The method of electroencephalography (EEG) is
the noninvasive recording of electrocortical activity of
the brain by placing multiple electrodes (typically
32 128 channels) on the scalp surface. Most of the
time, the recorded brain activity is related to the onset
of a specific event (e.g., the presentation of an experimental
stimulus or a motor response such as a button
press). Subsequently, EEG-data can be further analyzed
as so-called event-related potentials (ERP) or as neural
oscillations in the frequency domain by means of a
spectral or time-frequency analysis (for a comprehensive
introduction, the reader is referred to Luck, 2005).
Besides functional magnetic resonance imaging (fMRI)
and transcranial magnetic stimulation (TMS), the EEG
is one of most famous and frequently used research
techniques originating from electrophysiology and modern
cognitive neuroscience. As such, the use of EEG is
of great importance for electrophysiological studies in
the growing field of sport psychology and related fields
(e.g., movement science). In recent years, this method
has already been used to investigate the underlying
basis of electrophysiological activity associated with
movement-related patterns and information (e.g., movement
sounds) and furthermore specifying their spatiotemporal
dynamics in the human brain (e.g., see Justen
& Herbert, 2016).
Modern cognitive neuroscience and sport psychology
are increasingly interdependent and therefore are
increasingly influencing each other (Jansen-Osmann,
2008). Particularly, electrophysiological studies in the
area of human movement control have contributed to
the evaluation and validation of suitable rehabilitation
measures, e.g., for helping stroke patients. Based on
these findings, sport psychologists were able to improve
both the mental training of stroke patients for the planning
of previously learned as well as newly learned
movement-related tasks (e.g., grasping). However, these
research findings are not only valuable for the rehabilitation
of stroke patients, but in addition might also be
of high importance for athletes (e.g., in cases of injuryrelated
training pauses).
In most recent years, several attempts have been put
forward to develop mobile EEG systems in order to
investigate movement-related tasks in more natural
environments outside the ‘classical’ laboratory setting
(e.g., using a mobile EEG system during walking) (for
an introduction into this topic, the reader is referred to
Aspinall, Mavros, Coyne, & Roe, 2015; Debener,
Minow, Emkes, Gandras, & de Vos, 2012; Park,
Fairweather, & Donaldson, 2015). These approaches
seem to be very fruitful and promising for developing a
better understanding of the underlying spatio-temporal
dynamics during complex movement-related tasks in a
natural environment.
