» Articles » PMID: 20859445

Temporal Coding of Brain Patterns for Direct Limb Control in Humans

Overview
Journal Front Neurosci
Date 2010 Sep 23
PMID 20859445
Citations 17
Authors
Affiliations
Soon will be listed here.
Abstract

For individuals with a high spinal cord injury (SCI) not only the lower limbs, but also the upper extremities are paralyzed. A neuroprosthesis can be used to restore the lost hand and arm function in those tetraplegics. The main problem for this group of individuals, however, is the reduced ability to voluntarily operate device controllers. A brain-computer interface provides a non-manual alternative to conventional input devices by translating brain activity patterns into control commands. We show that the temporal coding of individual mental imagery pattern can be used to control two independent degrees of freedom - grasp and elbow function - of an artificial robotic arm by utilizing a minimum number of EEG scalp electrodes. We describe the procedure from the initial screening to the final application. From eight naïve subjects participating online feedback experiments, four were able to voluntarily control an artificial arm by inducing one motor imagery pattern derived from one EEG derivation only.

Citing Articles

A Bibliometric Review of Brain-Computer Interfaces in Motor Imagery and Steady-State Visually Evoked Potentials for Applications in Rehabilitation and Robotics.

Chio N, Quiles-Cucarella E Sensors (Basel). 2025; 25(1.

PMID: 39796947 PMC: 11722989. DOI: 10.3390/s25010154.


A Comprehensive Review of Endogenous EEG-Based BCIs for Dynamic Device Control.

Padfield N, Camilleri K, Camilleri T, Fabri S, Bugeja M Sensors (Basel). 2022; 22(15).

PMID: 35957360 PMC: 9370865. DOI: 10.3390/s22155802.


Neural correlates of user learning during long-term BCI training for the Cybathlon competition.

Tortora S, Beraldo G, Bettella F, Formaggio E, Rubega M, Del Felice A J Neuroeng Rehabil. 2022; 19(1):69.

PMID: 35790978 PMC: 9254548. DOI: 10.1186/s12984-022-01047-x.


Optimization of machine learning method combined with brain-computer interface rehabilitation system.

Wang C, Tsai K J Phys Ther Sci. 2022; 34(5):379-385.

PMID: 35527849 PMC: 9057683. DOI: 10.1589/jpts.34.379.


Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control.

Muller-Putz G, Kobler R, Pereira J, Lopes-Dias C, Hehenberger L, Mondini V Front Hum Neurosci. 2022; 16:841312.

PMID: 35360289 PMC: 8961864. DOI: 10.3389/fnhum.2022.841312.


References
1.
KRAUSZ G, Scherer R, Korisek G, Pfurtscheller G . Critical decision-speed and information transfer in the "Graz Brain-Computer Interface". Appl Psychophysiol Biofeedback. 2003; 28(3):233-40. DOI: 10.1023/a:1024637331493. View

2.
Neuper C, Muller G, Kubler A, Birbaumer N, Pfurtscheller G . Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment. Clin Neurophysiol. 2003; 114(3):399-409. DOI: 10.1016/s1388-2457(02)00387-5. View

3.
Kirsch R . Development of a neuroprosthesis for restoring arm and hand function via functional electrical stimulation following high cervical spinal cord injury. Conf Proc IEEE Eng Med Biol Soc. 2007; 2005:4142-4. DOI: 10.1109/IEMBS.2005.1615375. View

4.
Heasman J, Scott T, Kirkup L, Flynn R, Vare V, Gschwind C . Control of a hand grasp neuroprosthesis using an electroencephalogram-triggered switch: demonstration of improvements in performance using wavepacket analysis. Med Biol Eng Comput. 2002; 40(5):588-93. DOI: 10.1007/BF02345459. View

5.
Birbaumer N, Ghanayim N, Hinterberger T, Iversen I, Kotchoubey B, Kubler A . A spelling device for the paralysed. Nature. 1999; 398(6725):297-8. DOI: 10.1038/18581. View