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Items: 1 to 20 of 105

1.

Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata.

Bhattacharyya S, Sengupta A, Chakraborti T, Konar A, Tibarewala DN.

Med Biol Eng Comput. 2014 Feb;52(2):131-9. doi: 10.1007/s11517-013-1123-9. Epub 2013 Oct 29.

PMID:
24165805
2.

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection.

Ortega J, Asensio-Cubero J, Gan JQ, Ortiz A.

Biomed Eng Online. 2016 Jul 15;15 Suppl 1:73. doi: 10.1186/s12938-016-0178-x.

3.

Feature extraction and subset selection for classifying single-trial ECoG during motor imagery.

Wei Q, Gao X, Gao S.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:1589-92.

PMID:
17946051
4.

An empirical bayesian framework for brain-computer interfaces.

Lei X, Yang P, Yao D.

IEEE Trans Neural Syst Rehabil Eng. 2009 Dec;17(6):521-9. doi: 10.1109/TNSRE.2009.2027705. Epub 2009 Jul 17.

PMID:
19622442
5.

Classification of EEG with structural feature dictionaries in a brain computer interface.

Göksu F, Ince NF, Tadipatri VA, Tewfik AH.

Conf Proc IEEE Eng Med Biol Soc. 2008;2008:1001-4. doi: 10.1109/IEMBS.2008.4649324.

PMID:
19162827
6.

An Idle-State Detection Algorithm for SSVEP-Based Brain-Computer Interfaces Using a Maximum Evoked Response Spatial Filter.

Zhang D, Huang B, Wu W, Li S.

Int J Neural Syst. 2015 Nov;25(7):1550030. doi: 10.1142/S0129065715500306. Epub 2015 Jul 5.

PMID:
26246229
7.

A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching.

Yin X, Xu B, Jiang C, Fu Y, Wang Z, Li H, Shi G.

J Neural Eng. 2015 Jun;12(3):036004. doi: 10.1088/1741-2560/12/3/036004. Epub 2015 Apr 2.

PMID:
25834118
8.

Time sparsification of EEG signals in motor-imagery based brain computer interfaces.

Higashi H, Tanaka T.

Conf Proc IEEE Eng Med Biol Soc. 2012;2012:4271-4. doi: 10.1109/EMBC.2012.6346910.

PMID:
23366871
9.

Improving classification accuracy using fuzzy method for BCI signals.

Wei Y, Jun Y, Lin S, Hong L.

Biomed Mater Eng. 2014;24(6):2937-43. doi: 10.3233/BME-141113.

PMID:
25227000
10.

Simultaneously optimizing spatial spectral features based on mutual information for EEG classification.

Meng J, Yao L, Sheng X, Zhang D, Zhu X.

IEEE Trans Biomed Eng. 2015 Jan;62(1):227-40. doi: 10.1109/TBME.2014.2345458. Epub 2014 Aug 5.

PMID:
25122834
11.

Embedded grey relation theory in Hopfield neural network: application to motor imagery EEG recognition.

Hsu WY.

Clin EEG Neurosci. 2013 Oct;44(4):257-64. doi: 10.1177/1550059413477090. Epub 2013 Mar 26.

PMID:
23536381
12.

Channel selection for optimizing feature extraction in an electrocorticogram-based brain-computer interface.

Wei Q, Lu Z, Chen K, Ma Y.

J Clin Neurophysiol. 2010 Oct;27(5):321-7. doi: 10.1097/WNP.0b013e3181f52f2d.

PMID:
20844441
13.

Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces.

Wei Q, Wei Z.

Biomed Mater Eng. 2015;26 Suppl 1:S1523-32. doi: 10.3233/BME-151451.

PMID:
26405916
14.

Classifying single-trial EEG during motor imagery by iterative spatio-spectral patterns learning (ISSPL).

Wu W, Gao X, Hong B, Gao S.

IEEE Trans Biomed Eng. 2008 Jun;55(6):1733-43.

PMID:
18714838
15.

Transferring subspaces between subjects in brain--computer interfacing.

Samek W, Meinecke FC, Muller KR.

IEEE Trans Biomed Eng. 2013 Aug;60(8):2289-98. doi: 10.1109/TBME.2013.2253608. Epub 2013 Mar 20.

PMID:
23529075
16.

A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines.

Lu N, Li T, Ren X, Miao H.

IEEE Trans Neural Syst Rehabil Eng. 2017 Jun;25(6):566-576. doi: 10.1109/TNSRE.2016.2601240. Epub 2016 Aug 17.

PMID:
27542114
17.

Preprocessing and meta-classification for brain-computer interfaces.

Hammon PS, de Sa VR.

IEEE Trans Biomed Eng. 2007 Mar;54(3):518-25.

PMID:
17355065
18.

A novel Bayesian framework for discriminative feature extraction in Brain-Computer Interfaces.

Suk HI, Lee SW.

IEEE Trans Pattern Anal Mach Intell. 2013 Feb;35(2):286-99.

PMID:
22431526
19.

A Boosting-Based Spatial-Spectral Model for Stroke Patients' EEG Analysis in Rehabilitation Training.

Liu Y, Zhang H, Chen M, Zhang L.

IEEE Trans Neural Syst Rehabil Eng. 2016 Jan;24(1):169-79. doi: 10.1109/TNSRE.2015.2466079. Epub 2015 Aug 20.

PMID:
26302519
20.

Channel selection by genetic algorithms for classifying single-trial ECoG during motor imagery.

Wei Q, Tu W.

Conf Proc IEEE Eng Med Biol Soc. 2008;2008:624-7. doi: 10.1109/IEMBS.2008.4649230.

PMID:
19162733

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