A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces
نویسندگان
چکیده
منابع مشابه
Combining decoder design and neural adaptation in brain-machine interfaces.
Brain-machine interfaces (BMIs) aim to help people with paralysis by decoding movement-related neural signals into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Despite compelling laboratory experiments and ongoing FDA pilot clinical trials, system performance, robustness, and generalization remain challenges. We provide a perspective on how two com...
متن کاملObservation-based learning for brain-machine interfaces.
Canonically, 'mirror neurons' are cells in area F5 of the ventral premotor cortex that are active during both observation and execution of goal-directed movements. Recently, cells with similar properties have been observed in a number of other areas in the motor system, including the primary motor cortex. Mirror neurons are a part of a system whose function is thought to involve the prediction ...
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In this paper, a hybrid Machine Translation (MT) system is proposed by combining the result of a rule-based machine translation (RBMT) system with a statistical approach. The RBMT uses a set of linguistic rules for translation, which leads to better translation results in terms of word ordering and syntactic structure. On the other hand, SMT works better in lexical choice. Therefore, in our sys...
متن کاملChunk-based Decoder for Neural Machine Translation
Chunks (or phrases) once played a pivotal role in machine translation. By using a chunk rather than a word as the basic translation unit, local (intrachunk) and global (inter-chunk) word orders and dependencies can be easily modeled. The chunk structure, despite its importance, has not been considered in the decoders used for neural machine translation (NMT). In this paper, we propose chunk-bas...
متن کاملDesign and validation of a real-time spiking-neural-network decoder for brain-machine interfaces.
OBJECTIVE Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. APPROACH One possible solution is to...
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ژورنال
عنوان ژورنال: IEEE Transactions on Biomedical Circuits and Systems
سال: 2016
ISSN: 1932-4545,1940-9990
DOI: 10.1109/tbcas.2015.2483618