![]() ![]() ![]() Such precise timing, nevertheless, is often difficult to achieve. This methodology has powerful implications for understanding the neural basis of joint actions, such as conversation however, it also demands precise time-locking between the different brain recordings and sensory stimulation. Hyperscanning is an emerging technique that allows for the study of brain similarities between interacting individuals. We discuss practical implications (sampling rate, SNR, computation time, and data length) and aim to provide recommendations tailored to particular research questions. For group analysis, choosing the appropriate spectral estimation method appears to be more critical than the connectivity measure. Our results show that, overall, WPPC and GCMI tend to outperform other connectivity measures, while entropy was the only measure sensitive to bimodal deviations from a uniform phase distribution. We provide performance measures of each combination for simulated data (with precise control over true connectivity), a single-subject set of real MEG data, and a full group analysis of real MEG data. Here we systematically compare combinations of six standard spectral estimation methods (comprising fast Fourier and continuous wavelet transformation, bandpass filtering, and short-time Fourier transformation) and six connectivity measures (phase-locking value, Gaussian-Copula mutual information, Rayleigh test, weighted pairwise phase consistency, magnitude squared coherence, and entropy). As there is no consensus regarding best practice, a wide variety of methods has been applied. This leaves the investigator with two critical choices, namely a) the appropriate measure for spectral estimation (i.e., the transformation into the frequency domain) and b) the actual connectivity measure. Due to the distinct rhythmicity of these signals, undirected connectivity is typically assessed in the frequency domain. These results describe a specific timeline for speech tracking in speakers and listeners in line with the idea of a speech chain and hence, delays in communication.Īnalyses of cerebro-peripheral connectivity aim to quantify ongoing coupling between brain activity (measured by MEG/EEG) and peripheral signals such as muscle activity, continuous speech, or physiological rhythms (such as pupil dilation or respiration). Maximal speech tracking takes place approximately 110 ms after auditory presentation during perception and 25 ms before vocalisation during speech production. In the 2–10 Hz frequency range, we identified different latencies for maximal speech envelope tracking during speech production and speech perception. After time-locking EEG data collection and auditory recording and playback, we used a Gaussian copula mutual information measure to estimate the relationship between information content in the EEG and auditory signals. We use a paradigm in which participants listen to natural speech (Listening), produce natural speech (Speech Production), and listen to the playback of their own speech (Self-Listening), all while their neural activity is recorded with EEG. This study investigates the dynamics of speech envelope tracking during speech production, listening and self listening. ![]()
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