Detection of error-related negativity in complex visual stimuli: a new neuroergonomic arrow in the practitioner's quiver.

Brain processes responsible for the error-related negativity (ERN) evoked response potential (ERP) have historically been studied in highly controlled laboratory experiments through presentation of simple visual stimuli. The present work describes the first time the ERN has been evoked and successfully detected in visual search of complex stimuli. A letter flanker task and a motorcycle conspicuity task were presented to participants during electroencephalographic (EEG) recording. Direct visual inspection and subsequent statistical analysis of the resultant time-locked ERP data clearly indicated that the ERN was detectable in both groups. Further, the ERN pattern did not differ between groups. Such results show that the ERN can be successfully elicited and detected in visual search of complex static images, opening the door to applied neuroergonomic use. Harnessing the brain's error detection system presents significant opportunities and complex challenges, and implication of such are discussed in the context of human-machine systems. Practitioner Summary: For the first time, error-related negativity (ERN) has been successfully elicited and detected in a visually complex applied search task. Brain-process-based error detection in human-machine systems presents unique challenges, but promises broad neuroergonomic applications.

Data mining in psychiatric research.

Mathematical sciences and computational methods have found new applications in fields like medicine over the last few decades. Modern data acquisition and data analysis protocols have been of great assistance to medical researchers and clinical scientists. Especially in psychiatry, technology and science have made new computational methods available to assist the development of predictive modeling and to identify diseases more accurately. Data mining (or knowledge discovery) aims to extract information from large datasets and solve challenging tasks, like patient assessment, early mental disease diagnosis, and drug efficacy assessment. Accurate and fast data analysis methods are very important, especially when dealing with severe psychiatric diseases like schizophrenia. In this paper, we focus on computational methods related to data analysis and more specifically to data mining. Then, we discuss some related research in the field of psychiatry.

Classification of cancer cell death with spectral dimensionality reduction and generalized eigenvalues.

OBJECTIVE: Accurate cell death discrimination is a time consuming and expensive process that can only be performed in biological laboratories. Nevertheless, it is very useful and arises in many biological and medical applications. METHODS AND MATERIAL: Raman spectra are collected for 84 samples of A549 cell line (human lung cancer epithelia cells) that has been exposed to toxins to simulate the necrotic and apoptotic death. The proposed data mining approach for the multiclass cell death discrimination problem uses a multiclass regularized generalized eigenvalue algorithm for classification (multiReGEC), together with a dimensionality reduction algorithm based on spectral clustering. RESULTS: The proposed algorithmic scheme can classify A549 lung cancer cells from three different classes (apoptotic death, necrotic death and control cells) with 97.78%± 0.047 accuracy versus 92.22 ± 0.095 without the proposed feature selection preprocessing. The spectrum areas depicted by the algorithm corresponds to the 〉C O bond from the lipids and the lipid bilayer. This chemical structure undergoes different change of state based on cell death type. Further evidence of the validity of the technique is obtained through the successful classification of 7 cell spectra that undergo hyperthermic treatment. CONCLUSIONS: In this study we propose a fast and automated way of processing Raman spectra for cell death discrimination, using a feature selection algorithm that not only enhances the classification accuracy, but also gives more insight in the undergoing cell death process.

Real-time differentiation of nonconvulsive status epilepticus from other encephalopathies using quantitative EEG analysis: a pilot study.

PURPOSE: Distinguishing nonconvulsive status epilepticus (NCSE) from some nonepileptic encephalopathies is a challenging problem. In many situations, NCSE and nonepileptic encephalopathies are indistinguishable by clinical symptoms and can produce very similar electroencephalography (EEG) patterns. Misdiagnosis or delay to diagnosis of NCSE may increase the rate of morbidity and mortality. METHODS: We developed a fast-differentiating algorithm using quantitative EEG analysis to distinguish NCSE patients from patients with toxic/metabolic encephalopathy (TME). EEG recordings were collected from 11 patients, including 6 with NCSE and 5 with TME. Three nonlinear dynamic measures were used in the proposed algorithm: the maximum short-term Lyapunov exponent (STLmax), phase of attractor (phase/angular frequency), and approximate entropy (ApEn). A further refined metric derived from STLmax and phase of attractor (the mean distance to EEG epoch samples from their centroid in the feature space) was also utilized as a criterion. Paired t tests were carried out to further clarify the separation between the EEG patterns of NCSE and TME. RESULTS: Computational results showed that the performance of the proposed algorithm was sufficient to distinguish NCSE from TME. The results were consistent in all subjects in our study. CONCLUSIONS: The study presents evidence that the maximum short-term Lyapunov exponents (STLmax) and phase of attractors (phase/angular frequency) can be useful in assisting clinical diagnosis of NCSE. Findings presented in this article provide a promising indication that the proposed algorithm may correctly distinguish NCSE from TME. Although the exact mechanism of this association remains unknown, the authors suggest that epileptic activity is highly associated with and can be modeled by dynamic systems.

