Single-trial bistable perception classification based on sparse nonnegative tensor decomposition

The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper we apply a sparse nonnegative tensor factorization (NTF) based method to extract features from the local field potential (LFF) in monkey visual cortex for decoding its bistable structure-from-motion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequency representation of intracortical LFP data collected from the middle temporal area (MT) in a macaque monkey performing a SFM task. The advantages of the sparse NTF based feature extraction approach lies in its capability to yield components common across the space, time and frequency domains and at the same time discriminative across different conditions without prior knowledge of the discriminative frequency bands and temporal windows for a specific subject. We employ the support vector machines (SVM) classifier based on the features of the NTF components to decode the reported perception on a single-trial basis. Our results suggest that although other bands also have certain discriminability, the gamma band feature carries the most discriminative information for bistable perception, and that imposing the sparseness constraints on the nonnegative tensor factorization improves extraction of this feature.