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PubMed1979

EEG patterns during 'cognitive' tasks. I. Methodology and analysis of complex behaviors.

This paper presents a methodology which uses nonlinear pattern recognition to study the spatial distribution of EEG patterns accompanying higher cortical functions. The multivariate decision rules reveal the essential EEG patterns which differentiate performance of two tasks. Cross-validation classification accuracy measures the generality of the findings. Using this method, EEG patterns were derived from a group of 23 adults during performance of several complex tasks, including Koh's block design, writing sentences, mental paper folding, and reading silently. These patterns discriminate between the tasks, are consistent with, and extend the results of, visual EEG interpretations and univariate analysis of spectral intensities. Since writing sentences could not be distinguished from mere scribbling, it is unclear whether the EEG patterns found to distinguish complex behaviors were related to the cognitive components of tasks, or to sensory-motor and performance-related factors.

Adult↗
PubMed1979

EEG patterns during 'cognitive' tasks. II. Analysis of controlled tasks.

This experiment was designed to distinguish possible EEG correlates of the cognitive components of tasks from EEG patterns associated with stimulus characteristics, limb and eye movements, and performance-related factors such as subjects' ability and effort. Thirty-two right-handed adults each performed 30 trials, lasting 6-15 sec each, of four simplified, controlled tasks: mental rotation of geometric forms, serial addition of a column of signed digits, substitution of letters with subsequent word recognition and visual fixation. The first three tasks could not be differentiated from each other. Each of these tasks could be differentiated from visual fixation by approximately 10% generalized reductions in alpha and beta band intensities, and slight increases in theta band intensities frontally and occipitally. We conclude that the EEG patterns which differentiated the complex tasks described in Part I were due to inter-task differences in stimulus characteristics, efferent activities and/or performance-related factors, rather than to cognitive differences. With these controls, no evidence for lateralization of different types of cognitive activity was found in the EEG.

Adult↗
PubMed2006

Beyond mind-reading: multi-voxel pattern analysis of fMRI data.

A key challenge for cognitive neuroscience is determining how mental representations map onto patterns of neural activity. Recently, researchers have started to address this question by applying sophisticated pattern-classification algorithms to distributed (multi-voxel) patterns of functional MRI data, with the goal of decoding the information that is represented in the subject's brain at a particular point in time. This multi-voxel pattern analysis (MVPA) approach has led to several impressive feats of mind reading. More importantly, MVPA methods constitute a useful new tool for advancing our understanding of neural information processing. We review how researchers are using MVPA methods to characterize neural coding and information processing in domains ranging from visual perception to memory search.

Algorithms↗
PubMed2003

Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts.

The biochemical mode-of-action (MOA) for herbicides and other bioactive compounds can be rapidly and simultaneously classified by automated pattern recognition of the metabonome that is embodied in the 1H NMR spectrum of a crude plant extract. The ca. 300 herbicides that are used in agriculture today affect less than 30 different biochemical pathways. In this report, 19 of the most interesting MOAs were automatically classified. Corn (Zea mays) plants were treated with various herbicides such as imazethapyr, glyphosate, sethoxydim, and diuron, which represent various biochemical modes-of-action such as inhibition of specific enzymes (acetohydroxy acid synthase [AHAS], protoporphyrin IX oxidase [PROTOX], 5-enolpyruvylshikimate-3-phosphate synthase [EPSPS], acetyl CoA carboxylase [ACC-ase], etc.), or protein complexes (photosystems I and II), or major biological process such as oxidative phosphorylation, auxin transport, microtubule growth, and mitosis. Crude isolates from the treated plants were subjected to 1H NMR spectroscopy, and the spectra were classified by artificial neural network analysis to discriminate the herbicide modes-of-action. We demonstrate the use and refinement of the method, and present cross-validated assignments for the metabolite NMR profiles of over 400 plant isolates. The MOA screen also recognizes when a new mode-of-action is present, which is considered extremely important for the herbicide discovery process, and can be used to study deviations in the metabolism of compounds from a chemical synthesis program. The combination of NMR metabolite profiling and neural network classification is expected to be similarly relevant to other metabonomic profiling applications, such as in drug discovery.

Herbicides↗
PubMedDate not supplied

Feature classes for 1D, 2nd order image structure arise from natural image maximum likelihood statistics.

Much is understood of how quantitative aspects of image structure are measured by VI simple cells, but less about how qualitative structure is determined from these measurements. We review Geometric Texton Theory (GTT) that aims to describe this step from quantitative to qualitative. GTT proposes that qualitative feature categories arise through consideration of the maximum likelihood (ML) explanations of image measurements. It posits that a pair of output vectors of an ensemble of co-localised neurons signal the same feature category if and only if the corresponding ML explanations are qualitatively similar. We present mathematical and empirical results relevant to GTT for the limited case of measurement by 1D filters of up to 2nd order. The mathematical results identify the simplest explanations for measurements by such filters, while the empirical results identify the ML. We find that the ML explanations are not the most simple under any of the definitions of simple that we examined. However, the ML explanations do have properties predicted by GTT. In particular they change rapidly and qualitatively for certain narrow regions of measurement space, while remaining qualitative stable between those transition regions. Three feature categories arise naturally from the data: light bars, dark bars and edges. The results are consistent with GTT.

