The fractional Fourier transform as a biomedical signal and image processing tool: A review
This work presents a literature review of the fractional Fourier transform (FrFT) investigations
and applications in the biomedical field. The FrFT is a time-frequency analysis tool that has …
and applications in the biomedical field. The FrFT is a time-frequency analysis tool that has …
Unveiling Thoughts: A Review of Advancements in EEG Brain Signal Decoding into Text
The conversion of brain activity into text using electroencephalography (EEG) has gained
significant traction in recent years. Many researchers are working to develop new models to …
significant traction in recent years. Many researchers are working to develop new models to …
On the utility of power spectral techniques with feature selection techniques for effective mental task classification in noninvasive BCI
In this paper, classification of mental task-root brain-computer interfaces (BCIs) is being
investigated. The mental tasks are dominant area of investigations in BCI, which utmost …
investigated. The mental tasks are dominant area of investigations in BCI, which utmost …
Nonconvulsive epileptic seizure detection in scalp EEG using multiway data analysis
Nonconvulsive status epilepticus is a condition where the patient is exposed to abnormally
prolonged epileptic seizures without evident physical symptoms. Since these continuous …
prolonged epileptic seizures without evident physical symptoms. Since these continuous …
Genetic algorithms tuned expert model for detection of epileptic seizures from EEG signatures
The uncontrolled firing of neurons in brain leads to epileptic seizures in the patients. A novel
scheme to detect epileptic seizures from back ground electroencephalogram (EEG) is …
scheme to detect epileptic seizures from back ground electroencephalogram (EEG) is …
Exploring dimensionality reduction of EEG features in motor imagery task classification
PJ García-Laencina, G Rodríguez-Bermudez… - Expert Systems with …, 2014 - Elsevier
Abstract A Brain-Computer Interface (BCI) system based on motor imagery (MI) identifies
patterns of electrical brain activity to predict the user intention while certain movement …
patterns of electrical brain activity to predict the user intention while certain movement …
Performance enhancement of mental task classification using EEG signal: a study of multivariate feature selection methods
In the recent years, the research community has shown interest in the development of brain–
computer interface applications which assist physically challenged people to communicate …
computer interface applications which assist physically challenged people to communicate …
EEG-based recognition of attention state using wavelet and support vector machine
EC Djamal, DP Pangestu… - 2016 International Seminar …, 2016 - ieeexplore.ieee.org
Recognition of attention level is required to the evaluation of student learning process and
employee development. This research proposed a recognition of attention state using …
employee development. This research proposed a recognition of attention state using …
Review of EEG feature selection by neural networks
I Rakhmatulin - International Journal of Science and Business, 2020 - papers.ssrn.com
The basis of the work of electroencephalography (EEG) is the registration of electrical
impulses from the brain using a special sensor or electrode. This method is used to treat and …
impulses from the brain using a special sensor or electrode. This method is used to treat and …
[PDF][PDF] Review paper on feature extraction methods for EEG signal analysis
AR Mane, SD Biradar, RK Shastri - Int. J. Emerg. Trend Eng. Basic …, 2015 - researchgate.net
The main aim of Brain Computer interface is to effectively classify Electroencephalogram
(EEG). EEG signals are used to extract correct information from brain and classify with …
(EEG). EEG signals are used to extract correct information from brain and classify with …