Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI

Computational Intelligence and Neuroscience - United States
doi 10.1155/2015/781207
Full Text
Abstract

Available in full text

Date
Authors
Publisher

Hindawi Limited

Related search

Phishing Detection Based on Machine Learning and Feature Selection Methods

International Journal of Interactive Mobile Technologies
Computer NetworksE-learningComputer Science ApplicationsCommunications
2019English

Deep Learning Methods for EEG Signals Classification of Motor Imagery in BCI

IJITEE (International Journal of Information Technology and Electrical Engineering)
2019English

Feature Selection, Statistical Modeling and Its Applications to Universal JPEG Steganalyzer

English

Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Applied Sciences (Switzerland)
InstrumentationMaterials ScienceFluid FlowEngineeringComputer Science ApplicationsProcess ChemistryTransfer ProcessesTechnology
2017English

Stimuli and Feature Extraction Methods for EEG-based Brain-Machine Interfaces: A Systematic Comparison.

English

Author Correction: Multivariate EEG Analyses Support High-Resolution Tracking of Feature-Based Attentional Selection

Scientific Reports
Multidisciplinary
2018English

Applying Statistical and Analytical Methods to U.S. Department of Veterans Affairs Databases

Biostatistics and Epidemiology
EpidemiologyHealth Informatics
2020English

Hybrid Brain-Computer Interface (BCI) Based on the EEG and EOG Signals

Bio-Medical Materials and Engineering
MedicineBiomaterialsBiomedical Engineering
2014English

Feature Selection Methods Applied to Severe Brain Damages Data

2017English

Amanote Research

Note-taking for researchers

Follow Amanote

© 2024 Amaplex Software S.P.R.L. All rights reserved.

Privacy PolicyRefund Policy