Low-Rank Nonlinear Decoding of $μ$-ECoG from the Primary Auditory Cortex
This work addresses the problem of efficient neural decoding for brain-computer interfaces in neuroscience, though it is incremental as it builds on existing methods with a novel structural constraint.
The paper tackles the challenge of neural decoding from high-dimensional micro-electrocorticography data with limited training samples by introducing a low-rank neural network decoder, which significantly outperforms PCA-based models on auditory cortex data from awake rats.
This paper considers the problem of neural decoding from parallel neural measurements systems such as micro-electrocorticography ($μ$-ECoG). In systems with large numbers of array elements at very high sampling rates, the dimension of the raw measurement data may be large. Learning neural decoders for this high-dimensional data can be challenging, particularly when the number of training samples is limited. To address this challenge, this work presents a novel neural network decoder with a low-rank structure in the first hidden layer. The low-rank constraints dramatically reduce the number of parameters in the decoder while still enabling a rich class of nonlinear decoder maps. The low-rank decoder is illustrated on $μ$-ECoG data from the primary auditory cortex (A1) of awake rats. This decoding problem is particularly challenging due to the complexity of neural responses in the auditory cortex and the presence of confounding signals in awake animals. It is shown that the proposed low-rank decoder significantly outperforms models using standard dimensionality reduction techniques such as principal component analysis (PCA).