Sparse linear regression with elastic net regularization for brain-computer interfaces.
This paper demonstrates the feasibility of decoding neuronal population signals using a sparse linear regression model with an elastic net penalty. In offline analysis of real electrocorticographic (ECoG) neural data the elastic net achieved a timepoint decoding accuracy of 95% for classifying hand grasps vs. rest, and 82% for moving a cursor in 1-D space towards a target. These results were superior to those obtained using ℓ(2)-penalized and unpenalized linear regression, and marginally better than ℓ(1)-penalized regression. Elastic net and the ℓ(1)-penalty also produced sparse feature sets, but the elastic net did not eliminate correlated features, which could result in a more stable decoder for brain-computer interfaces.
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA. jwkelly@ece.cmu.edu
John W
JW
Kelly
Alan D
AD
Degenhart
Daniel P
DP
Siewiorek
Asim
A
Smailagic
Wei
W
Wang
D000465:Algorithms; D062207:Brain-Computer Interfaces; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000503:physiopathology* / Q000534:rehabilitation; D019054:Evoked Potentials, Motor; D018737:Hand Strength; D006801:Humans; D016014:Linear Models; D009044:Motor Cortex / Q000503:physiopathology*; D009415:Nerve Net / Q000503:physiopathology*; D009473:Neuronal Plasticity; D010363:Pattern Recognition, Automated / Q000379:methods; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity