相关论文: Peripheral brain interfacing: Reading high-frequen…

Brain-Computer Interface(BCI) systems support communication through direct measures of neural activity without muscle activity. Brain-Computer Interface systems need to be validated in long-term studies of real-world use by people with…

Continuous monitoring of non-invasive skin sympathetic nerve activity (SKNA) holds promise for understanding the sympathetic nervous system (SNS) dynamics in various physiological and pathological conditions. However, muscle noise artifacts…

Brain-computer interfaces (BCIs) make possible to interact with the external environment by decoding the mental intention of individuals. BCIs can therefore be used to address basic neuroscience questions but also to unlock a variety of…

Decoding brain signals accurately and efficiently is crucial for intra-cortical brain-computer interfaces. Traditional decoding approaches based on neural activity vector features suffer from low accuracy, whereas deep learning based…

Objective: Identifying the activity of motor neurons (MNs) non-invasively is possible by decomposing signals from muscles, e.g., surface electromyography (EMG) or ultrasound. The theoretical background of MN identification is convolutive…

The brain uses positive signals as a means of signaling. Forward interactions in the early visual cortex are also positive, realized by excitatory synapses. Only local interactions also include inhibition. Non-negative matrix factorization…

Brain-computer interfaces (BCIs) use brain signals such as electroencephalography to reflect user intention and enable two-way communication between computers and users. BCI technology has recently received much attention in healthcare…

BCI systems are able to communicate directly between the brain and computer using neural activity measurements without the involvement of muscle movements. For BCI systems to be widely used by people with severe disabilities, long-term…

Brain signals constitute the information that are processed by millions of brain neurons (nerve cells and brain cells). These brain signals can be recorded and analyzed using various of non-invasive techniques such as the…

Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer…

Neuroscientists today can measure activity from more neurons than ever before, and are facing the challenge of connecting these brain-wide neural recordings to computation and behavior. Here, we first describe emerging tools and…

Brain-computer interfaces (BCIs), transform neural signals in the brain into in-structions to control external devices. However, obtaining sufficient training data is difficult as well as limited. With the advent of advanced machine…

Brain-computer interface (BCI) is a practical pathway to interpret users' intentions by decoding motor execution (ME) or motor imagery (MI) from electroencephalogram (EEG) signals. However, developing a BCI system driven by ME or MI is…

Brain signals accompany various information relevant to human actions and mental imagery, making them crucial to interpreting and understanding human intentions. Brain-computer interface technology leverages this brain activity to generate…

Decoding behavior, perception, or cognitive state directly from neural signals has applications in brain-computer interface research as well as implications for systems neuroscience. In the last decade, deep learning has become the…

Deep convolutional neural networks (CNNs) have structures that are loosely related to that of the primate visual cortex. Surprisingly, when these networks are trained for object classification, the activity of their early, intermediate, and…

Non-invasive cortical neural interfaces have only achieved modest performance in cortical decoding of limb movements and their forces, compared to invasive brain-computer interfaces (BCIs). While non-invasive methodologies are safer,…

Developing accurate hand gesture perception models is critical for various robotic applications, enabling effective communication between humans and machines and directly impacting neurorobotics and interactive robots. Recently, surface…

Encoding models are used for predicting brain activity in response to sensory stimuli with the objective of elucidating how sensory information is represented in the brain. Encoding models typically comprise a nonlinear transformation of…

Despite many years of research, the quest to identify neural correlates of perceptual consciousness (NCC) remains unresolved. One major obstacle lies in methodological limitations: most studies rely on non-invasive neural measures with…