Dual stream neural networks for brain signal classification.

Abstract:

:In this paper we propose a dual stream neural network (DSNN) for classifying arbitrary collections of functional neuroimaging signals for the purpose of brain computer interfaces (BCIs). In the DSNN the first stream is an end-to-end classifier taking raw time-dependent signals as input and generating feature identification signatures from them. The second stream enhances the identified features from the first stream by adjoining a dynamic functional connectivity matrix (DFCM) aimed at incorporating nuanced multi-channel information during specified BCI tasks. The network is tuned only once, so that fixed hyperparameters are determined for all subsequent data sets at the outset. The resulting DSNN is a subject-independent classifier that works for any collection of 1D functional neuroimaging signals, with the option of integrating domain specific information in the design. The DSNN classifier is benchmarked against three publicly available datasets, where the classifier demonstrates performance comparable to, or better than the state-of-art in each instance. Finally, an information theoretic examination of the trained network is performed, utilizing various tools, to demonstrate how to glean interpretive insight into how the hidden layers of the network parse the underlying biological signals.

journal_name

J Neural Eng

authors

Kuang D,Michoski C

doi

10.1088/1741-2552/abc903

subject

Has Abstract

pub_date

2020-11-10 00:00:00

eissn

1741-2560

issn

1741-2552

pub_type

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