Mobile EEG and its potential to promote the theory and application of imagery-based motor rehabilitation

https://doi.org/10.1016/j.ijpsycho.2013.10.004Get rights and content

Highlights

  • Overview of the challenges and opportunities associated with mobile EEG

  • Summary of presently available systems

  • Discussion on EEG-based neurofeedback training a tool for neurorehabilitation

Abstract

Studying the brain in its natural state remains a major challenge for neuroscience. Solving this challenge would not only enable the refinement of cognitive theory, but also provide a better understanding of cognitive function in the type of complex and unpredictable situations that constitute daily life, and which are often disturbed in clinical populations. With mobile EEG, researchers now have access to a tool that can help address these issues. In this paper we present an overview of technical advancements in mobile EEG systems and associated analysis tools, and explore the benefits of this new technology. Using the example of motor imagery (MI) we will examine the translational potential of MI-based neurofeedback training for neurological rehabilitation and applied research.

Introduction

Understanding human behavior is one of the big challenges for humankind. In the last 20 years neuroscience has emerged as a key area of research and there is a recognition that understanding brain function in general, and brain–behavior relationships in particular, is vital to advance solutions for major public health issues such as mental health, dementia, obesity, or impairments remaining after suffering from stroke or traumatic brain injury. Immense improvements in the availability of neuroimaging methodologies together with high-profile initiatives, such as the decade of the brain, have brought a wealth of new insights into brain function and are already leading to new forms of treatment. However, a major challenge still is to understand the brain in its natural state. This would not only enable the refinement of cognitive theory but also to get a true understanding of cognitive function in the type of complex and unpredictable situations that constitute daily life. For example, how does our brain enable us to function in a highly complex situation such as navigating through a grocery shop while selecting products from a vast range of goods? How are these processes influenced by internal physiological states such as hunger or low mood? How does our brain help us to prioritize some actions and inhibit others? What are the brain correlates of impaired, challenging or maladaptive behavior expressed in typical life situations? These are of course hugely demanding questions, which cannot be easily answered. Yet with the mobile electroencephalogram (EEG), researchers now have a tool to explore these questions. In contrast to all other techniques presently available, mobile EEG truly allows us to take neuroscience into the field and study everyday brain function.

In this paper we will examine the benefits of mobile EEG and the challenges it has to meet to provide a fully fledged research tool in cognitive and clinical neuroscience, as well as a tool for clinical interventions and BCIs. We will exemplarily show how the technical challenges involved in mobile EEG have been addressed by recent advancements in the field. The focus will then be shifted to yet another opportunity associated with mobile EEG, which is the support of brain computer interface (BCI) based treatment delivery in the home environment. This will be done through the example of motor imagery (MI).

Section snippets

Why do we need mobile EEG?

EEG studies are typically conducted in a laboratory, and many arguments can be found in favor of this practice. For example, the environment is controlled and recording conditions are kept consistent across subjects. Laboratories are often electrically shielded and noise attenuated to reduce factors that may negatively affect data quality such as line noise. Moreover, the laboratory set-up allows for full control of the amount and type of information a participant is exposed to at any time of

Mobile EEG systems and current advancements

Mobile EEG systems have been available for a number of years now. But which requirements shall a true mobile EEG system fulfill? Obviously mobile EEG systems should allow natural body movements, which imply the use of non-stationary EEG systems that are ideally fully head-mounted. Systems therefore should be small, lightweight and transmit data wirelessly. Moreover a head-mounted cap-amplifier design ensures minimal isolated movements of individual electrodes, cables or the amplifier, which

Bridging the gap

As outlined above, a range of mobile EEG systems are available on the market. However, in our opinion none of them meets all the qualities required for neuroscience experiments in the field in an optimal way. For this reason a refined mobile and wireless system, the Oldenburg EEG system, was recently developed by Debener and colleagues, which is based on the Emotiv EPOC described above. The aim of the development was to create a system allowing flexible yet accurate electrode positioning and

Theoretical background: the neurosimulation theory of action

Jeannerod's theory of motor cognition (Jeannerod, 2001) suggests the neural simulation of actions as a key mechanism for motor control. According to this theory, action representation comprises overt as well as covert stages. The overt stage refers to the actual execution of movements and as such represents the stage most commonly associated with actions. However, motor cognition also comprises a number of covert stages which refer to e.g. intended movements, imagination of movements, as well

Conclusion

Mobile EEG is an exciting new technology with excellent potential for translational and applied research. However, to fulfill this potential EEG systems must not only be small and suitable for everyday settings, but also need to produce the data quality required for single trial analysis. One particularly exciting application of mobile EEG is neurofeedback training for MI and/or motor-imagery based BCIs. Initial evidence suggests that neurofeedback can increase MI effectiveness and helps

Acknowledgements

CK is supported by grant KR 3433/2-1, German Research Foundation (DFG).

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