Neural interface systems with on-device computing: machine learning and neuromorphic architectures
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Future neuroprosthetics will be tightly coupled with the user in such a way that the resulting system can replace and restore impaired upper limb functions because controlled by the same neural signals than their natural counterparts. However, robust and n ...
This paper reports a novel flexible neural probe fabricated by cylindrical substrates lithography for Brain-Computer-Interface (BCI) applications. The electrode sites were patterned on the cylindrical surface to acquire high space selectivity and the micro ...
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A brain-machine interface (BMI) is about transforming neural activity into action and sensation into perception (Figure 1). In a BMI system, neural signals recorded from the brain are fed into a decoding algorithm that translates these signals into motor o ...
As a result of improved understanding of brain mechanisms as well as unprecedented technical advancement in neural recording methods and computer technology, it is now possible to translate large-scale brain signals into movement intentions in real time. S ...
Brain-machine interfaces hold promise for restoring basic functions such as movement or speech for severely disabled patients, as well as for controlling neuroprosthetic devices for amputees. One of the major challenges of clinically viable neuroprostheses ...
Neuroprostheses based on electrical stimulation could potentially help disabled persons. They are based on neural interface that aim at creating an intimate contact with neural cells. The efficacy of neuroprostheses can be improved by increasing the select ...
The treatment of disorders of the nervous system poses a major clinical challenge. Development of neuromodulation (i.e., interfacing electronics to nervous tissue to modulate its function) has provided patients with neuronal-related deficits a new tool to ...
Sparse methods are widely used in image and audio processing for denoising and classification, but there have been few previous applications to neural signals for brain-computer interfaces (BCIs). We used the dictionary- learning algorithm K-SVD, coupled w ...
