A review of classification techniques of EMG signals during isotonic and isometric contractions
In recent years, there has been major interest in the exposure to physical therapy during
rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and …
rehabilitation. Several publications have demonstrated its usefulness in clinical/medical and …
Automatic epileptic seizure detection in EEG using nonsubsampled wavelet–fourier features
Epilepsy is a common neurological disorder that is difficult to treat. Monitoring brain activity
using electroencephalography (EEG) has become an important tool for the diagnosis of …
using electroencephalography (EEG) has become an important tool for the diagnosis of …
[PDF][PDF] Brain computer interface: EEG signal preprocessing issues and solutions
N Elsayed, ZS Zaghloul, M Bayoumi - Int. J. Comput. Appl, 2017 - e-tarjome.com
ABSTRACT Brain Computer Interface (BCI) is often directed at mapping, assisting, or
repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a …
repairing human cognitive or sensory-motor functions. Electroencephalogram (EEG) is a …
Random ensemble learning for EEG classification
MP Hosseini, D Pompili, K Elisevich… - Artificial intelligence in …, 2018 - Elsevier
Real-time detection of seizure activity in epilepsy patients is critical in averting seizure
activity and improving patients' quality of life. Accurate evaluation, presurgical assessment …
activity and improving patients' quality of life. Accurate evaluation, presurgical assessment …
Convolutional neural network with autoencoder-assisted multiclass labelling for seizure detection based on scalp electroencephalography
H Takahashi, A Emami, T Shinozaki, N Kunii… - Computers in biology …, 2020 - Elsevier
Objective In long-term video-monitoring, automatic seizure detection holds great promise as
a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) …
a means to reduce the workload of the epileptologist. A convolutional neural network (CNN) …
Predicting epileptic seizures from intracranial EEG using LSTM-based multi-task learning
Epilepsy afflicts nearly 1% of the world's population, and is characterized by the occurrence
of spontaneous seizures. It's important to make prediction before seizures, so that epileptic …
of spontaneous seizures. It's important to make prediction before seizures, so that epileptic …
[HTML][HTML] Identification of epileptic networks with graph convolutional network incorporating oscillatory activities and evoked synaptic responses
Stereoelectroencephalography (SEEG) offers unique neural data from in-depth brain
structures with fine temporal resolutions to better investigate the origin of epileptic brain …
structures with fine temporal resolutions to better investigate the origin of epileptic brain …
Modeling and Bayesian inference for processes characterized by abrupt variations
R Chiplunkar, B Huang - Journal of Process Control, 2023 - Elsevier
Abrupt variations are often observed in the datasets of chemical processes but they have not
been well studied in the literature. This paper proposes a method of modeling and …
been well studied in the literature. This paper proposes a method of modeling and …
Early prediction of epilepsy seizures vlsi bci system
ZS Zaghloul, M Bayoumi - arXiv preprint arXiv:1906.02894, 2019 - arxiv.org
Controlling the surrounding world and predicting future events has always seemed like a
dream, but that could become a reality using a Brain-Computer/Machine Interface (BCI/BMI) …
dream, but that could become a reality using a Brain-Computer/Machine Interface (BCI/BMI) …
Brain-computer interface for analyzing epileptic big data
MP Hosseini - 2018 - rucore.libraries.rutgers.edu
One percent of the world's population suffers from epilepsy, a chronic disorder characterized
by the occurrence of spontaneous seizures. About 30 percent of patients remain medically …
by the occurrence of spontaneous seizures. About 30 percent of patients remain medically …