Kelly John W, Degenhart Alan D, Siewiorek Daniel P, Smailagic Asim, Wang Wei
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2012;2012:4275-8. doi: 10.1109/EMBC.2012.6346911.
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.
本文展示了使用具有弹性网络惩罚的稀疏线性回归模型解码神经元群体信号的可行性。在对真实皮层脑电图（ECoG）神经数据的离线分析中，弹性网络在区分手部抓握与休息状态时，时间点解码准确率达到95%，在一维空间中向目标移动光标时的准确率为82%。这些结果优于使用ℓ(2)惩罚和无惩罚线性回归所获得的结果，并且略优于ℓ(1)惩罚回归。弹性网络和ℓ(1)惩罚也产生了稀疏特征集，但弹性网络并未消除相关特征，这可能会为脑机接口带来更稳定的解码器。
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