Bayesian Population Decoding of Motor Cortical Activity Using a Kalman Filter

By Wei Wu, Yun Gao, Elie Bienenstock, John P. Donoghue, Michael J. Black. Neural Computation, 2006

492 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Signal Processing

View Paper: https://www.semanticscholar.org/paper/87dfde8a114d37f80ebc8aa1783635fa1aa9f6a0

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Citations in the corpus

Neural control of computer cursor velocity by decoding motor cortical spiking activity in humans with tetraplegia (2008, methodology): “Details of the model training and use of the Kalman filter for neural decoding can be found in Wu et al [24, 25].”
The Science of Neural Interface Systems (2009, unknown): “A statistically principled approach begins with an encoding model that estimates the conditional probability of a neuron's response, given the motor behavior of the animal (Brockwell et al. 2004, Kemere et al. 2004, Sanger 1996, Shoham et al. 2005, Wu et al. 2006, Wu & Hatsopoulos 2008).”
Restoring motor control and sensory feedback in people with upper extremity amputations using arrays of 96 microelectrodes implanted in the median and ulnar nerves (2016, methodology): “A standard Kalman filter was implemented to perform the continuous neural decodes [61].”
Dimensionality reduction for large-scale neural recordings (2014, background): “Some prominent examples include decoding sensory stimuli 66,94 , physical location 62 , arm movements 65,95,96 , working memory 97 , object information 98 and more.”
Reach and grasp by people with tetraplegia using a neurally controlled robotic arm (2012, unknown): “To initialize the Kalman filter 12,36 , a tuning function was estimated for each unit by regressing its threshold crossing rates against instantaneous target directions (see below).”
Bridging the brain to the world: a perspective on neural interface systems. (2008, unknown): “Virtually every readily identifiable method of decoding information has been attempted in both FP and spike signals (for further discussion see Paninski et al., 2007; Schwartz et al., 2006; Serruya et al., 2003; Srinivasan et al., 2007; Truccolo et al., 2005; Wu et al., 2006 ).”
Functional, but Not Anatomical, Separation of "what" and "when" in Prefrontal Cortex Gaussian-process Factor Analysis for Low-dimensional Single-trial Analysis of Neural Population Activity (unknown): “1 preserves computational tractability (e.g., Wu et al. 2006 ).”