## Abstract Animal studies have shown that motherβinfant interactions can have longβterm impacts on areas of the brain that regulate fearful behavior and the physiology of stress. Here, the research on human infants and children is reviewed with an eye to whether early experiences have similar effe
The Development of Brain-Machine Interface Neuroprosthetic Devices
β Scribed by Parag G. Patil; Dennis A. Turner
- Publisher
- Springer-Verlag
- Year
- 2008
- Tongue
- English
- Weight
- 101 KB
- Volume
- 5
- Category
- Article
- ISSN
- 1933-7213
No coin nor oath required. For personal study only.
β¦ Synopsis
The development of brain-machine interface technology is a logical next step in the overall direction of neuroprosthetics. Many of the required technological advances that will be required for clinical translation of brain-machine interfaces are already under development, including a new generation of recording electrodes, the decoding and interpretation of signals underlying intention and planning, actuators for implementation of mental plans in virtual or real contexts, direct somatosensory feedback to the nervous system to refine actions, and training to encourage plasticity in neural circuits. Although pre-clinical studies in nonhuman primates demonstrate high efficacy in a realistic motor task with motor cortical recordings, there are many challenges in the clinical translation of even simple tasks and devices. Foremost among these challenges is the development of biocompatible electrodes capable of long-term, stable recording of brain activity and implantable amplifiers and signal processors that are sufficiently resistant to noise and artifact to faithfully transmit recorded signals to the external environment. Whether there is a suitable market for such new technology depends on its efficacy in restoring and enhancing neural function, its risks of implantation, and its long-term efficacy and usefulness. Now is a critical time in brain-machine interface development because most ongoing studies are science-based and noncommercial, allowing new approaches to be included in commercial schemes under development.
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## Abstract Previous decoding algorithms used in brain machine interfaces (BMIs) usually seek a static functional mapping between the spatioβtemporal neural activity and behavior and assume that the neural spike statistics do not change over time. However, recent work indicates the significant vari