Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters

Author summary Invasive electrophysiological recordings of human brain activity offer the unique ability to measure multiple, simultaneously active brain rhythms. Analyzing brain rhythms is complex due to the fact that different oscillations often overlap in space and time. Here we explore human resting state invasive electrophysiological recordings by using spatial filters, which combine information from all available recording electrodes to specifically extract oscillations with high signal to noise ratio. Using this technique, we explore variability in oscillation presence across subjects, the spatial spread and waveform shape of oscillations. We find that participants differ a lot in presence of oscillations, even when the recording electrodes have similar placement. We find that oscillations exhibit spatial spread exceeding the distance between electrodes and that the waveform shape of oscillations in different brain regions can be highly deviating from a sine wave. In invasive electrophysiological recordings, a variety of neural oscillations can be detected across the cortex, with overlap in space and time. This overlap complicates measurement of neural oscillations using standard referencing schemes, like common average or bipolar referencing. Here, we illustrate the effects of spatial mixing on measuring neural oscillations in invasive electrophysiological recordings and demonstrate the benefits of using data-driven referencing schemes in order to improve measurement of neural oscillations. We discuss referencing as the application of a spatial filter. Spatio-spectral decomposition is used to estimate data-driven spatial filters, a computationally fast method which specifically enhances signal-to-noise ratio for oscillations in a frequency band of interest. We show that application of these data-driven spatial filters has benefits for data exploration, investigation of temporal dynamics and assessment of peak frequencies of neural oscillations. We demonstrate multiple use cases, exploring between-participant variability in presence of oscillations, spatial spread and waveform shape of different rhythms as well as narrowband noise removal with the aid of spatial filters. We find high between-participant variability in the presence of neural oscillations, a large variation in spatial spread of individual rhythms and many non-sinusoidal rhythms across the cortex. Improved measurement of cortical rhythms will yield better conditions for establishing links between cortical activity and behavior, as well as bridging scales between the invasive intracranial measurements and noninvasive macroscale scalp measurements.

Automated summary

Invasive electrophysiological recordings of human brain activity can capture a variety of neural oscillations with overlap in space and time. By using spatial filters, researchers can explore variability in oscillation presence across subjects, as well as spatial spread and waveform shapes of different rhythms. Improved measurement of cortical rhythms through data-driven referencing schemes can enhance understanding of brain activity and behavior.