Topic: motor imagery

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

Brain-computer interface (BCI) applications enable innovative communication with and control 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

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