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Extraction of Bistable-Percept-Related Features IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 56, NO. 8, AUGUST 2009 2095 Extraction of Bistable-Percept-Related Features From Local Field Potential by Integration of Local Regression and Common Spatial Patterns Zhisong Wang, Member, IEEE, Alexander Maier, Nikos K. Logothetis, and Hualou Liang∗, Senior Member, IEEE Abstract—Bistable perception arises when an ambiguous stim- using bistable stimuli. Bistable perception refers to the phe- ulus under continuous view is perceived as an alternation of two nomenon of spontaneously alternating percepts while viewing mutually exclusive states. Such a stimulus provides a unique op- the same stimulus continuously. The study of bistable perception portunity for understanding the neural basis of visual perception because it dissociates the perception from the visual input. In this holds great promise for understanding the neural correlates of vi- paper, we focus on extracting the percept-related features from the sual perception [1]. The local field potential (LFP) is thought to local field potential (LFP) in monkey visual cortex for decoding its predominantly reflect the synaptic activities of local populations bistable structure-from-motion (SFM) perception. Our proposed of neurons [2] and has recently received increasing attention in feature extraction approach consists of two stages. First, we es- the analysis of the neuronal population activity [3], [4]. The in- timate and remove from each LFP trial the nonpercept-related stimulus-evoked activity via a local regression method called the vestigation of correlations between perceptual reports and LFP locally weighted scatterplot smoothing because of the dissocia- oscillations during physically identical but perceptually differ- tion between the perception and the stimulus in our experimental ent conditions may provide new insights on the mechanism of paradigm. Second, we use the common spatial patterns approach neural information processing and the neural basis of visual to design spatial filters based on the residue signals of multiple perception and perceptual decision-making. channels to extract the percept-related features. We exploit a sup- port vector machine (SVM) classifier on the extracted features to The LFP signal recorded during each trial is composed of decode the reported perception on a single-trial basis. We apply the percept-related and nonpercept-related neuronal activity, among proposed approach to the multichannel intracortical LFP data col- which the stimulus-evoked activity is often the strongest, over- lected from the middle temporal (MT) visual cortex in a macaque shadowing the other activity such as the induced activity and monkey performing an SFM task. We demonstrate that our ap- noise. However, in our data, the stimulus-evoked activity is not proach is effective in extracting the discriminative features of the percept-related activity from LFP and achieves excellent decoding related to perception due to the dissociation between the per- performance. We also find that the enhanced gamma band syn- ception and the stimulus in our experimental paradigm. There- chronization and reduced alpha and beta band desynchronization fore, we first need to estimate and remove the stimulus-evoked may be the underpinnings of the percept-related activity. activity from each LFP trial to facilitate the extraction of the Index Terms—Common spatial patterns (CSPs), event-related percept-related features from the remaining signal for single- synchronization and desynchronization, feature extraction, local trial decoding of bistable perception. field potential (LFP), local regression, locally weighted scatter- Ensemble averaging is a widely used approach for estimating plot smoothing (LOWESS), nonstationary time series, single trial, stimulus-evoked activity. It is performed by averaging across tri- stimulus-evoked activity, structure-from-motion (SFM), support vector machine (SVM). als the potentials measured over repeated presentations of a stim- ulus. However, ensemble averaging generally requires a large number of trials to achieve satisfactory results. This requires a I. INTRODUCTION long time for data acquisition, which may also cause nuisance HE QUESTION of how perception arises from neural ac- for the subjects. In addition, averaging assumes that there are no T tivity in the visual cortex is of central importance to cogni- variations in amplitude, latency, or waveform of the stimulus- tive neuroscience. To answer this question, one important exper- evoked potential across subsequent trials. This assumption may imental paradigm is to dissociate percepts from the visual input not be valid in practice [5]. Whenever it is violated, important information