Motor Priming as a Brain-Computer Interface
This paper reports on a project to overcome a difficulty associated with motor imagery (MI) in a brain–computer interface (BCI), in which user training relies on discovering how to best carry out the MI given only open-ended instructions. To address this challenge we investigate the use of a motor priming (MP), a similar mental task but one linked to a tangible behavioural goal. To investigate the efficacy of this approach in creating the changes in brain activity necessary to drive a BCI, an experiment is carried out in which the user is required to prepare and execute predefined movements. Significant lateralisations of alpha activity are discussed and significant classification accuracies of movement preparation versus no preparation are also reported; indicating that this method is promising alternative to motor imagery in driving a BCI.
KeywordsBrain-computer interface (BCI) Motor priming EEG Neurophysiological information processing and classification
A brain–computer interface (BCI) offers a unique mode of communication between a user and an environment by interpreting and acting upon signals arising directly from the brain . In doing so, BCI bypasses the body’s usual output pathway which requires motor signals to be sent from the brain, through the spinal cord and peripheral nerves to reach, and contract skeletal muscles . Through a number of conditions, most notably spinal cord injury and ALS, this pathway can be interrupted and in extreme cases, the body can lose control of all of its skeletal musculature. In those cases, BCI is the only means available for a patient to interact with his/her environment. As such, one critical application for BCI is to control assistive devices such as wheelchairs and neuroprosthesis.
1.1 Motor Imagery
Current BCI’s aimed at neuroprosthesis control are usually based around a mental task known as motor imagery . To perform motor imagery, a subject is instructed to imagine moving a particular part of their body such as opening and closing one of their hands. The act of imagining a movement is thought to engage the same neural circuits as those that would be involved in planning an actual movement  and has been shown to cause patterns in the amplitude of \(8-12\) Hz brainwave EEG activity. The nature of these patterns is consistent with a topographical map of the body found in the brain’s sensorimotor cortex known as the homunculus. In general, motor imagery relating to a particular body part will cause a suppression of \(\mu -\)rhythms in the corresponding region of the homunculus. To harness this phenomenon and create a BCI, machine learning algorithms are used to classify these brainwave patterns and to output an estimate of which body part’s movement, if any, was being imagined based on a user’s on–going EEG readings. The result of this classification can then be used to create a command which may operate a computer or some form of assistive device . As a mental task, motor imagery (MI) is well suited to brain computer interface for a number of reasons. Most importantly, it can be used to communicate different commands depending on the part of the body which the subject chooses to imagine moving. Secondly, MI represents a mental state that the brain can enter into entirely of it’s own volition and without the need for external stimulus. This means that subjects can potentially drive the BCI at their own pace . Finally, MI has been shown to be possible even when the subject has no control over the body part with which the movement is being imagined meaning that it can be utilised even by subjects with severe paralysis . To date, MI–based BCI have been successfully applied to a number of applications including navigating through virtual worlds  flying quadrocopters  and even controlling the exoskeleton that was used to deliver the opening kick at the 2014 FIFA World Cup.
However, despite it’s potential, this type of BCI has yet to reach the level of robustness and reliability necessary to be applied practically outside of laboratory conditions . One widely recognized challenge associated with this type of BCI is training users in effectively utilising MI to create strong and reliable patterns in their brainwave activity. Motor Imagery is an abstract mental visualisation task without a tangible behavioural goal and it’s therefore difficult to give explicit instructions on how it should be properly carried out. The instructions that are given are quite open to interpretation and usually in the form of “imagine opening and closing your left hand”. This allows for the possibility that different subjects may interpret these instructions differently and arrive at varying strategies on how to carry out the MI task. If these differing strategies bring with them differing levels of efficacy then this might account for the reported variability in subject performance of MI .
1.2 Motor Priming
Despite these parallels with MI, MP has received little, if any attention from the BCI community. Never the less, motor priming has several key characteristics which might make it an appealing alternative to motor imagery in voluntarily generating the brain activity patterns necessary to control a BCI. Most importantly, motor priming is tied to a clear behavioural goal, that is, to execute a prepared movement. Therefore in order to explain to a subject how to carry out MP, it’s only necessary to instruct them to make a predefined movement as quickly as possible. It can then be reasoned that the subject will instinctively place themselves in the required mental state in order to minimise their reaction time and in so doing create the necessary changes in brain activity to drive the BCI. This bypasses the need to give subjects instructions on how to carry out a mental task which may be difficult to convey and prone to misinterpretation. Furthermore, since the execution of a covertly prepared movement is common in daily activities, it’s reasoned that subjects might be able to draw on instinct or past experience to engage in motor priming, and might therefore be capable of carrying it out with less training than motor imagery. Finally, since motor priming is linked to a subject’s reaction time in carrying out a movement, that reaction time can be used as an unambiguous metric for subject performance. This allows for clear feedback during user training as well as a means of identifying how engaged the user is in carrying out the mental task.
