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ICPR
2010
IEEE

A Simulation Study on the Generative Neural Ensemble Decoding Algorithms

9 years 9 months ago
A Simulation Study on the Generative Neural Ensemble Decoding Algorithms
Brain-computer interfaces rely on accurate decoding of cortical activity to understand intended action. Algorithms for neural decoding can be broadly categorized into two groups: direct versus generative methods. Two generative models, the population vector algorithm (PVA) and the Kalman filter (KF), have been widely used for many intracortical BCI studies, where KF generally showed superior decoding to PVA. However, little has been known for which conditions each algorithm works properly and how KF translates the ensemble information. To address these questions, we performed a simulation study and demonstrated that KF and PVA worked congruently for uniformly distributed preferred directions (PDs) whereas KF outperformed PVA for non-uniform PDs. In addition, we showed that KF decoded better than PVA for low signal-tonoise ratio (SNR) or a small ensemble size. The results suggest that KF may decode direction better than PVA with nonuniform PDs or with low SNR and small ensemble size. Ke...
Sung-Phil Kim, Min-Ki Kim, Gwi-Tae Park
Added 13 Feb 2011
Updated 13 Feb 2011
Type Journal
Year 2010
Where ICPR
Authors Sung-Phil Kim, Min-Ki Kim, Gwi-Tae Park
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