Publications [#357013] of Vahid Tarokh

Papers Published

1. Angjelichinoski, M; Soltani, M; Choi, J; Pesaran, B; Tarokh, V, Deep Pinsker and James-Stein Neural Networks for Decoding Motor Intentions From Limited Data., Ieee Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society, vol. 29 (January, 2021), pp. 1058-1067 [doi]
   (last updated on 2023/06/01)

   Abstract:
   Non-parametric regression has been shown to be useful in extracting relevant features from Local Field Potential (LFP) signals for decoding motor intentions. Yet, in many instances, brain-computer interfaces (BCIs) rely on simple classification methods, circumventing deep neural networks (DNNs) due to limited training data. This paper leverages the robustness of several important results in non-parametric regression to harness the potentials of deep learning in limited data setups. We consider a solution that combines Pinsker's theorem as well as its adaptively optimal counterpart due to James-Stein for feature extraction from LFPs, followed by a DNN for classifying motor intentions. We apply our approach to the problem of decoding eye movement intentions from LFPs collected in macaque cortex while the animals perform memory-guided visual saccades to one of eight target locations. The results demonstrate that a DNN classifier trained over the Pinsker features outperforms the benchmark method based on linear discriminant analysis (LDA) trained over the same features.