Motor imagery

Error-related potentials (ErrPs) represent the neural signature of error processing in the brain and numerous studies have demonstrated their reliable detection using non-invasive techniques such as electroencephalography (EEG). Over recent decades, the brain-computer interface (BCI) community has shown growing interest in leveraging these intrinsic feedback signals to enhance system performance. However, the effective use of ErrPs in a closed-loop setup crucially depends on accurate single-trial detection, which is typically achieved using a subject-specific classifier (or decoder) trained on

Motor imagery and overt movement have been hypothesized to be functionally equivalent. At the same time there is evidence that hints at distinct mechanisms of both processes. To explore this disparity, we investigated whether prior imagined movements had the same effect as prior overt movements on motor adaptation performance during a subsequent reach. Additionally, we aimed to identify neuronal correlates of motor imagery predicting such motor adaptation.Movement kinematics (exoskeleton robot, Kinarm Lab) and EEG of 60 participants were recorded to investigate direction-specific adaptation du

Understanding how the brain processes motor and tactile imagery is crucial for advancing motor control and rehabilitation.However,there is a knowledge gap in research where EEG recordings are combined with TMS to study the effects of imagery:little is known about tactile imagery (TI) effects.In this study, we aimed to bridge this gap by examining corticospinal excitability during motor imagery (MI) and TI using TMS-EEG.Our previous findings demonstrated that both MI and TI increase corticospinal excitability,with MI having a stronger effect,whereas tactile stimulation (TS) decreases corticospi

Motor imagery (MI) is defined as a mental simulation of movements without any actual motor action. In last decades, several studies had advanced our understanding of this mental process. They notably showed shared neuroanatomical substrates between MI and physical practice. These neuronal networks seem to be dependant of individual expertise. Although the scientific literature seems to agree for a reduced and focused brain activity as expertise increases, a few investigated complex whole-body movements because MI is usually studied through standardized common tasks (e.g., keyboard sequences).

Motor Imagery (MI) based Brain-Computer Interfaces (BCI) can facilitate very early motor rehabilitation during the acute post-stroke period that is characterised by both greater impairment and, paradoxically, greater plasticity. Current Electroencephalography (EEG) based approaches require substantial time and effort to learn to use. We suggest that a multimodal and multiphase approach may allow stroke survivors to learn to use the BCI more rapidly, enabling early bedside intervention.In phase one, for two sessions, 17 participants learned to perform MI with the help of Transcranial Magnetic S

Aims: nowadays, commercial lightweight electroencephalography (EEG) headsets such as Emotiv EPOC and Muse are gaining popularity in the neuroscience community, mostly in research that monitors natural behaviour outside traditional laboratory settings. These devices often utilize only a few dry or active electrodes in specific locations and signal quality is often inferior compared to their traditional counterparts equipped with electrode caps that require conductive paste for proper functioning. In this study, we wanted to assess the feasibility of a portable, dry electrode-based EEG headset m

The motor imagery-based Brain-Computer Interface (BCI) converts the motor intention into a control signal by categorizing the electrophysiological patterns of various imaginative activities using ECoG, which has a broader frequency range and more excellent input quality than EEG. Nevertheless, because ECoG is an invasive method, it would be helpful to create an ECoG bidirectional classifier that minimizes the number of implanted electrodes. The current work aims to create a classifier for ECoG signals that can achieve high accuracy with a small number of electrodes. We utilized the ECoG datase

Moving a hand is like sailing: your motor cortex, the captain, receives hand location information to define movement parameters; and throughout the movement, your somatosensory areas, the localization equipment, gather data to ensure your hand is on the correct course, and to guide the captain in further commands. Models of information flow in primates suggest that during volitional movement, the sensory state information in Brodmann Area 3b (Ba3b) is spread to Ba4, then sent to muscles via motor commands, followed by muscle proprioceptive information through the thalamus to Ba2, and finally b

…of external devices for people with motor deficits by providing a bridge between brain signals and motor actions to replace and restore beneficial function. To measure neural response, Electrocorticography (ECoG) has been utilized as an effective modality for brain-computer interfaces (BCIs). Many studies have shown that both motor imagery and movement execution are located in the frontoparietal region of the brain (Hardwick et. al., 2018). In this study, we aim to demonstrate a shared representation o