A comprehensive review on critical issues and possible solutions of motor imagery based electroencephalography brain-computer interface
Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of
communication through the utilization of neural activity generated due to kinesthetic …
communication through the utilization of neural activity generated due to kinesthetic …
A comprehensive review of machine learning approaches for dyslexia diagnosis
Electroencephalography (EEG) is the commonly employed electro-biological imaging
technique for diagnosing brain functioning. The EEG signals are used to determine head …
technique for diagnosing brain functioning. The EEG signals are used to determine head …
Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG
One of the challenges in developing a Brain Computer Interface (BCI) is dealing with the
high dimensionality of the data when extracting features from EEG signals. Different feature …
high dimensionality of the data when extracting features from EEG signals. Different feature …
An improved intuitionistic fuzzy c-means clustering algorithm incorporating local information for brain image segmentation
H Verma, RK Agrawal, A Sharan - Applied Soft Computing, 2016 - Elsevier
The segmentation of brain magnetic resonance (MR) images plays an important role in the
computer-aided diagnosis and clinical research. However, due to presence of noise and …
computer-aided diagnosis and clinical research. However, due to presence of noise and …
[HTML][HTML] Machine learning with ensemble stacking model for automated sleep staging using dual-channel EEG signal
SK Satapathy, AK Bhoi, D Loganathan… - … Signal Processing and …, 2021 - Elsevier
Sleep staging is an important part of diagnosing the different types of sleep-related disorders
because any discrepancies in the sleep scoring process may cause serious health problems …
because any discrepancies in the sleep scoring process may cause serious health problems …
PSO-based feature selection and neighborhood rough set-based classification for BCI multiclass motor imagery task
S Udhaya Kumar, H Hannah Inbarani - Neural Computing and …, 2017 - Springer
In recent years, most of the researchers are developing brain–computer interface (BCI)
applications for the physically disabled to be able to interconnect with peripheral devices …
applications for the physically disabled to be able to interconnect with peripheral devices …
DSCNN-CAU: deep-learning-based mental activity classification for IoT implementation toward portable BCI
Mental activity classification (MAC) based on electroencephalogram (EEG) is used in the
brain–computer interface (BCI) and neurofeedback applications. For this purpose, machine …
brain–computer interface (BCI) and neurofeedback applications. For this purpose, machine …
A novel approach for classification of mental tasks using multiview ensemble learning (MEL)
Brain-computer interface (BCI) is a domain, in which a person can send information without
using any exterior nerve or muscles, just using their brain signal, called …
using any exterior nerve or muscles, just using their brain signal, called …
DiabDeep: Pervasive diabetes diagnosis based on wearable medical sensors and efficient neural networks
Diabetes impacts the quality of life of millions of people around the globe. However,
diabetes diagnosis is still an arduous process, given that this disease develops and gets …
diabetes diagnosis is still an arduous process, given that this disease develops and gets …
Prognosis of automated sleep staging based on two-layer ensemble learning stacking model using single-channel EEG signal
SK Satapathy, D Loganathan - Soft Computing, 2021 - Springer
Sleep is important part for human health and quality of life in the daily routine basis.
However, numerous individuals face sleep problems due to rapid changes occurred in both …
However, numerous individuals face sleep problems due to rapid changes occurred in both …