Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

Frontiers in Neuroscience
Subash PadmanabanBradley Greger

Abstract

Objective: The performance of machine learning algorithms used for neural decoding of dexterous tasks may be impeded due to problems arising when dealing with high-dimensional data. The objective of feature selection algorithms is to choose a near-optimal subset of features from the original feature space to improve the performance of the decoding algorithm. The aim of our study was to compare the effects of four feature selection techniques, Wilcoxon signed-rank test, Relative Importance, Principal Component Analysis (PCA), and Mutual Information Maximization on SVM classification performance for a dexterous decoding task. Approach: A nonhuman primate (NHP) was trained to perform small coordinated movements-similar to typing. An array of microelectrodes was implanted in the hand area of the motor cortex of the NHP and used to record action potentials (AP) during finger movements. A Support Vector Machine (SVM) was used to classify which finger movement the NHP was making based upon AP firing rates. We used the SVM classification to examine the functional parameters of (i) robustness to simulated failure and (ii) longevity of classification. We also compared the effect of using isolated-neuron and multi-unit firing rates as the...Continue Reading

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Citations

Mar 25, 2020··Yasmina Jaufeerally-FakimZahra Mungloo-Dilmohamud

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Methods Mentioned

BETA
PCA

Software Mentioned

LabVIEW
Mathworks
MIM
PCA
Matlab

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