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EMG prediction from motor cortical recordings via a Non-Negative Point Process Filter
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Dr Kia Nazarpour
Nazarpour K, Ethier C, Paninski L, Rebesco JM, Miall RC, Miller LE
IEEE Transactions Biomedical Engineering
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A constrained point process filtering mechanism for prediction of electromyogram (EMG) signals from multichannel neural spike recordings is proposed here. Filters from theKalman family are inherently sub-optimal in dealing with non-Gaussian observations, or a state evolution that deviates from the Markovian setting assumption. To address these limitations, we modeled the non-Gaussian neural spike train observations by using a generalized linear model (GLM) that encapsulates covariates of neural activity, including the neurons’ own spiking history, concurrent ensemble activity, and extrinsic covariates(EMG signals). In order to predict the envelopes of EMGs, we reformulated the Kalman filter (KF) in an optimization framework and utilized a non-negativity constraint. This structurecharacterizes the non-linear correspondence between neural activity and EMG signals reasonably. The EMGs were recorded from twelve forearm and hand muscles of a behaving monkey during a grip-force task. For the case of limited training data, the constrained point process filter improved the prediction accuracy when compared to a conventional Wiener cascade filter (a linear causal filter followed by a static non-linearity). The approach was tested for different bin sizes and delays between input spikes and EMG output; 20 ms bin size and 40 ms delay provided the highest prediction rates. For longer training data sets, results of the proposed filter and that of the Wiener cascade filter were comparable.
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