Topic: brain-computer interface

Nature staff discuss geophysical evidence that could indicate hidden chambers beyond Tutankhamun's tomb and what the findings might imply for the search for Nefertiti's burial place. The episode also examines a multifunctional brain-computer interface that translates neural activity into both speech and gestures, highlighting how one implant can support several forms of communication.

The role of the interface between internal and external factors which shape an agent's experience have been considered for millennia. In the 21st century, we have access to new technologies and ideas, but what are the breakthroughs we need that will afford new discoveries? Similarly, what disciplinary boundaries are appropriate as opposed to inhibitory; how much does neuroscience overlap with or have functional or explanatory distinctness from psychology, or other disciplines within the original umbrella of the Cognitive Science project? These questions drive our work in methods for cognition

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

We have been developing Brain-Computer Interface (BCI) using electrocorticography (ECoG) [1] , which is recorded by electrodes implanted on brain surface, and magnetoencephalography (MEG) [2] , which records the cortical activities non-invasively, for the clinical applications. The invasive BCI using ECoG has been applied for severely paralyzed patient to restore the communication and motor function. The non-invasive BCI using MEG has been applied as a neurofeedback tool to modulate some pathological neural activities to treat some neuropsychiatric disorders. Although these techniques have bee

Learning is a population phenomenon. Since it is the organized activity of populations of neurons that cause movement, learning a new skill must involve reshaping those population activity patterns. Seeing how the brain does this has been elusive, but a brain-computer interface approach can yield new insight. We presented monkeys with novel BCI mappings that we knew would be difficult for them to learn how to control. Over several days, we observed the emergence of new patterns of neural activity that endowed the animals with the ability to perform better at the BCI task. We speculate that the

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