Brain Products Press Release October 2011, Volume 40
Products in Practice
EEG recorded during gross-motor behavior by Hoedlmoser, K.
1 , Birklbauer, J.
2 , Rigler, S.
1 , Mueller, E.
1 , & Schabus, M.
1
1 Laboratory for Sleep and Consciousness Research, Department of Psychology, University of Salzburg, Austria
2 Department of Sport Science and Kinesiology, University of Salzburg, Austria
The aim of this pilot study was to gain experience regarding the feasibility of recording brain activity during gross-motor behavior, as we are planning to focus on brain oscillations that accompany gross-motor learning in our upcoming projects.
had got used to riding the inverse-steering bicycle participated in this pilot study.
Motor skills can be distinguished by the size of the muscle groups required to perform different skills. Gross-motor learning represents skills that use the body’s large musculature, such as in the legs, arms or torso (Magill, 2011). That kind of skills is important for major body movements like walking, maintaining balance, coordination, jumping and reaching. It is generally accepted that tasks involving more than 50% of all skeletal muscles constitute a gross-motor skill. These accordingly represent a substantial proportion of the movement patterns that occur in everyday life, and the majority of tasks in competitive sports. In contrast to fine-motor skills, gross-motor skills are increasingly governed by gravitational and movement-dependent (inertial) forces
(Schollhorn et al., 2009), generating a more complex pattern for movement control. They therefore (initially at least) pose greater challenges to the cognitive capacity of a learner (Wulf & Shea,
2002). Traditionally, EEG experimental paradigms have severely restricted body, head and eye movements of subjects in order to avoid introducing non-brain artifacts into EEG recordings.
We therefore used the Brain Products „extreme EEG pack“ to overcome these limitations, enabling us to sophisticatedly record
EEG during gross-motor behavior.
Experimental design
The subjects performed two different riding conditions, each lasting about 5 minutes: (1) straight-line riding (10 x 30m), and (2) slalom riding (10 x 30m, cone distance: 3m). Both conditions were executed twice with both the normal- and the inverse-steering bicycle in a randomized order. Additionally, two 3-minute resting conditions (eyes open, eyes closed) were recorded prior to the introduction of the gross-motor behavior conditions.
EEG recording
EEG data was recorded from 16 active electrodes (F3, Fz, F4, C3,
Cz, C4, P3, Pz, P4, O1, Oz, O2, A1, A2, Vup, Vlo) at a sampling rate of 1000Hz (using the actiCAP system from Brain Products GmbH,
Germany). Electrode impedance was brought below 25 k Ω . The amplifier (a 16-channel research amplifier; V-Amp; Brain Products
GmbH, Germany) plus the actiCAP ControlBox and the recording laptop were placed in a backpack (see Figure 1).
METHODS
Description of the gross-motor task
EEG data was recorded during the riding of both a normal-steering and an inverse-steering bicycle (which had a steering ratio of 1:1).
As when riding a normal bicycle, compensatory movements are needed with an inverse-steering bicycle in order for the rider to maintain balance at speeds up to 6-8 m/s (Wilson et al., 2004). In contrast to inverted sensory inputs such as in mirror tracing, the motor output for performing the original task using the speciallyfitted steering apparatus is inverted. This initially makes the bicycle unrideable, and produces an inversion-evoked cognitive load due to the inhibition of previously automated movement patterns (Toxopeus et al., 2007). Since the lower extremities also have to be controlled in order to produce the required propulsion, more than 50% of the skeletal muscles are involved in the motor task (Seiler, 1995), which by definition makes it a gross-motor skill.
Subjects
Five right-handed men (age: M = 28.8 years; SD = 7.08; age range:
23-37 years) who had taken part in an earlier study in which they www.brainproducts.com
Figure 1. Experimental set-up and equipment. Photo on the left: subject riding the inverse-steering bicycle with a backpack containing all the EEG equipment.
Photo on the right: normal-steering bicycle (front) and inverse-steering bicycle
(back).
