[HTML][HTML] The applied principles of EEG analysis methods in neuroscience and clinical neurology
H Zhang, QQ Zhou, H Chen, XQ Hu, WG Li, Y Bai… - Military Medical …, 2023 - Springer
Electroencephalography (EEG) is a non-invasive measurement method for brain activity.
Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural …
Due to its safety, high resolution, and hypersensitivity to dynamic changes in brain neural …
[HTML][HTML] How machine learning is powering neuroimaging to improve brain health
This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
[HTML][HTML] Deep learning-based stroke disease prediction system using real-time bio signals
The emergence of an aging society is inevitable due to the continued increases in life
expectancy and decreases in birth rate. These social changes require new smart healthcare …
expectancy and decreases in birth rate. These social changes require new smart healthcare …
Time-frequency domain deep convolutional neural network for the classification of focal and non-focal EEG signals
S Madhavan, RK Tripathy, RB Pachori - IEEE Sensors Journal, 2019 - ieeexplore.ieee.org
The neurological disease such as the epilepsy is diagnosed using the analysis of
electroencephalogram (EEG) recordings. The areas of the brain associated with the …
electroencephalogram (EEG) recordings. The areas of the brain associated with the …
Ischemic stroke identification based on EEG and EOG using ID convolutional neural network and batch normalization
In 2015, stroke was the number one cause of death in Indonesia. The majority type of stroke
is ischemic. The standard tool for diagnosing stroke is CT-Scan. For developing countries …
is ischemic. The standard tool for diagnosing stroke is CT-Scan. For developing countries …
Encoding rich frequencies for classification of stroke patients EEG signals
The stroke, which is a sudden cut in the blood supply in the brain, has become a severe
phenomenon. It has affected around 15 million people annually worldwide. Methods of …
phenomenon. It has affected around 15 million people annually worldwide. Methods of …
Recurrent quantification analysis-based emotion classification in stroke using electroencephalogram signals
Stroke is a cerebrovascular disorder, and one of the most common effects of stroke is
emotional disturbances. This present work classifies six emotions (anger, sadness …
emotional disturbances. This present work classifies six emotions (anger, sadness …
A survey on comparison analysis between EEG signal and MRI for brain stroke detection
S Bhattacharjee, S Ghatak, S Dutta… - … Technologies in Data …, 2019 - Springer
Encephalogram (EEG) provides the recordings of the brain and is used for detecting the
brain diseases. In this paper, a detailed study has been carried out for a few applications in …
brain diseases. In this paper, a detailed study has been carried out for a few applications in …
Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity
Stroke is one of the highest causes of death in adults and disability in Indonesia, even in the
world. Therefore, it is necessary to diagnose stroke in the early stage and give accurate …
world. Therefore, it is necessary to diagnose stroke in the early stage and give accurate …
Detection of EEG signal post-stroke using FFT and convolutional neural network
Stroke is a condition that occurs when the blood supply to the brain is disrupted or reduced.
It may be caused by a blockage (ischemic stroke) or rupture of a blood vessel (hemorrhagic …
It may be caused by a blockage (ischemic stroke) or rupture of a blood vessel (hemorrhagic …