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Sparse Ensemble Machine Learning to improve robustness of long-term decoding in iBMIs
This paper presents a novel sparse ensemble based machine learning approach to enhance robustness of intracorticalBrain 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 havedoi:10.1101/834028 fatcat:b7c6yu5yhbag7agnwtio6whhry