2014, Number S1
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Rev Invest Clin 2014; 66 (S1)
Characterization of electrical brain activity related to hand motor imagery in healthy subjects
Cantillo-Negrete J, Gutiérrez-Martínez J, Flores-Rodríguez TB, Cariño-Escobar RI, Elías-Viñas D
Language: Spanish
References: 31
Page: 111-121
PDF size: 449.37 Kb.
ABSTRACT
Brain computer interface systems (BCI) translate the
intentions of patients affected with locked-in syndrome
through the EEG signal characteristics, which are converted
into commands used to control external devices. One of
the strategies used, is to decode the motor imagery of the subject,
which can modify the neuronal activity in the sensory-motor
areas in a similar way to which it is observed in real
movement. The present study shows the activation patterns
that are registered in motor and motor imagery tasks of
right and left hand movement in a sample of young healthy
subjects of Mexican nationality. By means of frequency
analysis it was possible to determine the difference
conditions of motor imagery and movement. Using U Mann-
Whitney tests, differences with statistical significance (p ‹
0.05) where obtained, in the EEG channels C3, Cz, C4, T3
and P3 in the mu and beta rhythms, for subjects with
similar characteristics (age, gender, and education). With
these results, it would be possible to define a classifier or
decoder by gender that improves the performance rate and
diminishes the training time, with the goal of designing a
functional BCI system that can be transferred from the
laboratory to the clinical application in patients with motor
disabilities.
REFERENCES
Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan T. Brain-computer interfaces for communication and control. Clin Neurophysiol 2002; 113: 767-91.
Farwell LA, Donchin E. Talking off the top of your head: Toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr Clin Neurophysiol 1988; 70: 510-23.
Thulasidas M, Guan C, Wu J. Robust classification of EEG signal for brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 2006; 14: 24-9.
Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, et al. A spelling device for the paralysed. Nature 1999; 398: 297-8.
Galán F, Nuttin M, Lew E, Ferrez PW, et al. An asynchronous and non invasive brain-actuated wheelchair. Clin Neurophysiol 2008; 119: 2159-69.
Iturrake I, Antelis JM, Andrea K, Miguez J. A Noninvasive Brain-Actuated Wheelchair Based on a P300 Neurophysiological Protocol and Automated Navigation. IEEE Trans Robot 2009; 25(3): 614-27.
Taylor D, Helms-Tillery SI, Schwartz AB. Direct cortical control of 3D neuroprosthetic devices. Science 2002; 296: 1829-32.
Pfurtscheller G, Müller-Putz GR, Pfurtscheller J, Rupp R. EEG-Based asynchronous BCI controls functional electrical stimulation in a tetraplec patien. EURASIP J Appl Sig 2005: 3152-5.
Neuper C, Müller-Putz G, et al. Motor imagery and EEG-based control of spelling devices and neuroprostheses. Prog Brain Res 2006; 159: 393-409.
Heasaman JM, Scott TR, Kirkup L, Flynn RY, et al. Control of a hand grasp neuroprostheses using an electroencephalogramtriggered switch: demonstration of improvements in performance using wavepacket analysis. Med Biol Eng Comput 2002; 40: 588-93.
Pfurtscheller G, Guger C, Müller G, Krausz G, Neuper C. Brain Oscillations Control Hand Orthosis in a Tetraplegic. Neurosci Lett 2000; 292: 211-4.
Chih-Wei C, Chou-Ching KL, Ming-Shaung J. Hand Orthosis Controlled Using Brain-Computer Interface. J Med Biol Eng 2009; 29(5): 234-41.
Pfurtscheller G, Neuper C. Motor imagery activates primary sensorimotor area in humans. Neurosci Lett 1997; 239: 65-8.
Pfurtscheller G, Brunner C, Schlogl A, Lopez da Silva FH. Mu rhythm (de)syncronization and EEG single-trial classification of different motorimagery tasks. Neuroimage 2006; 31(1): 153-9.
Pineda JA, Allison BZ, Vankov A. The effects of self-movement observation and imagination on mu rhythms and readiness potentials (RP’s): toward a brain-computer interface (BCI). IEEE Trans Rehabil Eng 2000; 8(2): 219-22.
Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchonization and desynchronization: basic principles. Clin Neurophysiol 1999; 110: 1842-57.
Carmena JM, Lebedev MA, Crist RE, et al. Learning to control a brain-machine interface for reaching and grasping by primates. PLoS Biol 2003; 1: E42. Cantillo-Negrete J, et al. Actividad eléctrica cerebral e imaginación del movimiento de la mano. Rev Invest Clin 2014; 66 (Supl. 1): s111-s121 s121
Neuper C, Müller GR, Küber A, et al. Clinical application of an EEG-based brain–computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol 2003; 114: 399-409.
Johnson SH, SprehnG, Andrew SJ. Intact Motor Imagery in Chronic Upper Limb Hemiplegics: Evidence for Activity-Independent Action Representations. J Cogn Neurosci 2002; 14(6): 841-52.
Schlögl A, Lee F, Bischof H, Pfustscheller G. Characterization of four-class motor imagery EEG data for the BCI-competition 2005. J Neural Eng 2005; 1: L14-L22.
Lalor EC, Kelly SP, Finucane C, et al. Steady-state VEP-based brain-computer interface control in an immersive 3D gaming environment. EURASIP J Appl Sig P 2005; 2005: 3156-64.
Gysels E, Celka P. Phase Synchronization for the Recognition of Mental Tasks in a Brain–Computer Interface. IEEE Trans Neural Syst Rehabil Eng 2004; 12(4): 406-15.
Lemm S, Schäfer C, Curio G. Probabilistic modeling of sensorimotor mu-rhythms for classification of imaginary hand movements. IEEE Trans Biomed Eng 2004; 51: 1077-80.
Ostrosky-Solis F, Gómez-Pérez E, Ardilla A, et al. Batería Neuropsicológica NEUROPSI Atencion y Memoria, 6 a 85 años de edad. Bookstore, 2003.
Pfurtscheller G, Neuper C. Motor imagery and direct braincomputer communication. Proc IEEE 2001; 89(7): 1123-34.
Jung TP, Makeig S, Humphries C, Lee TW, Mckeown MJ, et al. Removing Electroencephalographic Artifacts by Blind Source Separation. Psychophysiology 2000; 37: 163-78.
Makeig S, Westerfield M, Jung JP, et al. Functionally Independent Components of the Late Positive Event-Related Potential during Visual Spatial Attention. J Neurosci 1999; 19(7): 2665-80.
Oostenveld R, Fries P, Eric M, Schoffelen JM. Field Trip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data. Comput Intell Neurosci 2011; 2011: 9.
McFarland DJ, Miner LA, Vaughan TM, Wolpaw JR. Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements. Brain Topogr 2000; 12(3): 177-86.
Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, et al. The thought translation device (TTD). IEEE Trans Rehabil Eng 2000; 8: 190-3.
Fazli S, Popescu F, Danóczy M, Blankertz B, Müller KR, Grozea C. Subject-independent mental state classification in single trials. Neural Net 2009; 22(9): 1305-12.