2 Materials and Methods
To investigate whether different motor priming tasks could be classified on a single trial basis and thus serve as an EEG control strategy, an experiment was carried out in which seven subjects were asked to covertly prepare, and then subsequently execute a movement as quickly as possible upon the presentation of a “go” signal.
Preparatory cues and associated movements
Press right button with right hand
Press left button with left hand
Press foot pedal with right foot
2.1 Data Collection
Behavioural data in the form of reaction times and EEG activity were collected throughout each experimental session. EEG activity was measured using a g.HIamp amplifier by gtec Medical Instruments GmbH, Austria, which recorded activity from 62 scalp locations conforming to a 10 / 10 electrode montage. EEG measurement was carried out at 512 Hz using a right ear reference and active electrodes.
2.2 Data Analysis
The average reaction time across all subjects was \(416 \pm 112\) ms while the accuracy was \(97.9 \pm 2.2\,\%\). Classification accuracy between “go” and “no–go” trials exhibited a high degree of variability depending on the latency at which the classifier was trained. All subjects temporarily reached periods of statistically significant classification however only the subjects \(\#1, \#2\) and \(\#5\) showed sustained periods high classification (\(>100\) ms). By inspecting the topography of weights learned by classifier during these periods it was found that these classifications were made primarily on the basis of an alpha desynchronisations in the occipital region. Figure 2a shows the classification accuracy left hand movement vs. no movement by the subject \(\#1\) with respect to time. Figure 2b shows the importance map of the weights learned by the classifier during the periods of statistically significant classification.
In the AUC analysis, subjects \(\#1\), 3&7 showed statistically significant lateralisations of alpha activity in response to left vs. right hand movement priming. These manifested as an increase in the AUC score almost immediately after the onset of the cue and reaching statistical significance (\(p < 0.05\)) at roughly t = 500 ms. This effect was observed for the duration of the trial and peaked in significance over the \(C3-C4\) and \(CP1-CP2\) electrode pairs.
The behavioural results showed similar reaction times and high task accuracy across all subjects suggesting that all seven participants were able to carry out the experiment with similar, satisfactory levels of proficiency. The AUC analysis revealed strong lateralization in alpha band activity over the sensorimotor cortex in three subjects in response to left or right hand movement preparation. The AUC scores reached strong statistical significance \((p < 0.01)\) within 500 ms after the onset of the cue; suggesting that this effect was indeed a result of movement preparation. In the context of BCI control, this results suggests that a user may issue either a left or right command on the basis of alpha band lateralisation over \(C3-C4 / CP1-CP2\) electrode pairs through MP in the same way as is possible through the use of MI.
Additionally, the “go” vs “no–go” trials, showed spurious but statistically significant discrimination based on desynchronisations in occipital alpha activity. These desynchronisations did not show any consistent spatial characteristics between the different movement conditions therefore it’s likely that this distinction is a result of a heightened state of attention during the movement trials rather than any motor specific activity. Nevertheless, this result suggests that it might be possible to distinguish intentional left and right BCI commands from ambient lateralisation in rolandic activity. The question remains as to why only three of the seven subjects exhibited significantly elevated AUC scores. The observed reaction times suggested all subjects were actively engaged with the mental task, however a more fine grained analysis may reveal underlying differences to suggest reduced concentration in the non–lateralising subjects. Alternatively, similar research on both motor priming  as well as visual spatial attention  has found that different subjects show varying degrees of lateralisation which might account for this inter-subject variability. If this is the case then a more general feature extraction method such as a common spatial pattern might yield better separation of left vs. right hand trials.
This research demonstrates that by carrying out a simple movement preparation task given only basic instructions, it’s possible for subjects to achieve significant changes in alpha band EEG activity that may be used to drive a brain computer interface. These changes in brain activity have similar characteristics to with those associated with motor imagery in terms of a contralateral decrease and an ipsilateral increase in alpha power relative to the hand being prepared for movement. Unlike MI, MP is linked to a tangible behavioural goal and should therefore be easy and intuitive for new users to acquire. The is hypothesis is supported by the fact that the three subjects who achieved significant lateralisation did so in one experimental session despite having no prior experience with MP tasks.
Further investigation into the reaction time characteristics of the non–laterali-sing subjects along with more general EEG feature selection is expected to account for the observed variability in subject performance.
- 1.Deiber, M.P., Sallard, E., Ludwig, C., Ghezzi, C., Barral, J., Ibañez, V.: EEG alpha activity reflects motor preparation rather than the mode of action selection. Front. Integrat. Neurosci. 6(59) (2012)Google Scholar
- 4.Oostenveld, R., Fries, P., Maris, E., Schoffelen, J.M.: Fieldtrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data. Comput. Intell. Neurosci. 2011, Article no. 1 (2010)Google Scholar