ABSTRACT
The study presents a novel approach designed to detect time-continuous states in time-series data, called the State-Detecting Algorithm (SDA). The SDA operates on unlabeled data and detects optimal change-points among intrinsic functional states in time-series data based on an ensemble of Ward's hierarchical clustering with time-connectivity constraint. The algorithm chooses the best number of states and optimal state boundaries, maximizing clustering quality metrics. We also introduce a series of methods to estimate the performance and confidence of the SDA when the ground truth annotation is unavailable. These include information value analysis, paired statistical tests, and predictive modeling analysis. The SDA was validated on EEG recordings of Guhyasamaja meditation practice with a strict staged protocol performed by three experienced Buddhist practitioners in an ecological setup. The SDA used neurophysiological descriptors as inputs, including PSD, power indices, coherence, and PLV. Post-hoc analysis of the obtained EEG states revealed significant differences compared to the baseline and neighboring states. The SDA was found to be stable with respect to state order organization and showed poor clustering quality metrics and no statistical significance between states when applied to randomly shuffled epochs (i.e., surrogate subject data used as controls). The SDA can be considered a general data-driven approach that detects hidden functional states associated with the mental processes evolving during meditation or other ongoing mental and cognitive processes.
ABSTRACT
The main aim of the present study was to investigate effects of partial reductions of electromyogram (EMG) on high-frequency scalp electroencephalogram (EEG) at rest and during performance of certain cognitive tasks. Nineteen healthy women performed the same cognitive tasks before and after cosmetic injections of Dysport in certain sites of facial muscles. Scalp EEG and EMG were recorded. Impact of Dysport injections on changes of spectral power in ß2 and low γ frequency ranges (18-40 Hz) in EEG and EMG derivations was investigated. Also changes of spectral power in EEG and EMG derivations during comparisons of different cognitive states were calculated before and after Dysport injections separately. Dysport injections led to EMG decreases in facial muscles around the injection zones and also led to reductions of power of electric processes in scalp derivations. Along with it results of EEG power comparisons between the pairs of the cognitive states were qualitatively similar before and after Dysport injections. These facts to all appearance demonstrate that though scalp EEGs in the range above 15-40 Hz are contaminated by EMG, in certain experimental situations EMG contamination does not preclude qualitative detections of electroencephalographic correlates of mental activities in ß2 and low γ frequency ranges. Parallel EEG and EMG registrations can help not to overestimate EMG contamination in psychophysiological EEG studies.
