about
Multivoxel pattern analysis for FMRI data: a reviewMachine learning approaches: from theory to application in schizophreniaBrain computer interfaces, a review.Multivariate pattern recognition for diagnosis and prognosis in clinical neuroimaging: state of the art, current challenges and future trendsTowards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject levelData mining EEG signals in depression for their diagnostic valueReliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validationUsing neurophysiological signals that reflect cognitive or affective state: six recommendations to avoid common pitfallsReview of the BCI Competition IV.The two-brain approach: how can mutually interacting brains teach us something about social interaction?Clinically useful brain imaging for neuropsychiatry: How can we get there?Simultaneous real-time monitoring of multiple cortical systems.Decoding of top-down cognitive processing for SSVEP-controlled BMIAn empirical comparison of different approaches for combining multimodal neuroimaging data with support vector machineFirst steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage.Decoding the infant mind: Multivariate pattern analysis (MVPA) using fNIRS.Extracting neurophysiological signals reflecting users' emotional and affective responses to BCI use: A systematic literature review.Feature selection of fMRI data based on normalized mutual information and fisher discriminant ratio.Exploring stability-based voxel selection methods in MVPA using cognitive neuroimaging data: a comprehensive study.Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data.Promises, Pitfalls, and Basic Guidelines for Applying Machine Learning Classifiers to Psychiatric Imaging Data, with Autism as an Example.Computational psychiatry as a bridge from neuroscience to clinical applications.A feasibility study of an improved procedure for using EEG to detect brain responses to imagery instruction in patients with disorders of consciousness.Electroencephalogram-based decoding cognitive states using convolutional neural network and likelihood ratio based score fusion.Individual classification of children with epilepsy using support vector machine with multiple indices of diffusion tensor imaging.On the generalizability of resting-state fMRI machine learning classifiers.Decoding sequence learning from single-trial intracranial EEG in humans.Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data.Structural imaging techniques in schizophrenia.Motor imagery for severely motor-impaired patients: evidence for brain-computer interfacing as superior control solution.Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.Single nucleotide variations: biological impact and theoretical interpretation.Sparse network-based models for patient classification using fMRI.Multi-Source Learning for Joint Analysis of Incomplete Multi-Modality Neuroimaging Data.Volume of interest-based [18F]fluorodeoxyglucose PET discriminates MCI converting to Alzheimer's disease from healthy controls. A European Alzheimer's Disease Consortium (EADC) study.Dynamic change of global and local information processing in propofol-induced loss and recovery of consciousness.Voxelwise multivariate analysis of multimodality magnetic resonance imaging.Predicting BCI subject performance using probabilistic spatio-temporal filters.Neuroimaging-based methods for autism identification: a possible translational application?An efficient ERP-based brain-computer interface using random set presentation and face familiarity.
P2860
Q26829721-2EDC1FDF-D327-43BC-9C69-D664C0F2A89EQ26991612-E2BD979E-3E1A-41B1-8116-7027DD4B1AF5Q26995073-7655619B-0831-4BC3-B7D9-CE94EEF24E52Q27008516-CF7B2CDE-673B-4613-A17D-AD6B9726CF57Q27026841-3F6E4C89-C94C-410A-AA6B-7ADEC48E6355Q27307952-0153F7A9-D74C-478A-987A-276AC1186E2CQ27902330-9F71A224-C8E0-42B2-B2F3-94F1BB9CF69EQ28083826-5E609A7F-79A2-47F5-909F-6B0BBCF7390EQ28727178-7055B061-4C34-45CE-9236-C111D76A45AAQ28778251-20EBBA83-D651-480D-BE98-3132DE4ACDC6Q30491457-F53E5191-7FB7-4B87-84A0-9065C6D95531Q30587875-2669EE7A-F17F-4E0F-A09E-EB8D4171797BQ30827324-A1A3E07C-DF0F-4B7F-A128-8D123AF5B3ECQ30839607-5D26777F-3A1D-4F94-B1EF-B7CE9DC8D6CDQ30844517-0EADC48D-E6CC-42B2-AB5D-728339A0E339Q30846483-F66BD997-A8AF-4A99-BA13-C713541C8E1BQ31013625-8DACDC25-06DA-479C-8553-6C64D01588BEQ31105292-220B7DA7-AFD6-43D6-B6E6-2F0D25C7282CQ31137086-6675E502-D63F-4F6A-BD76-6A2D23BAD727Q31138923-B6249259-CF2D-4AB7-93C3-13379B9CC122Q31149873-AF128ED8-1558-4E71-B518-52843C24ADD4Q33724477-B44F4E8D-C48A-45E8-9F5A-7E6448C78F4CQ33734146-718FAD69-427B-4522-8292-96451FD75772Q33739321-F2E11336-ABDB-4D67-8390-F9CB76369A9EQ33741452-F3D15D7A-7F21-46BB-901E-CC74BEAB2E3AQ33966116-AF5A762B-6AC6-47F2-A685-C13F064FE7D7Q34103232-48DCBEE0-9B48-4F7A-8762-BD2E9ED4FA8DQ34231349-D71D908F-837E-4970-B001-8A1B6DAE25F7Q34245657-705F09AD-DA8C-4B2E-824A-07383C891CD2Q34435365-6B0E8558-E969-4B1B-8EC1-26F0505E7C02Q34566499-102C25E4-7A36-4EBC-B6F3-C623A84A440CQ34621290-832906DF-0F92-421A-9166-276CCD60979EQ34767051-A6C5E0EA-1AC9-46AE-B2A5-55A9348F2FC9Q34981107-C54DCD69-18D0-441E-8BF9-F60670F5B3FCQ34988405-7C3FF50E-33E9-458E-A99C-B751466CF088Q35022277-E7671727-C3AD-49A7-A2E3-3594B2A466C0Q35072393-FDA265A3-8EFF-46B6-A835-DF3DCBCEFFFCQ35097242-DAF877CE-126F-4886-AD6B-1D9A7B48EDCAQ35208187-46F3918B-CB22-46BE-9DD1-EFC7D4B1F6D9Q35407678-8748E59D-37D9-422E-909C-03512E09F3EC
P2860
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 21 December 2010
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
Introduction to machine learning for brain imaging.
@en
Introduction to machine learning for brain imaging.
@nl
type
label
Introduction to machine learning for brain imaging.
@en
Introduction to machine learning for brain imaging.
@nl
prefLabel
Introduction to machine learning for brain imaging.
@en
Introduction to machine learning for brain imaging.
@nl
P2093
P1433
P1476
Introduction to machine learning for brain imaging.
@en
P2093
Benjamin Blankertz
Steven Lemm
Thorsten Dickhaus
P304
P356
10.1016/J.NEUROIMAGE.2010.11.004
P407
P577
2010-12-21T00:00:00Z