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Vigilance refers to the ability to maintain attention and to remain alert to stimuli in prolonged and monotonous tasks. Vigilance decrement describes the decline in performance in the course of such sustained attention tasks. Time-related alterations in attention have been found to be associated with changes in EEG. We investigated these time-on-task effects on the basis of changes in the conventional EEG spectral bands with the aim of finding a compound measure of vigilance. 148 healthy adults performed a cued Go/NoGo task that lasted approximately 21 min. Behavioural performance was examined by comparing the number of errors in the first and last quarters of the task using paired t-test. EEG data were epoched per trial, and time-on-task effects were modelled by using multiple linear regression, with frequency spectra band power values as independent variables and trial number as the dependent variable. Behavioural performance decreased in terms of omission errors only. Performance of the models, expressed by predicted R-squared, was between 0.10 and 0.27, depending on the particular task condition. The time-on-task EEG spectral changes were characterized by broad changes in the alpha and frontal changes in the beta and gamma bands. We were able to identify a set of EEG spectral features that predict time-on-task. Our output is considered to be a measure of vigilance, reflecting the allocation of mental resources for the maintenance of attention.

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Objectives: The electrophysiological characteristics of attention-deficit/hyperactivity disorder (ADHD) and recent machine-learning methods promise easy-to-use approaches that can complement existing diagnostic tools when sufficiently large samples are used. Neuroalgorithms are models of multidimensional brain networks by means of which ADHD patient data can be separated from healthy control data.Methods: Spontaneous electroencephalographic and event-related potential (ERP) data were collected three times over the course of 2 years from a multicentre sample of adults comprising 181 patients with ADHD and 147 healthy controls. Spectral power and ERP amplitude and latency measures were used as input data for a semi-automatic machine-learning framework.Results: ADHD patients and healthy controls could be classified with a sensitivity ranging from 75% to 83% and specificity values of 71% to 77%. In the analysis of the repeated measurements, sensitivity values of the selected logistic regression model remained high (72% and 76%), while specificity values slightly decreased over time (64% and 67%).Conclusions: Implementation of the system in clinical practice requires facilities to track affected networks, as well as expertise in neuropathophysiology. Therefore, the use of neuroalgorithms can enhance the diagnostic process by making it less subjective and more reliable and linking it to the underlying pathology.

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BACKGROUND: Event-related potentials have repeatedly revealed electrophysiological markers of cognitive dysfunction associated with Mild Traumatic Brain Injury (MTBI) and may represent a sensitive tool to guide cognitive rehabilitative interventions. We previously found patients with symptomatic MTBI characterized by smaller P300 (or P3) wave amplitudes in a NoGo-P3 subcomponent in the acute phase of the injury. The goal of this longitudinal study was to investigate whether this early NoGo-P3 subcomponent differs over time in symptomatic MTBI patients and healthy controls. METHODS: We included adults with a diagnosis of MTBI and individually matched healthy controls tested at 1 week, 3 months, and 1 year after the MTBI. Symptoms were assessed by the Rivermead Post-Concussion Symptoms Questionnaire. NoGo-P3 was collected by using a cued Go/NoGo task and the relevant subcomponent was extracted by independent component analysis. RESULTS: Among 53 adults with a diagnosis of MTBI and 53 controls, we included 35 with symptomatic MTBI and 35 matched healthy controls (18 females each group; mean age 34.06±13.15 and 34.26±12.98 years). Amplitudes for the early NoGo-P3 subcomponent were lower for symptomatic MTBI patients than controls (P<0.05) at 1 week post-injury. Furthermore, mixed ANOVA revealed a significant time by group interaction (P<0.05), so the effect of time differed for symptomatic MTBI patients and healthy controls. The amplitudes for MTBI patients normalized from 1 week to 3 months post-injury and were comparable to those of controls from 3 months to 1 year post-injury. However, amplitudes for 3 MTBI patients with particularly severe complaints 1 year post-injury did not normalize and were lower than those for the remaining MTBI sample (P<0.05). CONCLUSIONS: Selected event-related potentials can be used as a sensitive and objective tool to illustrate the cognitive consequences of and recovery after MTBI.

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Mild traumatic brain injuries (mTBI) generate acute disruptions of brain function and a subset of patients shows persisting cognitive, affective, and somatic symptoms. Deficits in the executive function domain are among the more frequent cognitive impairments reported by mTBI patients. By means of independent component analysis, event-related potential components from a visual cued go/nogo task, namely contingent negative variation (CNV) and NoGo-P3, were decomposed into distinct independent components that have been shown to be associated with the executive processes of energization, monitoring, and task setting. A group of symptomatic mTBI patients was compared with a group of controls matched for sex, age, and education. Patients showed reduced amplitudes in the late CNV as well as in the early NoGo-P3 subcomponents. Whereas the decreased CNVlate component indicates an impaired ability to generate representations of stimulus-response associations and to energize the maintenance of response patterns, the reduced P3NOGOearly component suggests a deficient ability to invest attentional effort in the initiation of response patterns in mTBI patients. Besides indicating the effects of mTBI on cognitive brain processing, the results may open up the possibility for assessing individual mTBI profiles and facilitate personalized rehabilitative measures.

