Affiliations: [a]
Lunex International University of Health, Exercise and Sports, Differdange, Luxembourg
| [b] Technische Universität München, Munich, Germany
Correspondence:
[*]
Address for correspondence: Felippe Toledo, 50, avenue du Parc des Sports, 4671, Differdange, Luxembourg. Tel.: +352 49 176 64488 703; E-mail: [email protected].
Abstract: BACKGROUND:Action observation describes a concept where the subsequent motor behavior of an individual can be modulated though observing an action. This occurs through the activation of neurons in the action observation network, acting on a variety of motor learning processes. This network has been proven highly useful in the rehabilitation of patients with acquired brain injury, placing “action observation” as one of the most effective techniques for motor recovery in physical neurorehabilitation. OBJECTIVE:The aim of this paper is to define an EEG marker for motor learning, guided through observation. METHODS:Healthy subjects (n = 41) participated voluntarily for this research. They were asked to repeat an unknown motor behavior, immediately after observing a video. During the observation, EEG raw signals where collected with a portable EEG and the results were later compared with success and fail on repeating the motor procedure. The comparison was then analyzed with the Mann-Whitney U test for non-parametrical data, with a confidence interval of 95%. RESULTS:A significant relation between motor performance and neural activity was found for Alpha (p = 0,0149) and Gamma (0,0005) oscillatory patterns. CONCLUSION:Gamma oscillations with frequencies between 41 and 49,75 Hz, seem to be an adequate EEG marker for motor performance guided through the action observation network. The technology used for this paper is easy to use, low-cost and presents valid measurements for the recommended oscillatory frequencies, implying a possible use on rehabilitation, by collecting data in real-time during therapeutic interventions and assessments.
Keywords: EEG-Marker, action observation network, motor learning, neurorehabilitation
