title: Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution.
abstract: Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode movement intent from delta-band (0.1-4 Hz) EEG recorded immediately before movement execution in healthy volunteers. We used data from epochs starting 1.5 s before movement onset to classify future movements into one of three classes: stand-up, sit-down, or quiet. We assessed classification accuracy in both externally triggered and self-paced paradigms. Movement onset was determined from electromyography (EMG) recordings synchronized with EEG signals. We employed an artifact subspace reconstruction (ASR) algorithm to eliminate high amplitude noise before building our time-embedded EEG features. We applied local Fisher's discriminant analysis to reduce the dimensionality of our spatio-temporal features and subsequently used a Gaussian mixture model classifier for our three class problem. Our results demonstrate significantly better than chance classification accuracy (chance level = 33.3%) for the self-initiated (78.0 ± 2.6%) and triggered (74.7 ± 5.7%) paradigms. Surprisingly, we found no significant difference in classification accuracy between the self-paced and cued paradigms when using the full set of non-peripheral electrodes. However, accuracy was significantly increased for self-paced movements when only electrodes over the primary motor area were used. Overall, this study demonstrates that delta-band EEG recorded immediately before movement carries discriminative information regarding movement type. Our results suggest that EEG-based classifiers could improve lower-limb neuroprostheses and neurorehabilitation techniques by providing earlier detection of movement intent, which could be used in robot-assisted strategies for motor training and recovery of function.
authors.affiliation: Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, National Institutes of Health Bethesda, MD, USA ; Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Bulea
authors.affiliation: Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Prasad
authors.affiliation: Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Atilla
authors.initials: A
authors.lastname: Kilicarslan
authors.affiliation: Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA.
authors.forename: Jose L
authors.initials: JL
authors.lastname: Contreras-Vidal
keywords: EEG; brain-machine interface; classification; electroencephalography; lower extremity; mobile neuroimaging; movement-related cortical potentials