Abstract
Neuroimaging studies provide evidence of cortical involvement immediately before and during gait and during gait-related behaviors such as stepping in place or motor imagery of gait. Here we attempt to perform single-trial classification of gait intent from another movement plan (point intent) or from standing in place. Subjects walked naturally from a starting position to a designated ending position, pointed at a designated position from the starting position, or remained standing at the starting position. The 700 ms of recorded electroencephalography (EEG) before movement onset was used for single-trial classification of trials based on action type and direction (left walk, forward walk, right walk, left point, right point, and stand) as well as action type regardless of direction (stand, walk, point). Classification using regularized LDA was performed on a principal components analysis (PCA) reduced feature space composed of coefficients from levels 1 to 9 of a discrete wavelet decomposition using the Daubechies 4 wavelet. We achieved significant classification for all conditions, with errors as low as 17% when averaged across nine subjects. LDA and PCA highly weighted frequency ranges that included movement related potentials (MRPs), with smaller contributions from frequency ranges that included mu and beta idle motor rhythms. Additionally, error patterns suggested a spatial structure to the EEG signal. Future applications of the cortical gait intent signal may include an additional dimension of control for prosthetics, preemptive corrective feedback for gait disturbances, or human computer interfaces (HCI).
Highlights
The detection of locomotive intent has potential as a control signal in brain-computer interfaces, for mitigating complications in movement disorders, or for use in environments with human computer interfaces (HCI)
This study demonstrated that single trial EEG data is (1) classifiable for walk intent before the onset of natural movement, (2) classifiable between two motor plans that activate overlapping muscles, and (3) classifiable for an action at different target spatial locations
The largest contributors to successful classification were low frequencies (0–4 Hz) and channels located over areas involved in motor planning or motor production
Summary
The detection of locomotive intent has potential as a control signal in brain-computer interfaces, for mitigating complications in movement disorders, or for use in environments with human computer interfaces (HCI). Detection of this signal is valuable for spinal cord injury patients and other users of lower limb prosthetics or artificial exoskeletons. It may prove useful for alleviating abnormal states that prevent proper gait coordination such as during freezing of gait in Parkinson’s disease by providing preemptive visual cues that help prevent freezing episodes (Hanakawa et al, 1999; Jiang and Norman, 2006; Nieuwboer, 2008). We explore the possibility of electroencephalography (EEG) to differentiate the intent to produce a locomotive movement from another movement as well as the spatial direction of the movement
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