A robust spike and wave algorithm for detecting seizures in a genetic absence seizure model.

Animal Models are used extensively in basic epilepsy research. In many studies, there is a need to accurately score and quantify all epileptic spike and wave discharges (SWDs) as captured by electroencephalographic (EEG) recordings. Manual scoring of long term EEG recordings is a time-consuming and tedious task that requires inordinate amount of time of laboratory personnel and an experienced electroencephalographer. In this paper, we adapt a SWD detection algorithm, originally proposed by the authors for absence (petit mal) seizure detection in humans, to detect SWDs appearing in EEG recordings of Fischer 334 rats. The algorithm is robust with respect to the threshold parameters. Results are compared to manual scoring and the effect of different threshold parameters is discussed.

Assessment of linear and nonlinear synchronization measures for analyzing EEG in a mild epileptic paradigm.

Epilepsy is one of the most common brain disorders and may result in brain dysfunction and cognitive disturbances. Epileptic seizures usually begin in childhood without being accommodated by brain damage and are tolerated by drugs that produce no brain dysfunction. In this study, cognitive function is evaluated in children with mild epileptic seizures controlled with common antiepileptic drugs. Under this prism, we propose a concise technical framework of combining and validating both linear and nonlinear methods to efficiently evaluate (in terms of synchronization) neurophysiological activity during a visual cognitive task consisting of fractal pattern observation. We investigate six measures of quantifying synchronous oscillatory activity based on different underlying assumptions. These measures include the coherence computed with the traditional formula and an alternative evaluation of it that relies on autoregressive models, an information theoretic measure known as minimum description length, a robust phase coupling measure known as phase-locking value, a reliable way of assessing generalized synchronization in state-space and an unbiased alternative called synchronization likelihood. Assessment is performed in three stages; initially, the nonlinear methods are validated on coupled nonlinear oscillators under increasing noise interference; second, surrogate data testing is performed to assess the possible nonlinear channel interdependencies of the acquired EEGs by comparing the synchronization indexes under the null hypothesis of stationary, linear dynamics; and finally, synchronization on the actual data is measured. The results on the actual data suggest that there is a significant difference between normal controls and epileptics, mostly apparent in occipital-parietal lobes during fractal observation tests.

Review on solving the inverse problem in EEG source analysis.

In this primer, we give a review of the inverse problem for EEG source localization. This is intended for the researchers new in the field to get insight in the state-of-the-art techniques used to find approximate solutions of the brain sources giving rise to a scalp potential recording. Furthermore, a review of the performance results of the different techniques is provided to compare these different inverse solutions. The authors also include the results of a Monte-Carlo analysis which they performed to compare four non parametric algorithms and hence contribute to what is presently recorded in the literature. An extensive list of references to the work of other researchers is also provided. This paper starts off with a mathematical description of the inverse problem and proceeds to discuss the two main categories of methods which were developed to solve the EEG inverse problem, mainly the non parametric and parametric methods. The main difference between the two is to whether a fixed number of dipoles is assumed a priori or not. Various techniques falling within these categories are described including minimum norm estimates and their generalizations, LORETA, sLORETA, VARETA, S-MAP, ST-MAP, Backus-Gilbert, LAURA, Shrinking LORETA FOCUSS (SLF), SSLOFO and ALF for non parametric methods and beamforming techniques, BESA, subspace techniques such as MUSIC and methods derived from it, FINES, simulated annealing and computational intelligence algorithms for parametric methods. From a review of the performance of these techniques as documented in the literature, one could conclude that in most cases the LORETA solution gives satisfactory results. In situations involving clusters of dipoles, higher resolution algorithms such as MUSIC or FINES are however preferred. Imposing reliable biophysical and psychological constraints, as done by LAURA has given superior results. The Monte-Carlo analysis performed, comparing WMN, LORETA, sLORETA and SLF, for different noise levels and different simulated source depths has shown that for single source localization, regularized sLORETA gives the best solution in terms of both localization error and ghost sources. Furthermore the computationally intensive solution given by SLF was not found to give any additional benefits under such simulated conditions.

Antiepileptic drug intervention decouples electroencephalogram (EEG) signals: a case study in Unverricht-Lundborg Disease.

Change in severity of myoclonus as an outcome measure of antiepileptic drug (AED) treatment in patients with Unverricht-Lundborg Disease (ULD) has been estimated by utilizing the Unified Myoclonus Rating Scale (UMRS). In this study, we measure treatment effects through EEG analysis using mutual information approach to quantify interdependence/coupling strength among different electrode sites. Mutual information is known to have the ability to capture linear and non-linear dependencies between EEG time series with superior performance over the traditional linear measures. One subject with ULD participated in this study and 1-hour EEG recordings were acquired before and after treatment of AED. Our results indicate that the mutual information is significantly lower after taking the add-on AED for four weeks at least. This finding could lead to a new insight for developing a new outcome measure for patient with ULD, when UMRS could potentially fail to detect a significant difference.