Abstract
Rhythmic neural activity, so-called oscillations, plays a key role in neural information transmission, processing, and storage. Neural oscillations in distinct frequency bands are central to physiological brain function, and alterations thereof have been associated with several neurological and psychiatric disorders. The most common methods to analyze neural oscillations, e.g., short-time Fourier transform or wavelet analysis, assume that measured neural activity is composed of a series of symmetric prototypical waveforms, e.g., sinusoids. However, usually, the models generating the signal, including waveform shapes of experimentally measured neural activity are unknown. Decomposing asymmetric waveforms of nonlinear origin using these classic methods may result in spurious harmonics visible in the estimated frequency spectra. Here, we introduce a new method for capturing rhythmic brain activity based on recurrences of similar states in phase-space. This method allows for a time-resolved estimation of amplitude fluctuations of recurrent activity irrespective of or specific to waveform shapes. The algorithm is derived from the well-established field of recurrence analysis, which, in comparison to Fourier-based analysis, is still very uncommon in neuroscience. In this paper, we show its advantages and limitations in comparison to short-time Fourier transform and wavelet convolution using periodic signals of different waveform shapes. Furthermore, we demonstrate its application using experimental data, i.e., intracranial and noninvasive electrophysiological recordings from the human motor cortex of one epilepsy patient and one healthy adult, respectively.
Highlights
During the last two decades, neural oscillations have gained increasing attention as a fundamental mechanism of neural communication (Buehlmann and Deco, 2010; Palmigiano et al, 2017)
Using combined multitaper-wavelet analysis, we found a desynchronization around the button press (∼−1–0 ms), ranging across frequency bands delta to beta (∼3–32 Hz) in all three electrodes (Figures 10Bii–Dii)
In contrast to wavelet analysis, we found a broad gamma activation during the button press, which was most prominently followed by a gamma desynchronization in electrode two and visible in electrodes one and three to a lesser degree
Summary
During the last two decades, neural oscillations have gained increasing attention as a fundamental mechanism of neural communication (Buehlmann and Deco, 2010; Palmigiano et al, 2017). Neural oscillations are defined as temporally recurring patterns of neuronal activity, referred to as periodic and rhythmic activity. Oscillations mainly represent synchronized input to neural ensembles consisting of thousands of cells (Buzsáki and Draguhn, 2004). The spatial specificity of recorded activity mainly depends on the measurement device used, with, e.g., surface electrocorticography covering a much broader scale than, e.g., invasive local field potential recordings (LFPs). Oscillatory activity in the human brain is subdivided into five frequency bands: delta (30 Hz) (Buzsáki and Draguhn, 2004). A wide range of physiological processes in the animal and
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