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This article presents the design and a first pilot evaluation of the computer-based training program Calcularis for children with developmental dyscalculia (DD) or difficulties in learning mathematics. The program has been designed according to insights on the typical and atypical development of mathematical abilities. The learning process is supported through multimodal cues, which encode different properties of numbers. To offer optimal learning conditions, a user model completes the program and allows flexible adaptation to a child's individual learning and knowledge profile. Thirty-two children with difficulties in learning mathematics completed the 6-12-weeks computer training. The children played the game for 20 min per day for 5 days a week. The training effects were evaluated using neuropsychological tests. Generally, children benefited significantly from the training regarding number representation and arithmetic operations. Furthermore, children liked to play with the program and reported that the training improved their mathematical abilities.

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BACKGROUND: There are numerous event-related potential (ERP) studies in relation to attention-deficit hyperactivity disorder (ADHD), and a substantial number of ERP correlates of the disorder have been identified. However, most of the studies are limited to group differences in children. Independent component analysis (ICA) separates a set of mixed event-related potentials into a corresponding set of statistically independent source signals, which are likely to represent different functional processes. Using a support vector machine (SVM), a classification method originating from machine learning, this study aimed at investigating the use of such independent ERP components in differentiating adult ADHD patients from non-clinical controls by selecting a most informative feature set. A second aim was to validate the predictive power of the SVM classifier by means of an independent ADHD sample recruited at a different laboratory. METHODS: Two groups of age-matched adults (75 ADHD, 75 controls) performed a visual two stimulus go/no-go task. ERP responses were decomposed into independent components, and a selected set of independent ERP component features was used for SVM classification. RESULTS: Using a 10-fold cross-validation approach, classification accuracy was 91%. Predictive power of the SVM classifier was verified on the basis of the independent ADHD sample (17 ADHD patients), resulting in a classification accuracy of 94%. The latency and amplitude measures which in combination differentiated best between ADHD patients and non-clinical subjects primarily originated from independent components associated with inhibitory and other executive operations. CONCLUSIONS: This study shows that ERPs can substantially contribute to the diagnosis of ADHD when combined with up-to-date methods.

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Our spelling training software recodes words into multisensory representations comprising visual and auditory codes. These codes represent information about letters and syllables of a word. An enhanced version, developed for this study, contains an additional phonological code and an improved word selection controller relying on a phoneme-based student model. We investigated the spelling behavior of children by means of learning curves based on log-file data of the previous and the enhanced software version. First, we compared the learning progress of children with dyslexia working either with the previous software (n = 28) or the adapted version (n = 37). Second, we investigated the spelling behavior of children with dyslexia (n = 37) and matched children without dyslexia (n = 25). To gain deeper insight into which factors are relevant for acquiring spelling skills, we analyzed the influence of cognitive abilities, such as attention functions and verbal memory skills, on the learning behavior. All investigations of the learning process are based on learning curve analyses of the collected log-file data. The results evidenced that those children with dyslexia benefit significantly from the additional phonological cue and the corresponding phoneme-based student model. Actually, children with dyslexia improve their spelling skills to the same extent as children without dyslexia and were able to memorize phoneme to grapheme correspondence when given the correct support and adequate training. In addition, children with low attention functions benefit from the structured learning environment. Generally, our data showed that memory sources are supportive cognitive functions for acquiring spelling skills and for using the information cues of a multi-modal learning environment.

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BACKGROUND: In the context of sensory and cognitive-processing deficits in ADHD patients, there is considerable evidence of altered event related potentials (ERP). Most of the studies, however, were done on ADHD children. Using the independent component analysis (ICA) method, ERPs can be decomposed into functionally different components. Using the classification method of support vector machine, this study investigated whether features of independent ERP components can be used for discrimination of ADHD adults from healthy subjects. METHODS: Two groups of age- and sex-matched adults (74 ADHD, 74 controls) performed a visual two stimulus GO/NOGO task. ERP responses were decomposed into independent components by means of ICA. A feature selection algorithm defined a set of independent component features which was entered into a support vector machine. RESULTS: The feature set consisted of five latency measures in specific time windows, which were collected from four different independent components. The independent components involved were a novelty component, a sensory related and two executive function related components. Using a 10-fold cross-validation approach, classification accuracy was 92%. CONCLUSIONS: This study was a first attempt to classify ADHD adults by means of support vector machine which indicates that classification by means of non-linear methods is feasible in the context of clinical groups. Further, independent ERP components have been shown to provide features that can be used for characterizing clinical populations.