Paul Sajda

Neural Representation of Task Difficulty and Decision Making during Perceptual Categorization: A Timing Diagram

When does the brain know that a decision is difficult to make? How does decision difficulty affect the allocation of neural resources and timing of constituent cortical processing? Here, we use single-trial analysis of electroencephalography (EEG) to identify neural correlates of decision difficulty and relate these to neural correlates of decision accuracy. Using a cued paradigm, we show that we can identify a component in the EEG that reflects the inherent task difficulty and not simply a correlation with the stimulus. We find that this decision difficulty component arises ≈220 ms after stimulus presentation, between two EEG components that are predictive of decision accuracy [an “early” (170 ms) and a “late” (≈300 ms) component]. We use these results to develop a timing diagram for perceptual decision making and relate the component activities to parameters of a diffusion model for decision making.

Cortically coupled computer vision for rapid image search

We describe a real-time electroencephalography (EEG)-based brain-computer interface system for triaging imagery presented using rapid serial visual presentation. A target image in a sequence of nontarget distractor images elicits in the EEG a stereotypical spatiotemporal response, which can be detected. A pattern classifier uses this response to reprioritize the image sequence, placing detected targets in the front of an image stack. We use single-trial analysis based on linear discrimination to recover spatial components that reflect differences in EEG activity evoked by target versus nontarget images. We find an optimal set of spatial weights for 59 EEG sensors within a sliding 50-ms time window. Using this simple classifier allows us to process EEG in real time. The detection accuracy across five subjects is on average 92%, i.e., in a sequence of 2500 images, resorting images based on detector output results in 92% of target images being moved from a random position in the sequence to one of the first 250 images (first 10% of the sequence). The approach leverages the highly robust and invariant object recognition capabilities of the human visual system, using single-trial EEG analysis to efficiently detect neural signatures correlated with the recognition event.

Linear Spatial Integration for Single-Trial Detection in Encephalography

Conventional analysis of electroencephalography (EEG) and magnetoencephalography (MEG) often relies on averaging over multiple trials to extract statistically relevant differences between two or more experimental conditions. In this article we demonstrate single-trial detection by linearly integrating information over multiple spatially distributed sensors within a predefined time window. We report an average, single-trial discrimination performance of Az approximately 0.80 and faction correct between 0.70 and 0.80, across three distinct encephalographic data sets. We restrict our approach to linear integration, as it allows the computation of a spatial distribution of the discriminating component activity. In the present set of experiments the resulting component activity distributions are shown to correspond to the functional neuroanatomy consistent with the task (e.g., contralateral sensorymotor cortex and anterior cingulate). Our work demonstrates how a purely data-driven method for learning an optimal spatial weighting of encephalographic activity can be validated against the functional neuroanatomy.

Quality of evidence for perceptual decision making is indexed by trial-to-trial variability of the EEG

A fundamental feature of how we make decisions is that our responses are variable in the choices we make and the time it takes to make them. This makes it impossible to determine, for a single trial of an experiment, the quality of the evidence on which a decision is based. Even for stimuli from a single experimental condition, it is likely that stimulus and encoding differences lead to differences in the quality of evidence. In the research reported here, with a simple “face”/“car” perceptual discrimination task, we obtained late (decision-related) and early (stimulus-related) single-trial EEG component amplitudes that discriminated between faces and cars within and across conditions. We used the values of these amplitudes to sort the response time and choice within each experimental condition into more-face-like and less-face-like groups and then fit the diffusion model for simple decision making (a well-established model in cognitive psychology) to the data in each group separately. The results show that dividing the data on a trial-by-trial basis by using the late-component amplitude produces differences in the estimates of evidence used in the decision process. However, dividing the data on the basis of the early EEG component amplitude or the times of the peak amplitudes of either component did not index the information used in the decision process. The results we present show that a single-trial EEG neurophysiological measure for nominally identical stimuli can be used to sort behavioral response times and choices into those that index the quality of decision-relevant evidence.