Animals↗
PubMed2003

A test bed for insect-inspired robotic control.

Flying insects are remarkable examples of sophisticated sensory-motor control systems. Insects have solved the fundamental challenge facing the field of mobile robots: robust sensory-motor mapping. Control models based on insects can contribute much to the design of robotic control systems. We present our work on a preliminary robotic control system inspired by current behavioural and physiological models of the fruit fly, Drosophila melanogaster. We designed a five-degrees-of-freedom robotic system that serves as a novel simulation/mobile robot hybrid. This design has allowed us to implement a fly-inspired control system that uses visual and mechanosensory feedback. Our results suggest that a simple control scheme can yield surprisingly robust fly-like robotic behaviour.

Adaptation, Physiological↗
PubMed1997

A model of visual recognition and categorization.

To recognize a previously seen object, the visual system must overcome the variability in the object's appearance caused by factors such as illumination and pose. Developments in computer vision suggest that it may be possible to counter the influence of these factors, by learning to interpolate between stored views of the target object, taken under representative combinations of viewing conditions. Daily life situations, however, typically require categorization, rather than recognition, of objects. Due to the open-ended character of both natural and artificial categories, categorization cannot rely on interpolation between stored examples. Nonetheless, knowledge of several representative members, or prototypes, of each of the categories of interest can still provide the necessary computational substrate for the categorization of new instances. The resulting representational scheme based on similarities to prototypes appears to be computationally viable, and is readily mapped onto the mechanisms of biological vision revealed by recent psychophysical and physiological studies.

Animals↗
PubMed2006

Automated search for arthritic patterns in infrared spectra of synovial fluid using adaptive wavelets and fuzzy C-means analysis.

Analysis of synovial fluid by infrared (IR) clinical chemistry requires expert interpretation and is susceptible to subjective error. The application of automated pattern recognition (APR) may enhance the utility of IR analysis. Here, we describe an APR method based on the fuzzy C-means cluster adaptive wavelet (FCMC-AW) algorithm, which consists of two parts: one is a FCMC using the features from an M-band feature extractor adopting the adaptive wavelet algorithm and the second is a Bayesian classifier using the membership matrix generated by the FCMC. A FCMC-cross-validated quadratic probability measure (FCMC-CVQPM) criterion is used under the assumption that the class probability density is equal to the value of the membership matrix. Therefore, both values of posterior probabilities and selection criterion MFQ can be obtained through the membership matrix. The distinctive advantage of this method is that it provides not only the 'hard' classification of a new pattern, but also the confidence of this classification, which is reflected by the membership matrix.

Algorithms↗
PubMed2003

Hot spot detection based on feature space representation of visual search.

This paper presents a new framework for capturing intrinsic visual search behavior of different observers in image understanding by analysing saccadic eye movements in feature space. The method is based on the information theory for identifying salient image features based on which visual search is performed. We demonstrate how to obtain feature space fixation density functions that are normalized to the image content along the scan paths. This allows a reliable identification of salient image features that can be mapped back to spatial space for highlighting regions of interest and attention selection. A two-color conjunction search experiment has been implemented to illustrate the theoretical framework of the proposed method including feature selection, hot spot detection, and back-projection. The practical value of the method is demonstrated with computed tomography image of centrilobular emphysema, and we discuss how the proposed framework can be used as a basis for decision support in medical image understanding.

Adult↗
PubMed2006

Observer efficiency in discrimination tasks simulating malignant and benign breast lesions imaged with ultrasound.

We investigate and extend the ideal observer methodology developed by Smith and Wagner to detection and discrimination tasks related to breast sonography. We provide a numerical approach for evaluating the ideal observer acting on radio frequency (RF) frame data, which involves inversion of large nonstationary covariance matrices, and we describe a power-series approach to computing this inverse. Considering a truncated power series suggests that the RF data be Wiener-filtered before forming the final envelope image. We have compared human performance for Wiener-filtered and conventional B-mode envelope images using psychophysical studies for 5 tasks related to breast cancer classification. We find significant improvements in visual detection and discrimination efficiency in four of these five tasks. We also use the Smith-Wagner approach to distinguish between human and processing inefficiencies, and find that generally the principle limitation comes from the information lost in computing the final envelope image.

Algorithms↗
PubMed2006

The effect of nonlinear human visual system components on performance of a channelized Hotelling observer in structured backgrounds.

Linear model observers based on statistical decision theory have been used successfully to predict human visual detection of aperiodic signals in a variety of noisy backgrounds. However, some models have included nonlinearities such as a transducer or nonlinear decision rules to handle intrinsic uncertainty. In addition, masking models used to predict human visual detection of signals superimposed on one of two identical backgrounds (masks) usually include a number of nonlinear components in the channels that reflect properties of the firing of cells in the primary visual cortex (V1). The effect of these nonlinearities on the ability of linear model observers to predict human signal detection in real patient structured backgrounds is unknown. We evaluate the effect of including different nonlinear human visual system components into a linear channelized Hotelling observer (CHO) using a signal known exactly but variable (SKEV) task. In particular, we evaluate whether the rank order of two compression algorithms (JPEG versus JPEG 2000) and two compression encoder settings (JPEG 2000 default versus JPEG 2000 optimized) based on model observer signal detection performance in X-ray coronary angiograms is altered by inclusion of nonlinear components. The results show: 1) the simpler linear CHO model observer outperforms CHO model with the nonlinear components; 2) the rank order of model observer performance for the compression algorithms/parameters does not change when the nonlinear components are included. For the present task and images, the results suggest that the addition of the nonlinearities to a channelized Hotelling model may add complexity to the model observers without great impact on rank order evaluation of image processing and/or acquisition algorithms.

Algorithms↗
PubMed2006

Using human and model performance to compare MRI reconstructions.

Magnetic resonance imaging (MRI) reconstruction techniques are often validated with signal-to-noise ratio (SNR), contrast-to-noise ratio, and mean-to-standard-deviation ratio measured on example images. We present human and model observers as a novel approach to evaluating reconstructions for low-SNR magnetic resonance (MR) images. We measured human and channelized Hotelling observers in a two-alternative forced-choice signal-known-exactly detection task on synthetic MR images. We compared three reconstructions: magnitude, wavelet-based denoising, and phase-corrected real. Human observers performed approximately equally using all three reconstructions. The model observer showed very close agreement with the humans over the range of images. These results contradict previous predictions in the literature based on SNR. Thus, we propose that human observer studies are important for validating MRI reconstructions. The model's performance indicates that it may provide an alternative to human studies.

Artificial Intelligence↗
PubMed2006

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.

Algorithms↗
PubMed2006

Attention-based dynamic visual search using inner-scene similarity: algorithms and bounds.

A visual search is required when applying a recognition process on a scene containing multiple objects. In such cases, we would like to avoid an exhaustive sequential search. This work proposes a dynamic visual search framework based mainly on innerscene similarity. Given a number of candidates (e.g., subimages), we hypothesize is that more visually similar candidates are more likely to have the same identity. We use this assumption for determining the order of attention. Both deterministic and stochastic approaches, relying on this hypothesis, are considered. Under the deterministic approach, we suggest a measure similar to Kolmogorov's epsilon-covering that quantifies the difficulty of a search task. We show that this measure bounds the performance of all search algorithms and suggest a simple algorithm that meets this bound. Under the stochastic approach, we model the identity of the candidates as a set of correlated random variables and derive a search procedure based on linear estimation. Several experiments are presented in which the statistical characteristics, search algorithm, and bound are evaluated and verified.

Algorithms↗
PubMed2007

Robust object recognition with cortex-like mechanisms.

We introduce a new general framework for the recognition of complex visual scenes, which is motivated by biology: We describe a hierarchical system that closely follows the organization of visual cortex and builds an increasingly complex and invariant feature representation by alternating between a template matching and a maximum pooling operation. We demonstrate the strength of the approach on a range of recognition tasks: From invariant single object recognition in clutter to multiclass categorization problems and complex scene understanding tasks that rely on the recognition of both shape-based as well as texture-based objects. Given the biological constraints that the system had to satisfy, the approach performs surprisingly well: It has the capability of learning from only a few training examples and competes with state-of-the-art systems. We also discuss the existence of a universal, redundant dictionary of features that could handle the recognition of most object categories. In addition to its relevance for computer vision, the success of this approach suggests a plausibility proof for a class of feedforward models of object recognition in cortex.

Algorithms↗
PubMed2006

Qualitative visual environment retrieval.

A system for retrieval of an unstructured environment under static and dynamic scenarios is proposed. The use of cylindrical mosaics or omnidirectional images is exploited for providing a rich description about the surrounding environment spanning 360 degrees. The environment description is based on defining the attributes of the nodes of a graph derived from the angular partitions of the captured images. Content-based image retrieval for each of these partitions is performed on an exemplar image database to annotate the nodes of the graph. The complete environment description is recovered by collating the retrieval results over all the partitions based on a simple voting scheme. This offers a qualitative description of the location in a totally natural and unstructured surrounding. The experiments yield quite promising results.

Environment↗
PubMed1997

Invariant object recognition using higher-order neural networks, line-segment spectra and multi-resolution training.

A second-order neural network architecture is introduced that achieves invariant recognition with respect to an object's position and orientation in an image. This network does not show the combinatorial growth in network size as image size is increased, which is commonly observed in higher-order architectures. A new concept called an object's line-segment spectrum is introduced. It is argued that the weights of the second-order architecture are determined by these line-segments. Training time then becomes a function of object size rather than image size. The network is tested on the 26 capital letters of the alphabet. It is shown that in this application a multi-resolution training approach leads to reduced training time and improved performance.

Neural Networks, Computer↗