The voice we most often hear is our own, and proper interaction between speaking and hearing is essential for both acquisition and performance of spoken language. Disturbed audiovocal interactions have been implicated in aphasia, stuttering, and schizophrenic voice hallucinations, but paradigms for a noninvasive assessment of auditory self-monitoring of speaking and its possible dysfunctions are rare. Using magnetoencephalograpy we show here that self-uttered syllables transiently activate the speaker's auditory cortex around 100 ms after voice onset. These phasic responses were delayed by 11 ms in the speech-dominant left hemisphere relative to the right, whereas during listening to a replay of the same utterances the response latencies were symmetric. Moreover, the auditory cortices did not react to rare vowel changes interspersed randomly within a series of repetitively spoken vowels, in contrast to regular change-related responses evoked 100-200 ms after replayed rare vowels. Thus, speaking primes the human auditory cortex at a millisecond time scale, dampening and delaying reactions to self-produced "expected" sounds, more prominently in the speech-dominant hemisphere. Such motor-to-sensory priming of early auditory cortex responses during voicing constitutes one element of speech self-monitoring that could be compromised in central speech disorders.

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Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law $$P\propto 1/{f}^{\beta }$$ P ∝ 1 / f β and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent $$\beta$$ β . For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them.