EMG prediction from motor cortical recordings via a nonnegative point-process filter

IEEE Transactions on Biomedical Engineering

Volumn 59, Issue 7, 2012, Pages 1829-1838

  Nazarpour, Kianoush ^a   Ethier, Christian ^b   Paninski, Liam ^c   Rebesco, James M ^b   Miall, R Chris ^d   Miller, Lee E ^b  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

^b NORTHWESTERN UNIVERSITY   (United States)

^c Och Spine at New York Presbyterian Hospitals   (United States)

^d UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

Brain machine interface (BMI); electromyogram (EMG) signal; generalized linear model (GLM); Kalman filter; optimization

Indexed keywords

BIN SIZE; BRAIN MACHINE INTERFACE; CORTICAL RECORDING; COVARIATES; ELECTROMYOGRAM; EMG SIGNAL; FILTERING MECHANISM; GAUSSIANITY; GENERALIZED LINEAR MODEL; HAND MUSCLES; LIMITED TRAINING DATA; MULTI-CHANNEL; NEURAL ACTIVITY; NEURAL SPIKE; NEURAL SPIKE TRAINS; NON-GAUSSIAN; NONNEGATIVITY CONSTRAINTS; OPTIMIZATION FRAMEWORK; PREDICTION ACCURACY; STATE EVOLUTIONS; STATIC NON-LINEARITY; TRAINING DATA SETS;

BRAIN; ELECTROMYOGRAPHY; GAUSSIAN NOISE (ELECTRONIC); KALMAN FILTERS; NEURAL NETWORKS; NEURONS; OPTIMIZATION;

FORECASTING;

ARTICLE; BRAIN COMPUTER INTERFACE; BRAIN NERVE CELL; ELECTROMYOGRAM; FOREARM; HAND; HAPLORHINI; HUMAN; KERNEL METHOD; MOTOR CORTEX; MUSCLE; MUSCLE CONTRACTION; PINCH STRENGTH;

ALGORITHMS; ANIMALS; DATABASES, FACTUAL; ELECTRODES, IMPLANTED; ELECTROMYOGRAPHY; FOREARM; HAND; LINEAR MODELS; MACACA MULATTA; MAN-MACHINE SYSTEMS; MOTOR CORTEX; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 84862745270     PISSN: 00189294     EISSN: 15582531     Source Type: Journal    
DOI: 10.1109/TBME.2011.2159115     Document Type: Article

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