In Journal of neural engineering ; h5-index 52.0
OBJECTIVE : Previous electrophysiological research has characterized canonical oscillatory patterns associated with movement mostly from recordings of primary sensorimotor cortex. Less work has attempted to decode movement based on electrophysiological recordings from a broader array of brain areas such as those sampled by stereoelectroencephalography (sEEG), especially in humans. We aimed to identify and characterize different movement-related oscillations across a relatively broad sampling of brain areas in humans and if they extended beyond brain areas previously associated with movement.
APPROACH : We used a linear support vector machine (SVM) to decode time-frequency spectrograms time-locked to movement, and we validated our results with cluster permutation testing and common spatial pattern (CSP) decoding.
MAIN RESULTS : We were able to accurately classify sEEG spectrograms during a keypress movement task versus the inter-trial interval. Specifically, we found these previously-described patterns: beta (13 - 30 Hz) desynchronization, beta synchronization (rebound), pre-movement alpha (8 - 15 Hz) modulation, a post-movement broadband gamma (60 - 90 Hz) increase and an event-related potential. These oscillatory patterns were newly observed in a wide range of brain areas accessible with sEEG that are not accessible with other electrophysiology recording methods. For example, the presence of beta desynchronization in the frontal lobe was more widespread than previously described, extending outside primary and secondary motor cortices.
SIGNIFICANCE : Our classification revealed prominent time-frequency patterns which were also observed in previous studies that used non-invasive electroencephalography (EEG) and electrocorticography (ECoG), but here we identified these patterns in brain regions that had not yet been associated with movement. This provides new evidence for the anatomical extent of the system of putative motor networks that exhibit each of these oscillatory patterns.
Rockhill Alexander P, Mantovani Alessandra, Stedelin Brittany, Nerison Caleb S, Raslan Ahmed M, Swann Nicole C
2022-Dec-22
Stereoelectroencephalography, beta, machine learning, movement, oscillations, sEEG, support vector machine
