Objective: High-frequency band (HFB) activity, measured using implanted sensors over the cortex, is increasingly considered as a feature for the study of brain function and the design of neural-implants, such as Brain-Computer Interfaces (BCIs). One common way of extracting these power signals is using a wavelet dictionary, which involves the selection of different temporal sampling and temporal smoothing parameters, such that the resulting HFB signal best represents the temporal features of the neuronal event of interest. Typically, the use of neuro-electrical signals for closed-loop BCI control requires a certain level of signal downsampling and smoothing in order to remove uncorrelated noise, optimize performance and provide fast feedback. However, a fixed setting of the sampling and smoothing parameters may lead to a suboptimal representation of the underlying neural responses and poor BCI control. This problem can be resolved with a systematic assessment of parameter settings.
Approach: With classification of HFB power responses as performance measure, different combinations of temporal sampling and temporal smoothing values were applied to data from sensory and motor tasks recorded with high-density and standard clinical electrocorticography (ECoG) grids in 12 epilepsy patients.
Main results: The results suggest that HFB ECoG responses are best performed with high sampling and subsequent smoothing. For the paradigms used in this study, optimal temporal sampling ranged from 29 Hz to 50 Hz. Regarding optimal smoothing, values were similar between tasks (0.1-0.9 s), except for executed complex hand gestures, for which two optimal possible smoothing windows were found (0.4-0.6 s and 0.9-2.7 s).
Significance: The range of optimal values indicates that parameter optimization depends on the functional paradigm and may be subject-specific. Our results advocate a methodical assessment of parameter settings for optimal decodability of ECoG signals.
Keywords: Brain-Computer Interface; Electrocorticography; High-frequency band; Sensorimotor cortex; Temporal sampling; Temporal smoothing; Wavelets.