ospEDA decomposes EDA signals into tonic and phasic parts via valley detection, orthogonal subspace projection, and NNLS deconvolution, achieving lower RMSE and higher F1 scores than six prior methods across simulated noise levels and five real datasets.
A Unified Dynamic Model for the Decomposition of Skin Conductance and the Inference of Sudomotor Nerve Activities
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ospEDA: Orthogonal Subspace Projection for Electrodermal Activity Decomposition
ospEDA decomposes EDA signals into tonic and phasic parts via valley detection, orthogonal subspace projection, and NNLS deconvolution, achieving lower RMSE and higher F1 scores than six prior methods across simulated noise levels and five real datasets.