EEG analyses
The offline processing of the EEG data was performed using Brain
Vision Analyzer software (Version 2.01.3931; Brain Products
GmbH, Germany). All signals were filtered (0.5Hz highpass filter;
30Hz lowpass filter) and then re-referenced offline to a linked mastoids reference (A1, A2). Ocular artifacts were automatically minimized (Gratton, Coles & Donchin, 1983) and visually controlled in order to ensure reliable and valid data for further processing. Data were segmented into one-second epochs, and power spectra were calculated for each segment using Fast
Fourier Transformation (10% Hanning window). Averaged power page 11 of 18

Brain Products Press Release October 2011, Volume 40 spectra were obtained for each of the resting conditions, as well as for the different riding conditions (straight-line vs. slalom) and bicycle conditions (normal vs. inverse steering). The alpha frequency band was determined individually for each subject using individual alpha frequency IAF(i) [IAF(i) − 2Hz to IAF(i) +
2Hz]. In order to account for inter-subject variability, task-related alpha power changes between spectral power during the riding of the bicycle (SP_riding) and spectral power during the eyesopen resting condition (SP_resting) was expressed in percentage terms: alpha power change in % = [(SP_riding - SP_resting) /
SP_resting] x 100.
RESULTS
Initial exploratory analyses revealed descriptive differences between the two riding conditions (slalom vs. straight-line) and the bicycle conditions (inverse vs. normal steering). As Figure 2 depicts, alpha power change with respect to a resting reference was found to be more prominent during inverse cycling than during normal-steering cycling (see Figure 2).
was surprisingly good, and we were therefore able to perform preliminary data analyses concerning our hypothesis with respect to EEG alpha power during the riding of an inverse-steering bicycle versus the riding of a normal-steering bicycle. These analyses revealed a strong task-related alpha synchronization that was more prominent during inverse steering than during normal steering (see. Figure 2), although the sample was very small (N = 5).
As the inverse-steering bicycle provides an inversion-evoked cognitive load due to the inhibition of highly automated movement patterns, the observed power increase in the alpha band with respect to a resting reference can be considered as a cortical correlate of the inhibition of already familiar automated memory traces. Throughout the utilized gross-motor task, the subjects had to switch between inverse-steering and normal-steering cycling modes within a very short period of time. It can therefore be hypothesized that the subjects were in a specific processing mode in which they had to inhibit both (i) highly automated movement patterns (riding a normal-steering bicycle) during inverse-steering cycling, and (ii) recently-acquired movement patterns (riding an inverse-steering bicycle) during normal-steering cycling. Our pilot data indicate that it is feasible to successfully investigate the brain activity that accompanies gross-motor learning, and to employ a sophisticated approach while conducting such an investigation.
Figure 2. Alpha power change [%] for the 4 different conditions: slalom riding with the inverse-steering bicycle (A) and the normal-steering bicycle (C), as well as straight-line riding with the inverse-steering bicycle (B) and the normalsteering bicycle (D). Warm colors denote alpha power increases. Note that the alpha power change is generally more pronounced at the frontal sites, and is strongest when the inverse-steering bicycle is being ridden, especially during slalom riding.
CONCLUSION
The main intention of this pilot study was to gain experience regarding the feasibility of recording brain activity throughout the course of a gross-motor task. Overall, the data quality
ACKNOWLEDGEMENTS
This investigation was funded by the “Stiftungs- und Förderungsgesellschaft“ at the Paris–Lodron-University, Salzburg.
References
Gratton, G., Coles, M. G. H., & Donchin, E. (1983). A new method for off line removal of ocular artifact. Electroencephalography and Clinical
Neurophysiology, 55, 468-484.
Magill, R.A. Motor learning and control. 9th ed. New York, NY: McGraw-Hill,
2011.
Schollhorn, W.I., Mayer-Kress, G., Newell, K.M., & Michelbrink, M. Time scales of adaptive behavior and motor learning in the presence of stochastic perturbations. Hum MovSci 2009, 28(3), 319-33.
Seiler, R. Kognitive Organisation von Bewegungshandlungen. Empirische
Untersuchungen mit dem Inversions prinzip. Sankt Augustin, Germany:
Academia Verlag, 1995.
Toxopeus, C.M., de Vries, P.M., de Jong, B.M., et al. Cerebral activation patterns related to initiation and inhibition of hand movement. Neuroreport
2007, 18(15), 1557-60.
Wilson, D.G., Papadopoulos, J., Whitt, F.R. Bicycling science. 3rd ed. Cambridge,
Mass.: MIT Press, 2004.
Wulf, G., & Shea, C.H. Principles derived from the study of simple skills do not generalize to complex skill learning. Psychon Bull Rev 2002, 9(2), 185-211.
www.brainproducts.com
page 12 of 18