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Decoding intention at sensorimotor timescales.

Salvaris M, Haggard P - PLoS ONE (2014)

Bottom Line: Several Brain Computer Interface applications have used motor imagery of repetitive actions to achieve this goal.These systems are relatively successful, but only if the intention is sustained over a period of several seconds; much longer than the timescales identified in psychophysiological studies for normal preparation for voluntary action.The implications for volition are considered.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cognitive Neuroscience, University College London, London, United Kingdom.

ABSTRACT
The ability to decode an individual's intentions in real time has long been a 'holy grail' of research on human volition. For example, a reliable method could be used to improve scientific study of voluntary action by allowing external probe stimuli to be delivered at different moments during development of intention and action. Several Brain Computer Interface applications have used motor imagery of repetitive actions to achieve this goal. These systems are relatively successful, but only if the intention is sustained over a period of several seconds; much longer than the timescales identified in psychophysiological studies for normal preparation for voluntary action. We have used a combination of sensorimotor rhythms and motor imagery training to decode intentions in a single-trial cued-response paradigm similar to those used in human and non-human primate motor control research. Decoding accuracy of over 0.83 was achieved with twelve participants. With this approach, we could decode intentions to move the left or right hand at sub-second timescales, both for instructed choices instructed by an external stimulus and for free choices generated intentionally by the participant. The implications for volition are considered.

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Comparison of decoding using EEG and EMG during imaginary movement task for the last three participants.The black line shows the decoding accuracy achieved when condition labels were randomly reshuffled.
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pone-0085100-g005: Comparison of decoding using EEG and EMG during imaginary movement task for the last three participants.The black line shows the decoding accuracy achieved when condition labels were randomly reshuffled.

Mentions: For three participants we also recorded EMG during the real and imaginary movement tasks. Figure 5 compares the decoding of movement intention during movement imagery in these participants, based on both EEG and EMG. Figure 6 shows decoding during the foreperiod between precue and Go signal in the precueing task, for the same participants. In both cases, classification achieved using EEG far exceeds that of using EMG. In figure 6, decoding using EEG rises earlier than the decoding using EMG and that at the onset of the Go signal decoding using EMG is very poor. With this we conclude that the model depends on the neuromodulation of the sensorimotor EEG rhythms associated with motor preparation, rather than motor execution.


Decoding intention at sensorimotor timescales.

Salvaris M, Haggard P - PLoS ONE (2014)

Comparison of decoding using EEG and EMG during imaginary movement task for the last three participants.The black line shows the decoding accuracy achieved when condition labels were randomly reshuffled.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3921113&req=5

pone-0085100-g005: Comparison of decoding using EEG and EMG during imaginary movement task for the last three participants.The black line shows the decoding accuracy achieved when condition labels were randomly reshuffled.
Mentions: For three participants we also recorded EMG during the real and imaginary movement tasks. Figure 5 compares the decoding of movement intention during movement imagery in these participants, based on both EEG and EMG. Figure 6 shows decoding during the foreperiod between precue and Go signal in the precueing task, for the same participants. In both cases, classification achieved using EEG far exceeds that of using EMG. In figure 6, decoding using EEG rises earlier than the decoding using EMG and that at the onset of the Go signal decoding using EMG is very poor. With this we conclude that the model depends on the neuromodulation of the sensorimotor EEG rhythms associated with motor preparation, rather than motor execution.

Bottom Line: Several Brain Computer Interface applications have used motor imagery of repetitive actions to achieve this goal.These systems are relatively successful, but only if the intention is sustained over a period of several seconds; much longer than the timescales identified in psychophysiological studies for normal preparation for voluntary action.The implications for volition are considered.

View Article: PubMed Central - PubMed

Affiliation: Institute of Cognitive Neuroscience, University College London, London, United Kingdom.

ABSTRACT
The ability to decode an individual's intentions in real time has long been a 'holy grail' of research on human volition. For example, a reliable method could be used to improve scientific study of voluntary action by allowing external probe stimuli to be delivered at different moments during development of intention and action. Several Brain Computer Interface applications have used motor imagery of repetitive actions to achieve this goal. These systems are relatively successful, but only if the intention is sustained over a period of several seconds; much longer than the timescales identified in psychophysiological studies for normal preparation for voluntary action. We have used a combination of sensorimotor rhythms and motor imagery training to decode intentions in a single-trial cued-response paradigm similar to those used in human and non-human primate motor control research. Decoding accuracy of over 0.83 was achieved with twelve participants. With this approach, we could decode intentions to move the left or right hand at sub-second timescales, both for instructed choices instructed by an external stimulus and for free choices generated intentionally by the participant. The implications for volition are considered.

Show MeSH
Related in: MedlinePlus