Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.
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Brain computer interfaces, a review.Creating the feedback loop: closed-loop neurostimulationLocal field potentials in primate motor cortex encode grasp kinetic parameters.The Brain Is Faster than the Hand in Split-Second Intentions to Respond to an Impending Hazard: A Simulation of Neuroadaptive Automation to Speed Recovery to Perturbation in Flight Attitude.Music mnemonics aid Verbal Memory and Induce Learning - Related Brain Plasticity in Multiple SclerosisSongs induced mood recognition system using EEG signals.Ant Colony Optimization Based Feature Selection Method for QEEG Data ClassificationComparison of EEG-features and classification methods for motor imagery in patients with disorders of consciousness.A Real-Time Magnetoencephalography Brain-Computer Interface Using Interactive 3D Visualization and the Hadoop EcosystemAutomatic migraine classification via feature selection committee and machine learning techniques over imaging and questionnaire dataEEG-based classification for elbow versus shoulder torque intentions involving stroke subjects.A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.Channel selection methods for the P300 Speller.A semi-supervised support vector machine approach for parameter setting in motor imagery-based brain computer interfaces.Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEGClassification of EEG signals using a multiple kernel learning support vector machineSelection of Efficient Features for Discrimination of Hand Movements from MEG Using a BCI Competition IV Data Set.Sitting and standing intention can be decoded from scalp EEG recorded prior to movement executionPerformance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief.Electrode replacement does not affect classification accuracy in dual-session use of a passive brain-computer interface for assessing cognitive workloadA comparison of univariate, vector, bilinear autoregressive, and band power features for brain-computer interfaces.An algorithm for idle-state detection in motor-imagery-based brain-computer interface.Evolutionary optimization of classifiers and features for single-trial EEG discriminationToward a model-based predictive controller design in brain-computer interfaces.Brain-computer interface systems: progress and prospects.Parkinsonism-related features of neuronal discharge in primates.EEG-based analysis of human driving performance in turning left and right using Hopfield neural network.Toward a semi-self-paced EEG brain computer interface: decoding initiation state from non-initiation state in dedicated time slots.Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson's Disease.Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation.Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.Random forests for feature selection in QSPR Models - an application for predicting standard enthalpy of formation of hydrocarbonsDetecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals.Automatic detection of sleep apnea based on EEG detrended fluctuation analysis and support vector machine.Multiple Kernel Based Region Importance Learning for Neural Classification of Gait States from EEG Signals.A Genetic-Based Feature Selection Approach in the Identification of Left/Right Hand Motor Imagery for a Brain-Computer Interface.Channel selection and feature projection for cognitive load estimation using ambulatory EEG.Classification of mental tasks from EEG signals using extreme learning machine.Navigating features: a topologically informed chart of electromyographic features space.Evaluating Brain-Computer Interface Performance in an ALS Population: Checkerboard and Color Paradigms.
P2860
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P2860
Comparison of linear, nonlinear, and feature selection methods for EEG signal classification.
description
2003 nî lūn-bûn
@nan
2003 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2003 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2003年の論文
@ja
2003年論文
@yue
2003年論文
@zh-hant
2003年論文
@zh-hk
2003年論文
@zh-mo
2003年論文
@zh-tw
2003年论文
@wuu
name
Comparison of linear, nonlinea ...... for EEG signal classification.
@ast
Comparison of linear, nonlinea ...... for EEG signal classification.
@en
type
label
Comparison of linear, nonlinea ...... for EEG signal classification.
@ast
Comparison of linear, nonlinea ...... for EEG signal classification.
@en
prefLabel
Comparison of linear, nonlinea ...... for EEG signal classification.
@ast
Comparison of linear, nonlinea ...... for EEG signal classification.
@en
P2093
P1476
Comparison of linear, nonlinea ...... for EEG signal classification.
@en
P2093
Charles W Anderson
David A Peterson
Deon Garrett
Michael H Thaut
P304
P356
10.1109/TNSRE.2003.814441
P577
2003-06-01T00:00:00Z