Representation and decoding of bilateral arm motor imagery using unilateral cerebral LFP signals

In the field of upper limb brain computer interfaces (BCIs), the research focusing on bilateral decoding mostly based on the neural signals from two cerebral hemispheres. In addition, most studies used spikes for decoding. Here we examined the representation and decoding of different laterality and regions arm motor imagery in unilateral motor cortex based on local field potentials (LFPs).

The LFP signals were recorded from a 96-channel Utah microelectrode array implanted in the left primary motor cortex of a paralyzed participant. There were 7 kinds of tasks: rest, left, right and bilateral elbow and wrist flexion. We performed time-frequency analysis on the LFP signals and analyzed the representation and decoding of different tasks using the power and energy of different frequency bands.

The frequency range of <8 Hz and >38 Hz showed power enhancement, whereas 8-38 Hz showed power suppression in spectrograms while performing motor imagery. There were significant differences in average energy between tasks. What's more, the movement region and laterality were represented in two dimensions by demixed principal component analysis. The 135-300 Hz band signal had the highest decoding accuracy among all frequency bands and the contralateral and bilateral signals had more similar single-channel power activation patterns and larger signal correlation than contralateral and ipsilateral signals, bilateral and ipsilateral signals.

The results showed that unilateral LFP signals had different representations for bilateral motor imagery on the average energy of the full array and single-channel power levels, and different tasks could be decoded. These proved the feasibility of multilateral BCI based on the unilateral LFP signal to broaden the application of BCI technology.

https://www.chictr.org.cn/showproj.aspx?proj=130829, identifier ChiCTR2100050705.