The spiking of neuronal populations in motor cortex provides accurate information about movement parameters. Here we show that hand movement target and velocity can be inferred from multiple local field potentials (LFPs) in single trials approximately as efficiently as from multiple single-unit activity (SUA) recorded from the same electrodes. Our results indicate that LFPs can be used as an additional signal for decoding brain activity, particularly for new neuroprosthetic applications.

Recent studies showed that the low-frequency component of local field potentials (LFPs) in monkey motor cortex carries information about parameters of voluntary arm movements. Here, we studied how different signal components of the LFP in the time and frequency domains are modulated during center-out arm movements. Analysis of LFPs in the time domain showed that the amplitude of a slow complex waveform beginning shortly before the onset of arm movement is modulated with the direction of the movement. Examining LFPs in the frequency domain, we found that direction-dependent modulations occur in three frequency ranges, which typically increased their amplitudes before and during movement execution: Յ4, 6 -13, and 63-200 Hz. Cosine-like tuning was prominent in all signal components analyzed. In contrast, activity in a frequency band Ϸ30 Hz was not modulated with the direction of movement and typically decreased its amplitude during the task. This suggests that high-frequency oscillations have to be divided into at least two functionally different regimes: one Ϸ30 Hz and one Ͼ60 Hz. Furthermore, using multiple LFPs, we could show that LFP amplitude spectra can be used to decode movement direction, with the best performance achieved by the combination of different frequency ranges. These results suggest that using the different frequency components in the LFP is useful in improving inference of movement parameters from local field potentials.

No abstract

Previous studies have shown that activity of neuronal populations in the primary motor cortex (MI), processed by the population vector method, faithfully predicts upcoming movements. In our previous studies we found that single neurons responded differently during movements of one arm vs. combined movements of the two arms. It was, therefore, not clear whether the population vector approach could produce reliable movement predictions also for bimanual movements. This study tests this question by comparing the predictive quality of population vectors for unimanual and bimanual arm movements. We designed a bimanual motor task that requires coordinated movements of the two arms, in which each arm may move in eight directions, and recorded single unit activity in the MI of two rhesus (Macaca mulatta) monkeys during the performance of unimanual and bimanual arm movements. We analysed the activity of 212 MI cells from both hemispheres and found that, despite bimanual related activity, the directional tuning and preferred directions of most cells were preserved in unimanual and bimanual movements. We demonstrate that population vectors, constructed from the activity of MI cells, predict accurately the direction of movement both for unimanual and for bimanual movements even when the two arms move simultaneously in different directions.