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Items: 16

1.

Improving Real-Time Lower Limb Motor Imagery Detection Using tDCS and an Exoskeleton.

Rodríguez-Ugarte M, Iáñez E, Ortiz M, Azorín JM.

Front Neurosci. 2018 Oct 23;12:757. doi: 10.3389/fnins.2018.00757. eCollection 2018.

2.

Effects of tDCS on Real-Time BCI Detection of Pedaling Motor Imagery.

Rodriguez-Ugarte MS, Iáñez E, Ortiz-Garcia M, Azorín JM.

Sensors (Basel). 2018 Apr 8;18(4). pii: E1136. doi: 10.3390/s18041136.

3.

Application of the Stockwell Transform to Electroencephalographic Signal Analysis during Gait Cycle.

Ortiz M, Rodríguez-Ugarte M, Iáñez E, Azorín JM.

Front Neurosci. 2017 Nov 28;11:660. doi: 10.3389/fnins.2017.00660. eCollection 2017.

4.

Personalized Offline and Pseudo-Online BCI Models to Detect Pedaling Intent.

Rodríguez-Ugarte M, Iáñez E, Ortíz M, Azorín JM.

Front Neuroinform. 2017 Jul 11;11:45. doi: 10.3389/fninf.2017.00045. eCollection 2017.

5.

Low Intensity Focused tDCS Over the Motor Cortex Shows Inefficacy to Improve Motor Imagery Performance.

Angulo-Sherman IN, Rodríguez-Ugarte M, Iáñez E, Azorín JM.

Front Neurosci. 2017 Jul 6;11:391. doi: 10.3389/fnins.2017.00391. eCollection 2017.

6.

Effect of tDCS stimulation of motor cortex and cerebellum on EEG classification of motor imagery and sensorimotor band power.

Angulo-Sherman IN, Rodríguez-Ugarte M, Sciacca N, Iáñez E, Azorín JM.

J Neuroeng Rehabil. 2017 Apr 19;14(1):31. doi: 10.1186/s12984-017-0242-1.

7.

Detection of intention of pedaling start cycle through EEG signals.

Rodriguez-Ugarte M, Hortal E, Costa A, Ianez E, Ubeda A, Azorin JM.

Conf Proc IEEE Eng Med Biol Soc. 2016 Aug;2016:1496-1499. doi: 10.1109/EMBC.2016.7590993.

PMID:
28268610
8.

EEG-Based Detection of Starting and Stopping During Gait Cycle.

Hortal E, Úbeda A, Iáñez E, Azorín JM, Fernández E.

Int J Neural Syst. 2016 Nov;26(7):1650029. doi: 10.1142/S0129065716500295. Epub 2016 Apr 4.

PMID:
27354191
9.

Decoding the Attentional Demands of Gait through EEG Gamma Band Features.

Costa Á, Iáñez E, Úbeda A, Hortal E, Del-Ama AJ, Gil-Agudo Á, Azorín JM.

PLoS One. 2016 Apr 26;11(4):e0154136. doi: 10.1371/journal.pone.0154136. eCollection 2016.

10.

Assessing movement factors in upper limb kinematics decoding from EEG signals.

Úbeda A, Hortal E, Iáñez E, Perez-Vidal C, Azorín JM.

PLoS One. 2015 May 28;10(5):e0128456. doi: 10.1371/journal.pone.0128456. eCollection 2015.

11.

A supplementary system for a brain-machine interface based on jaw artifacts for the bidimensional control of a robotic arm.

Costa Á, Hortal E, Iáñez E, Azorín JM.

PLoS One. 2014 Nov 12;9(11):e112352. doi: 10.1371/journal.pone.0112352. eCollection 2014. Erratum in: PLoS One. 2015;10(2):e0118257.

12.

Control of a 2 DoF robot using a brain-machine interface.

Hortal E, Ubeda A, Iáñez E, Azorín JM.

Comput Methods Programs Biomed. 2014 Sep;116(2):169-76. doi: 10.1016/j.cmpb.2014.02.018. Epub 2014 Mar 12.

PMID:
24694722
13.

Using eye movement to control a computer: a design for a lightweight electro-oculogram electrode array and computer interface.

Iáñez E, Azorin JM, Perez-Vidal C.

PLoS One. 2013 Jul 3;8(7):e67099. doi: 10.1371/journal.pone.0067099. Print 2013.

14.

Endogenous brain-machine interface based on the correlation of EEG maps.

Ubeda A, Iáñez E, Azorín JM, Perez-Vidal C.

Comput Methods Programs Biomed. 2013 Nov;112(2):302-8. doi: 10.1016/j.cmpb.2013.01.012. Epub 2013 Feb 28.

PMID:
23453295
15.

Mental tasks classification for BCI using image correlation.

Úbeda A, Iáñez E, Azorin JM.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:6303-6. doi: 10.1109/IEMBS.2011.6091555.

PMID:
22255779
16.

Multimodal human-machine interface based on a brain-computer interface and an electrooculography interface.

Iáñez E, Ùbeda A, Azorín JM.

Conf Proc IEEE Eng Med Biol Soc. 2011;2011:4572-5. doi: 10.1109/IEMBS.2011.6091132.

PMID:
22255355

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