PT  - JOURNAL ARTICLE
AU  - Shoeb Shaikh
AU  - Rosa So
AU  - Tafadzwa Sibindi
AU  - Camilo Libedinsky
AU  - Arindam Basu
TI  - Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs
AID  - 10.1101/834028
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 834028
4099  - http://biorxiv.org/content/early/2019/11/07/834028.short
4100  - http://biorxiv.org/content/early/2019/11/07/834028.full
AB  - This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracortical Brain Machine Interfaces (iBMIs) in the face of non-stationary distribution of input neural data across time. Each classifier in the ensemble is trained on a randomly sampled (with replacement) set of input channels. These sparse connections ensure that with a high chance, few of the base classifiers should be less affected by the variations in some of the recording channels. We have tested the generality of this technique on different base classifiers - linear discriminant analysis (LDA), support vector machine (SVM), extreme learning machine (ELM) and multilayer perceptron (MLP). Results show decoding accuracy improvements of up to ≈ 21%, 13%, 19%, 10% in non-human primate (NHP) A and 7%, 9%, 7%, 9% in NHP B across test days while using the sparse ensemble approach over a single classifier model for LDA, SVM, ELM and MLP algorithms respectively. The technique also holds ground when the most informative electrode on the test day is dropped. Accordingly, improvements of up to ≈ 24%, 11%, 22%, 9% in NHP A and 14%, 19%, 7%, 28% in NHP B are obtained for LDA, SVM, ELM and MLP respectively.