A review on machine learning for EEG signal processing in bioengineering
Electroencephalography (EEG) has been a staple method for identifying certain health
conditions in patients since its discovery. Due to the many different types of classifiers …
conditions in patients since its discovery. Due to the many different types of classifiers …
Machine learning applications on neuroimaging for diagnosis and prognosis of epilepsy: A review
Abstract Machine learning is playing an increasingly important role in medical image
analysis, spawning new advances in the clinical application of neuroimaging. There have …
analysis, spawning new advances in the clinical application of neuroimaging. There have …
Optimized deep learning for EEG big data and seizure prediction BCI via internet of things
A brain-computer interface (BCI) for seizure prediction provides a means of controlling
epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected …
epilepsy in medically refractory patients whose site of epileptogenicity cannot be resected …
Connectome biomarkers of drug‐resistant epilepsy
Drug‐resistant epilepsy (DRE) considerably affects patient health, cognition, and well‐
being, and disproportionally contributes to the overall burden of epilepsy. The most common …
being, and disproportionally contributes to the overall burden of epilepsy. The most common …
Coherence pursuit: Fast, simple, and robust principal component analysis
This paper presents a remarkably simple, yet powerful, algorithm termed coherence pursuit
(CoP) to robust principal component analysis (PCA). As inliers lie in a low-dimensional …
(CoP) to robust principal component analysis (PCA). As inliers lie in a low-dimensional …
Cloud-based deep learning of big EEG data for epileptic seizure prediction
Develo** a Brain-Computer Interface (BCI) for seizure prediction can help epileptic
patients have a better quality of life. However, there are many difficulties and challenges in …
patients have a better quality of life. However, there are many difficulties and challenges in …
Multimodal data analysis of epileptic EEG and rs-fMRI via deep learning and edge computing
Background and objective Multimodal data analysis and large-scale computational
capability is entering medicine in an accelerative fashion and has begun to influence …
capability is entering medicine in an accelerative fashion and has begun to influence …
Non-intrusive energy disaggregation using non-negative matrix factorization with sum-to-k constraint
Energy disaggregation or non-intrusive load monitoring addresses the issue of extracting
device-level energy consumption information by monitoring the aggregated signal at one …
device-level energy consumption information by monitoring the aggregated signal at one …
Deep learning with edge computing for localization of epileptogenicity using multimodal rs-fMRI and EEG big data
Epilepsy is a chronic brain disorder characterized by the occurrence of spontaneous
seizures of which about 30 percent of patients remain medically intractable and may …
seizures of which about 30 percent of patients remain medically intractable and may …
Clinical application of machine learning models for brain imaging in epilepsy: a review
Epilepsy is a common neurological disorder characterized by recurrent and disabling
seizures. An increasing number of clinical and experimental applications of machine …
seizures. An increasing number of clinical and experimental applications of machine …