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Abstract Tactile Imagery (TI) remains a fairly understudied phenomenon despite growing attention to this topic in recent years. Here, we investigated the effects of TI on corticospinal excitability by measuring motor evoked potentials (MEPs) induced by single-pulse transcranial magnetic stimulation (TMS). The effects of TI were compared with those of tactile stimulation (TS) and kinesthetic motor imagery (kMI). Twenty-two participants performed three tasks in randomly assigned order: imagine finger tapping (kMI); experience vibratory sensations in the middle finger (TS); and mentally reproduce the sensation of vibration (TI). MEPs increased during both kMI and TI, with a stronger increase for kMI. No statistically significant change in MEP was observed during TS. The demonstrated differential effects of kMI, TI and TS on corticospinal excitability have practical implications for devising the imagery-based and TS-based brain–computer interfaces (BCIs), particularly the ones intended to improve neurorehabilitation by evoking plasticity changes in sensorimotor circuitry.

Error-related potentials (ErrPs) have attracted attention in part because of their practical potential for building brain-computer interface (BCI) paradigms. BCIs, facilitating direct communication between the brain and machines, hold great promise for brain-AI interaction. Therefore, a comprehensive understanding of ErrPs is crucial to ensure reliable BCI outcomes. In this study, we investigated ErrPs in the context of the “guess what I am thinking” paradigm. 23 healthy participants were instructed to imagine an object from a predetermined set, while an algorithm randomly selected another object that was either the same as or different from the imagined object. We recorded and analyzed the participants’ EEG activity to capture their mental responses to the algorithm’s “predictions”. The study identified components distinguishing correct from incorrect responses. It discusses their nature and how they differ from ErrPs extensively studied in other BCI paradigms. We observed pronounced variations in the shape of ErrPs across different stimulus sets, underscoring the significant influence of visual stimulus appearance on ErrP peaks. These findings have implications for designing effective BCI systems, especially considering the less conventional BCI paradigm employed. They emphasize the necessity of accounting for stimulus factors in BCI development.

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.