Multi-View Broad Learning System for Primate Oculomotor Decision Decoding

Abstract--Multi-view learning improves the learning performance by utilizing multi-view data: data collected from mul tiple 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 compleme ntary components of simultaneously recorded neural signals, loc al field potentials (LFPs) and action potentials (spikes), can be tr eated as two views. In this paper, we extended broad learning syste m (BLS), a recently proposed wide neural network architectur e, from single-view learning to multi-view learning, and vali dated its performance in monkey oculomotor decision decoding fro m medial frontal LFPs and spikes. We demonstrated that medial frontal LFPs and spikes in nonhuman primate do contain complementary information about the oculomotor decision, and that the proposed multi-view BLS is a more effective approac h to classify the oculomotor decision, than several classica l and state-of-the-art single-view and multi-view learning app roaches. UL TIview learning attempts to improve the learning performance by utilizing multi-view data, which can be collected from multiple data sources, or different featu re sets extracted from the same data source. For example, in an invasive brain-machine interface (BMI) using electrode s [1], effective BMI cursor control can be achieved using acti on potentials (spikes), which are high-pass filtered neural si gnals, or local field potentials (LFPs), which are low-pass filtered neural signals measured from the same electrodes. The spike s and LFPs can represent two views of the same task. There have been a few studies on applying multi-view learning to human brain state decoding. Kandemir et al. [2] combined multi-task learning and multi-view learning i n decoding a user's affective state, by treating different ty pes He and D. Wu are with the Key Laboratory of Im age Processing and Intelligent Control (Huazhong University o f Science and Technology), Ministry of Education.