  Multi-view learning improves the learning performance by utilizing multi-view
data: data collected from multiple sources, or feature sets extracted from the
same data source. This approach is suitable for primate brain state decoding
using cortical neural signals. This is because the complementary components of
simultaneously recorded neural signals, local field potentials (LFPs) and
action potentials (spikes), can be treated as two views. In this paper, we
extended broad learning system (BLS), a recently proposed wide neural network
architecture, from single-view learning to multi-view learning, and validated
its performance in decoding monkeys' oculomotor decision from medial frontal
LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in
non-human primate do contain complementary information about the oculomotor
decision, and that the proposed multi-view BLS is a more effective approach for
decoding the oculomotor decision than several classical and state-of-the-art
single-view and multi-view learning approaches.
