The role of ECoG magnitude and phase in decoding position, velocity, and acceleration during continuous motor behavior

Jiri Hammer^1*, Jörg Fischer², Johanna Ruescher^3,4, Andreas Schulze-Bonhage^1,4, Ad Aertsen^1,3 and Tonio Ball^1,4

• ¹Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany
• ²CorTec GmbH, Freiburg, Germany
• ³Neurobiology and Biophysics, Faculty of Biology, University of Freiburg, Freiburg, Germany
• ⁴Epilepsy Center, University Medical Center Freiburg, Freiburg, Germany

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.