regarding the dynamics of the cognitive process un- Manuscript received August 27, 2008; revised December 22, 2008 and der study is lost. Time-varying adaptive filters [6] such as the February 3, 2009. First published April 7, 2009; current version published least mean square (LMS) and recursive least squares (RLS), and July 15, 2009. This work was supported by the National Institutes of Health Wiener filter can also be used to estimate the stimulus-evoked under Grant NIH R01 MH072034 and by the Max Planck Society. Asterisk indicates corresponding author. activity. However, all of these filters require a reference sig- Z. Wang is with the Microsoft Corporation, Bellevue, WA 98007 USA. nal, which is typically calculated as the ensemble average of all A. Maier is with the National Institutes of Health, Bethesda, MD 20892 USA. trials except the trial being considered. Therefore, they suffer N. K. Logothetis is with the Max Planck Institute for Biological Cybernetics, Tubingen,¨ Germany (e-mail: [email protected]). from the trial-by-trial variability of the stimulus-evoked activity ∗H. Liang is with the School of Biomedical Engineering, Drexel University, since they rely on averaging multiple trials to obtain a good Philadelphia, PA 19104 USA (e-mail: [email protected]). reference signal for each single trial. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. In this paper, we consider a local regression method called Digital Object Identifier 10.1109/TBME.2009.2018630 locally weighted scatterplot smoothing (LOWESS) [7]–[9] to 0018-9294/$25.00 © 2009 IEEE Authorized licensed use limited to: Drexel University. Downloaded on July 15, 2009 at 08:16 from IEEE Xplore. Restrictions apply. 2096 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 56, NO. 8, AUGUST 2009 estimate the stimulus-evoked activity from each single LFP trial. the application of the feature extraction approach for decoding LOWESS has gained widespread acceptance in statistics as an the bistable SFM perception. Finally, Section 4 contains the appealing solution for fitting smooth curves to noisy data. The conclusions. advantage of LOWESS lies in the fact that it does not require the specification of a global function to fit all the data samples, II. MATERIALS AND METHODS but only demands the setting of several parameters to fit the data locally. Consequently, it greatly simplifies the estimation of the A. Subjects and Neurophysiological Recordings complex processes such as the stimulus-evoked activity. In addi- Electrophysiological recordings were performed in a healthy tion, the LOWESS-based approach does not require a reference adult male rhesus monkey. After behavioral training was com- signal and is robust against trial-by-trial variability. After the plete, an occipital recording chamber was implanted and a cran- removal of the stimulus-evoked activity based on LOWESS, we iotomy was made. Intracortical recordings were conducted with propose to use the common spatial patterns (CSP) method to a multielectrode array while the monkey was viewing SFM stim- extract the percept-related features from the remaining signals uli, which consisted of an orthographic projection of a transpar- of multiple channels. CSP finds spatial filters that maximize the ent sphere that was covered with randomly distributed dots on its variance for one class and simultaneously minimize the vari- entire surface. SFM is the perception of 3-D shape from motion ance for the other class. It has become a popular feature extrac- cues. When in motion, this stimulus gives the striking percept tion approach in EEG-based brain–computer interface (BCI) of a 3-D sphere spinning in one of the two possible directions: applications [10]–[12]. There are many feature extraction ap- clockwise or counterclockwise. proaches including principal component analysis (PCA), inde- Trials were initiated with a short period of fixation (300– pendent component analysis (ICA), empirical mode decompo- 500 ms), followed by the presentation of the SFM stimulus sition (EMD), and the time-embedding techniques. PCA and for 2000–3000 ms with an average interstimulus-interval of ICA generate multiple components for each trial from multiple 2000 ms. The stimulus rotated for the entire period of presenta- channels. EMD generates multiple components for each trial tion, giving the appearance of a 3-D structure. The monkey was from each channel. The time-embedding techniques transform well-trained and required to indicate the choice of rotation di- 1-D time series of the sensor signals into a high-dimensional rection (clockwise or counterclockwise) by pushing one of two space of time-delayed time series to make subspace projection levers. The stimuli in the task were randomly intermixed with possible, but by doing
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