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Abstract:
Bistable perception arises when a stimulus under continuous view is perceived as the alternation of two mutually exclusive states. Such a stimulus provides a unique opportunity for understanding the neural basis of visual perception because it dissociates the perception from the visual input. In this paper we focus on extracting the percept-related features of the induced activity from the local field potential (LFP) in monkey visual cortex for decoding its bistable structure-from-motion (SFM) perception. Because of the dissociation between the perception and the stimulus in our experimental paradigm, the stimulus-evoked activity in our data is not related to perception. Our proposed feature extraction approach consists of two stages. First, we estimate the stimulus-evoked activity via a wavelet transform based method and remove it from the single trials of each channel. Second, we use the common spatial patterns (CSP) approach to design spatial filters based on the remaining induced activity of multiple channels to extract the percept-related features. We exploit the linear discriminant analysis (LDA) classifier and the support vector machine (SVM) classifier on the extracted features to decode the reported perception on a single-trial basis. We apply the proposed approach to the multichannel intracortical LFP data collected from the middle temporal (MT) visual cortex in a macaque monkey performing a SFM task. We demonstrate that our approach is effective in extracting the discriminative features of the percept-related induced activity from LFP, which leads to excellent decoding performance. We also discover that the enhanced gamma band synchronization and reduced alpha band desynchronization may be the underpinnings of the induced activity.
