Paper Info
Title
Sparse Coding Of Ecog Signals Identifies Interpretable Components For Speech Control In Human Sensorimotor Cortex
Abstract
The concept of sparsity has proven useful to understanding elementary neural computations in sensory systems. However, the role of sparsity in motor regions is poorly understood. Here, we investigated the functional properties of sparse structure in neural activity collected with high-density electrocorticography (ECoG) from speech sensorimotor cortex (vSMC) in neurosurgical patients. Using independent components analysis (ICA), we found individual components corresponding to individual major oral articulators (i.e., Coronal Tongue, Dorsal Tongue, Lips), which were selectively activated during utterances that engaged that articulator on single trials. Some of the components corresponded to spatially sparse activations. Components with similar properties were also extracted using convolutional sparse coding (CSC), and required less data pre-processing. Finally, individual utterances could be accurately decoded from vSMC ECoG recordings using linear classifiers trained on the high-dimensional sparse codes generated by CSC. Together, these results suggest that sparse coding may be an important framework and tool for understanding sensory-motor activity generating complex behaviors, and may be useful for brain-machine interfaces.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037645
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Electrocorticography,Neural coding,Computer science,Neural activity,Speech recognition,Sensorimotor cortex,Independent component analysis,Sensory system,Dorsal tongue
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
3
4
Name
Order
Citations
PageRank
Kristofer E Bouchard1188.99
Alejandro F Bujan271.22
Edward Chang312.71
Friedrich T. Sommer429330.93