL) and Machine Learning (ML) shows
tremendous growth in the medical field. Medical images are considered as the actual origin …
tremendous growth in the medical field. Medical images are considered as the actual origin …
Identification of epileptic EEG signals using convolutional neural networks
Epilepsy is one of the chronic neurological disorders that is characterized by a sudden burst
of excess electricity in the brain. This abnormality appears as a seizure, the detection of …
of excess electricity in the brain. This abnormality appears as a seizure, the detection of …
Epileptic seizure detection using a hybrid 1D CNN‐machine learning approach from EEG data
F Hassan, SF Hussain… - Journal of Healthcare …, 2022 - Wiley Online Library
Electroencephalography (EEG) is a widely used technique for the detection of epileptic
seizures. It can be recorded in a noninvasive manner to present the electrical activity of the …
seizures. It can be recorded in a noninvasive manner to present the electrical activity of the …
Electroencephalogram signal classification based on Fourier transform and Pattern Recognition Network for epilepsy diagnosis
Q Gao, AH Omran, Y Baghersad, O Mohammadi… - … Applications of Artificial …, 2023 - Elsevier
Epilepsy is a central nervous system (CNS) disorder that affects nerve cells in the brain and
produces seizures in which consciousness is lost. People with epilepsy have frequent …
produces seizures in which consciousness is lost. People with epilepsy have frequent …
Automatic identification of schizophrenia using EEG signals based on discrete wavelet transform and RLNDiP technique with ANN
Schizophrenia (ScZ) is a detrimental condition of the brain often associated with depression,
anxiety, and socio-psychological problems. In the traditional diagnosis approach, the results …
anxiety, and socio-psychological problems. In the traditional diagnosis approach, the results …
Application of entropy for automated detection of neurological disorders with electroencephalogram signals: a review of the last decade (2012-2022)
An automated Neurological Disorder detection system can be considered as a cost-effective
and resource efficient tool for medical and healthcare applications. In automated …
and resource efficient tool for medical and healthcare applications. In automated …
Channel based epilepsy seizure type detection from electroencephalography (EEG) signals with machine learning techniques
Epileptic seizures result from disturbances in the electrical activity of the brain, classified as
focal, generalized, or unknown. Failure to correctly classify epileptic seizures may result in …
focal, generalized, or unknown. Failure to correctly classify epileptic seizures may result in …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …