Abstract
Brain computer interfaces (BCIs) provide a direct communication channel by using brain signals, enabling patients with motor impairments to interact with external devices. Motion intention detection is useful for intuitive movement-based BCI as movement is the fundamental mode of interaction with the environment. The aim of this paper is to investigate the temporal dynamics of brain processes using electroencephalography (EEG) to explore novel neural correlates of motion intention. We investigate the changes in temporal dependencies of the EEG by characterising the decay of autocorrelation during asynchronous voluntary finger tapping movement. The evolution of the autocorrelation function is characterised by its relaxation time, which is used as a robust marker for motion intention. We observed that there was reorganisation of temporal dependencies in EEG during motion intention. The autocorrelation decayed slower during movement intention and faster during the resting state. There was an increase in temporal dependence during movement intention. The relaxation time of the autocorrelation function showed significant (p < 0.05) discrimination between movement and resting state with the mean sensitivity of 78.37 ± 8.83%. The relaxation time provides movement related information that is complementary to the well-known event-related desynchronisation (ERD) by characterising the broad band EEG dynamics which is frequency independent in contrast to ERD. It can also detect motion intention on average 0.51s before the actual movement onset. We have thoroughly compared autocorrelation relaxation time features with ERD in four frequency bands. The relaxation time may therefore, complement the well-known features used in motion-based BCI leading to more robust and intuitive BCI solutions. The results obtained suggest that changes in autocorrelation decay may involve reorganisation of temporal dependencies of brain activity over longer duration during motion intention. This opens the possibilities of investigating further the temporal dynamics of fundamental neural processes underpinning motion intention.
Highlights
Brain computer interfaces (BCIs) that use electroencephalography (EEG) are being increasingly used in research due to their non-invasive nature and their potential in therapeutic applications such as motor rehabilitation [1,2,3,4]
The aim of this paper is to identify novel neural correlates of movement based on changes in the dynamics of EEG by characterising its temporal dependencies, such that they provide movement intention related information that is complementary to event-related desynchronisation (ERD) and motor-related cortical potential (MRCP)
We have observed that relaxation time provides information about movement intention that is distinct from ERD
Summary
Brain computer interfaces (BCIs) that use electroencephalography (EEG) are being increasingly used in research due to their non-invasive nature and their potential in therapeutic applications such as motor rehabilitation [1,2,3,4]. SSVEP and ERP employ visual and attention processes, and they always require an external stimulus to evoke a detectable response. Neural correlates of movement enable intuitive control of BCIs by producing movement intention at will without requiring any external stimuli [9, 10]. Changes in power at specific EEG frequency bands are used for detecting movement intention. This disregards the movement related information present in the rest of the EEG spectrum and in the time domain, as the EEG signal is essentially non-stationary. We explore movement related information which is not reflected in the band power changes, by studying the evolution of temporal dependencies in the EEG during motion intention
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