TY - GEN
T1 - Percept-related cortical induced activity during bistable perception
AU - Wang, Zhisong
AU - Logothetis, Nikos K.
AU - Liang, Hualou
PY - 2009/7/31
Y1 - 2009/7/31
N2 - 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-frommotion (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.
AB - 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-frommotion (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.
UR - https://www.scopus.com/pages/publications/70449440453
U2 - 10.1109/IJCNN.2009.5178612
DO - 10.1109/IJCNN.2009.5178612
M3 - Conference article published in proceeding or book
AN - SCOPUS:70449440453
SN - 9781424435531
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 3289
EP - 3296
BT - 2009 International Joint Conference on Neural Networks, IJCNN 2009
PB - IEEE
T2 - 2009 International Joint Conference on Neural Networks, IJCNN 2009
Y2 - 14 June 2009 through 19 June 2009
ER -