Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI

Action observation (AO) is widely used as a post-stroke therapy to activate sensorimotor circuits through the mirror neuron system. However, passive observation is often considered to be less effective and less interactive than goal-directed movement observation, leading to the suggestion that observation of goal-directed actions may have stronger therapeutic potential, as goal-directed AO has been shown to activate mechanisms for monitoring action errors. Some studies have also suggested the use of AO as a form of Brain–computer interface (BCI) feedback. In this study, we investigated the potential for observation of virtual hand movements within a P300-based BCI as a feedback system to activate the mirror neuron system. We also explored the role of feedback anticipation and estimation mechanisms during movement observation. Twenty healthy subjects participated in the study. We analyzed event-related desynchronization and synchronization (ERD/S) of sensorimotor EEG rhythms and Error-related potentials (ErrPs) during observation of virtual hand finger flexion presented as feedback in the P300-BCI loop and compared the dynamics of ERD/S and ErrPs during observation of correct feedback and errors. We also analyzed these EEG markers during passive AO under two conditions: when subjects anticipated the action demonstration and when the action was unexpected. A pre-action mu-ERD was found both before passive AO and during action anticipation within the BCI loop. Furthermore, a significant increase in beta-ERS was found during AO within incorrect BCI feedback trials. We suggest that the BCI feedback may exaggerate the passive-AO effect, as it engages feedback anticipation and estimation mechanisms as well as movement error monitoring simultaneously. The results of this study provide insights into the potential of P300-BCI with AO-feedback as a tool for neurorehabilitation

Dynamics of cortical excitability in stimulus-response mapping for overt and covert movements is locked to visual stimulus: an LRP-TMS Study

In this study, we employed EEG recordings to compare cortical dynamics during real hand movements versus motor imagery in a stimulus-response task

Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option

Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation

Mental Strategies in a P300-BCI: Visuomotor Transformation Is an Option

Currently, P300-BCIs are mostly used for spelling tasks, where the number of commands is equal to the number of stimuli that evoke event-related potentials (ERPs). Increasing this number slows down the BCI operation because each stimulus has to be presented several times for better classification. Furthermore, P300 spellers typically do not utilize potentially useful imagery-based approaches, such as the motor imagery successfully practiced in motor rehabilitation. Here, we tested a P300-BCI with a motor-imagery component. In this BCI, the number of commands was increased by adding mental strategies instead of increasing the number of targets. Our BCI had only two stimuli and four commands. The subjects either counted target appearances mentally or imagined hand movements toward the targets. In this design, the motor-imagery paradigm enacted a visuomotor transformation known to engage cortical and subcortical networks participating in motor control. The operation of these networks suffers in neurological conditions such as stroke, so we view this BCI as a potential tool for the rehabilitation of patients. As an initial step toward the development of this clinical method, sixteen healthy participants were tested. Consistent with our expectation that mental strategies would result in distinct EEG activities, ERPs were different depending on whether subjects counted stimuli or imagined movements. These differences were especially clear in the late ERP components localized in the frontal and centro-parietal regions. We conclude that (1) the P300 paradigm is suitable for enacting visuomotor transformations and (2) P300-based BCIs with multiple mental strategies could be used in applications where the number of possible outputs needs to be increased while keeping the number of targets constant. As such, our approach adds to both the development of versatile BCIs and clinical approaches to rehabilitation

Single-Subject TMS Pulse Visualization on MRI-Based Brain Model: A precise method for mapping TMS pulses on cortical surface

Highly accurate visualization of the points of transcranial magnetic stimulation (TMS) application on the brain cortical surface could provide anatomy-specific analysis of TMS effects. TMS is widely used to activate cortical areas with high spatial resolution, and neuronavigation enables site-specific TMS of particular gyrus sites. Precise control of TMS application points is crucial in determining the stimulation effects. Here, we propose a method that gives an opportunity to visualize and analyze the stimulated cortical sites by processing multi-parameter data. • This method uses MRI data to create a participant's brain model for visualization. The MRI data is segmented to obtain a raw 3D model, which is further optimized in 3D modeling software. • A Python script running in Blender uses the TMS coil's orientation data and participant's brain 3D model to define and mark the cortical sites affected by the particular TMS pulse. • The Python script can be easily customized to visualize TMS points task-specifically

Towards a multimodal brain-computer interface: intracranial recordings in humans performing speech and handwriting tasks.

While it is known that the same brain area could be involved in multiple functions, such multimodality has yet to be utilized in applications like brain-computer interfaces (BCI). For instance, could the same BCI decode both hand movements and speech? Here we studied stereo EEG (sEEG) patterns in two patients with epilepsy performing motor and language tasks as parts of the same experimental session. sEEG electrodes were implanted in various regions of frontal and temporal cortices. In the motor task, the patients wrote digits by hand whereas in the language task they pronounced or imagined pronouncing words. The superior frontal gyrus (SFG) and superior temporal gyrus (STG) were engaged in both tasks whereas the middle frontal gyrus (MFG) and middle temporal gyrus (MTG) were engaged only in the handwriting task. In addition to task-execution neural patterns, preparatory activity was observed, particularly the STG. Based on the differences in the STG and SFG, the articulatory versus imagined speech could be decoded using a machine learning classifier. We suggest that multimodal BCIs be used in the future to improve speech restoration and rehabilitation in neurological patients

Data_Sheet_1_SDA: a data-driven algorithm that detects functional states applied to the EEG of Guhyasamaja meditation.docx

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.</p