Abstract
In neuronal population signals, including the electroencephalogram (EEG) and electrocorticogram (ECoG), the low-frequency component (LFC) is particularly informative about motor behavior and can be used for decoding movement parameters for brain-machine interface (BMI) applications. An idea previously expressed, but as of yet not quantitatively tested, is that it is the LFC phase that is the main source of decodable information. To test this issue, we analyzed human ECoG recorded during a game-like, one-dimensional, continuous motor task with a novel decoding method suitable for unfolding magnitude and phase explicitly into a complex-valued, time-frequency signal representation, enabling quantification of the decodable information within the temporal, spatial and frequency domains and allowing disambiguation of the phase contribution from that of the spectral magnitude. The decoding accuracy based only on phase information was substantially (at least 2 fold) and significantly higher than that based only on magnitudes for position, velocity and acceleration. The frequency profile of movement-related information in the ECoG data matched well with the frequency profile expected when assuming a close time-domain correlate of movement velocity in the ECoG, e.g., a (noisy) “copy” of hand velocity. No such match was observed with the frequency profiles expected when assuming a copy of either hand position or acceleration. There was also no indication of additional magnitude-based mechanisms encoding movement information in the LFC range. Thus, our study contributes to elucidating the nature of the informative LFC of motor cortical population activity and may hence contribute to improve decoding strategies and BMI performance.
Highlights
Brain machine interfaces (BMIs) are devices that have the potential to restore movement ability in severely paralyzed patients by using neuronal signals to control external effectors
We addressed the following questions regarding the role of phase information in decoding movement position, velocity, and acceleration from the low-frequency component (LFC): Is the accuracy of decoding based on phase substantially higher than that based on the magnitude of the spectral components? Can the contribution of LFC phase to continuous movement decoding be understood assuming a simple model, in which the LFC contains a time domain correlate of the trajectory that “copies” the movement? To address these issues we engaged human subjects with ECoG implantations in a game-like, one-dimensional, continuous motor task and analyzed hand movement-related ECoG signals recorded from sensorimotor cortical areas in the frontal and parietal cortex with a novel decoding algorithm
Using the decoding approach based on a time-frequency representation of the ECoG as described in section Fourier Descriptors of Short-Time Fourier transform, we first addressed the question whether LFC phase is the major source of movementrelated decodable information
Summary
Brain machine interfaces (BMIs) are devices that have the potential to restore movement ability in severely paralyzed patients by using neuronal signals to control external effectors. A prominent BMI approach is to directly translate neuronal movement-related activity corresponding to (intended, attempted, or executed) movements into those of an external actuator. This direct motor decoding approach has been successfully used for closed-loop motor control with multiple single-unit activity (SUA) in monkeys (Carmena et al, 2003) and humans (Hochberg et al, 2006; Collinger et al, 2013) and with electrocorticography (ECoG) (Yanagisawa et al, 2011; Milekovic et al, 2012), utilizing a low frequency component (LFC) of measured neuronal population signals (Milekovic et al, 2012). The functional role of phase in cortical motor control has received much less attention (but see Miller et al, 2012) than in the domain of sensory processing
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