The less complex temporal patterns of resting­state EEG activity, the lower the visual temporal order threshold.

Speech understanding, watching a movie, listening to music etc., requires perception of the temporal order of at least two incoming events. A history of performing these tasks may be reflected in spontaneous brain activity. Here, we examined the relationship between the complexity (temporal dynamics) of resting­state EEG (rsEEG) signal, assessed using the multivariate MultiScale Entropy (mMSE) algorithm, and the perception of event ordering, indexed by a visual temporal order threshold (TOT), i.e., the minimum duration necessary to correctly identify the before­after relation between two stimuli. Healthy adolescents and young adults performed a psychophysical task measuring the TOT and underwent an eyes­closed rsEEG study. The features of mMSE vectors, namely the area under curve (AUC) that represents total signal complexity, as well as the MaxSlope and the AvgEnt, corresponding to the entropy at fine­ and coarse­grained timescales, respectively, were obtained for the central (midline), anterior, middle and posterior channel sets. The greater the AUC and AvgEnt values in the central, left and right posterior areas, and the higher AUC in the right middle region, the higher the TOT. The most significant relationships were found for the midline electrodes (Fz, Cz, Pz, Oz). There were no significant correlations between the MaxSlope values and the TOT. To the best of our knowledge, this is the first study demonstrating that spontaneous EEG signal complexity is associated with the temporal order perception of two stimuli presented in rapid succession. Our findings may indicate that low total and coarse entropy levels of rsEEG signal are beneficial for visual temporal order judgments.

Novel Methodological Tools for Behavioral Interventions: The Case of HRV-Biofeedback. Sham Control and Quantitative Physiology-Based Assessment of Training Quality and Fidelity.

Scientific research on heart rate variability (HRV) biofeedback is burdened by certain methodological issues, such as lack of consistent training quality and fidelity assessment or control conditions that would mimic the intervention. In the present study, a novel sham HRV-biofeedback training was proposed as a credible control condition, indistinguishable from the real training. The Yield Efficiency of Training Index (YETI), a quantitative measure based on the spectral distribution of heart rate during training, was suggested for training quality assessment. A training fidelity criterion derived from a two-step classification process based on the average YETI index and its standard deviation (YETISD) was suggested. We divided 57 young, healthy volunteers into two groups, each subjected to 20 sessions of either real or sham HRV-biofeedback. Five standard HRV measures (standard deviation of the NN (SDNN), root mean square of the standard deviation of the NN (RMSSD), total power, low-frequency (LF), and high-frequency (HF) power) collected at baseline, after 10 and 20 sessions were subjected to analysis of variance. Application of a training fidelity criterion improved sample homogeneity, resulting in a substantial gain in effect sizes of the group and training interactions for all considered HRV indices. Application of methodological amendments, including proper control conditions (such as sham training) and quantitative assessment of training quality and fidelity, substantially improves the analysis of training effects. Although presented on the example of HRV-biofeedback, this approach should similarly benefit other behavioral training procedures that interact with any of the many psychophysiological mechanisms in the human body.