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.

Finding a low-dimensional manifold of neural signals is crucial for understanding neural computation and developing a robust brain-computer interface (BCI). Latent variable models have been proposed to find such manifolds, and proved to be successful in various cognitive and behavioral tasks. However, natural speaking is a complex coordination of multiple motor tasks, and due to such complex nature, it has been challenging to find a low-dimensional and behaviorally relevant manifold of the speech cortex. We hypothesize that contents represented on the manifold should be consistent across repea

Animals can learn and perform many behaviors without interference. However, it is unclear how changes in neural activity during learning avoid interference with activity underlying similar behaviors. Recently, Losey et al. (bioRxiv, 2022) identified a possible explanation by examining monkey motor cortical population activity under a brain-computer interface (BCI) paradigm where linear mappings controlled a computer cursor. The activity produced for a known mapping was altered after learning a new mapping in a way that made the activity more beneficial for this new mapping. That is, the activi

Advancements in machine learning and artificial intelligence have given rise to promising neural network-based models of the complex nonlinear relationships between movement intention and neural activity. In particular, recurrent neural networks (RNNs) are one commonly used algorithm for brain-computer interface (BCI) control, due to their ability to learn temporal dependencies within the training data. However, most BCI training data contains highly stereotyped temporal and behavioral structure to which RNNs can overfit, causing poor performance in online settings when behavior and temporal c

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

When presented with identical task conditions across multiple trials, the brain produces variable patterns of neural activity. This neural variability stems from many sources, one of which is fluctuations in internal states (e.g., arousal, attention, and motivation). Neural variability may reflect changes in these internal cognitive factors and can influence our perceptual and decision-making abilities. For instance, when tasked with shooting consecutive free throws on a basketball court, we may experience changes in our arousal, and our minds might wander. Such trial-to-trial variability can

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

The local field potential (LFP) is an important brain signal to study due to its role in neural mechanisms such as visual perception and decision making, and in brain-computer interface development. Investigating LFPs often involves complex, biophysically detailed computational models, which face significant computational demands.Addressing this, we developed a simplified computational model of a cortical column representing the mouse V1 area, balancing morphological realism and practicality for large-network simulations. The model includes 17 sub-populations encompassing pyramidal neurons, PV