Nonnegative matrix factorization for rapid recovery of constituent spectra in magnetic resonance chemical shift imaging of the brain

We present an algorithm for blindly recovering constituent source spectra from magnetic resonance (MR) chemical shift imaging (CSI) of the human brain. The algorithm, which we call constrained nonnegative matrix factorization (cNMF), does not enforce independence or sparsity, instead only requiring the source and mixing matrices to be nonnegative. It is based on the nonnegative matrix factorization (NMF) algorithm, extending it to include a constraint on the positivity of the amplitudes of the recovered spectra. This constraint enables recovery of physically meaningful spectra even in the presence of noise that causes a significant number of the observation amplitudes to be negative. We demonstrate and characterize the algorithms performance using /sup 31/P volumetric brain data, comparing the results with two different blind source separation methods: Bayesian spectral decomposition (BSD) and nonnegative sparse coding (NNSC). We then incorporate the cNMF algorithm into a hierarchical decomposition framework, showing that it can be used to recover tissue-specific spectra given a processing hierarchy that proceeds coarse-to-fine. We demonstrate the hierarchical procedure on /sup 1/H brain data and conclude that the computational efficiency of the algorithm makes it well-suited for use in diagnostic work-up.

Complete Volumetric Decomposition of Individual Trabecular Plates and Rods and Its Morphological Correlations With Anisotropic Elastic Moduli in Human Trabecular Bone

Trabecular plates and rods are important microarchitectural features in determining mechanical properties of trabecular bone. A complete volumetric decomposition of individual trabecular plates and rods was used to assess the orientation and morphology of 71 human trabecular bone samples. The ITS‐based morphological analyses better characterize microarchitecture and help predict anisotropic mechanical properties of trabecular bone.

Quantification of the roles of trabecular microarchitecture and trabecular type in determining the elastic modulus of human trabecular bone.

The roles of microarchitecture and types of trabeculae in determining elastic modulus of trabecular bone have been studied in μCT images of 29 trabecular bone samples by comparing their Youngs moduli calculated by finite element analysis (FEA) with different trabecular type‐specific reconstructions. The results suggest that trabecular plates play an essential role in determining elastic properties of trabecular bone.

EEG-Informed fMRI Reveals Spatiotemporal Characteristics of Perceptual Decision Making

Single-unit and multiunit recordings in primates have already established that decision making involves at least two general stages of neural processing: representation of evidence from early sensory areas and accumulation of evidence to a decision threshold from decision-related regions. However, the relay of information from early sensory to decision areas, such that the accumulation process is instigated, is not well understood. Using a cued paradigm and single-trial analysis of electroencephalography (EEG), we previously reported on temporally specific components related to perceptual decision making. Here, we use information derived from our previous EEG recordings to inform the analysis of fMRI data collected for the same behavioral task to ascertain the cortical origins of each of these EEG components. We demonstrate that a cascade of events associated with perceptual decision making takes place in a highly distributed neural network. Of particular importance is an activation in the lateral occipital complex implicating perceptual persistence as a mechanism by which object decision making in the human brain is instigated.

Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring

We describe a brain-computer interface (BCI) system, which uses a set of adaptive linear preprocessing and classification algorithms for single-trial detection of error related negativity (ERN). We use the detected ERN as an estimate of a subjects perceived error during an alternative forced choice visual discrimination task. The detected ERN is used to correct subject errors. Our initial results show average improvement in subject performance of 21% when errors are automatically corrected via the BCI. We are currently investigating the generalization of the overall approach to other tasks and stimulus paradigms.

Intermediate-level visual representations and the construction of surface perception

Visual processing has often been divided into three stagesearly, intermediate, and high level vision, which roughly correspond to the sensation, perception, and cognition of the visual world. In this paper, we present a network-based model of intermediate-level vision that focuses on how surfaces might be represented in visual cortex. We propose a mechanism for representing surfaces through the establishment of ownershipa selective binding of contours and regions. The representation of ownership provides a central locus for visual integration. Our simulations show the ability to segment real and illusory images in a manner consistent with human perception. In addition, through ownership, other processes such as depth, transparency, and surface completion can interact with one another to organize an image into a perceptual scene.