A condition-independent framework for the classification of error-related brain activity.

The cognitive processing and detection of errors is important in the adaptation of the behavioral and learning processes. This brain activity is often reflected as distinct patterns of event-related potentials (ERPs) that can be employed in the detection and interpretation of the cerebral responses to erroneous stimuli. However, high-accuracy cross-condition classification is challenging due to the significant variations of the error-related ERP components (ErrPs) between complexity conditions, thus hindering the development of error recognition systems.

Discrete states of attention during active visual fixation revealed by Markovian analysis of the time series of intrusive saccades.

The frequency of intrusive saccades during maintenance of active visual fixation has been used as a measure of sustained visual attention in studies of healthy subjects as well as of neuropsychiatric patient populations. In this study, the mechanism that generates intrusive saccades during active visual fixation was investigated in a population of young healthy men performing three sustained fixation tasks (fixation to a visual target, fixation to a visual target with visual distracters, and fixation straight ahead in the dark). Markov Chain modeling of inter-saccade intervals (ISIs) was utilized.

Automatic identification of oculomotor behavior using pattern recognition techniques.

In this paper, a methodological scheme for identifying distinct patterns of oculomotor behavior such as saccades, microsaccades, blinks and fixations from time series of eye's angular displacement is presented. The first step of the proposed methodology involves signal detrending for artifacts removal and estimation of eye's angular velocity. Then, feature vectors from fourteen first-order statistical features are formed from each angular displacement and velocity signal using sliding, fixed-length time windows.

Amplitude normalization applied to an artificial neural network-based automatic sleep spindle detection system.

Sleep spindles are significant rhythmic transients present in the sleep electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. Automatic sleep spindle detection techniques are sought for the automation of sleep staging and the detailed study of sleep spindle patterns, of possible physiological significance. A deficiency of many of the available automatic detection techniques is their reliance on the amplitude level of the recorded EEG voltage values.

Performance evaluation of an Artificial Neural Network automatic spindle detection system.

Sleep spindles are transient waveforms found in the electroencephalogram (EEG) of non-rapid eye movement (NREM) sleep. Sleep spindles are used for the classification of sleep stages and have been studied in the context of various psychiatric and neurological disorders, such as Alzheimer's disease (AD) and the so-called Mild Cognitive Impairment (MCI), which is considered to be a transitional stage between normal aging and dementia. The visual processing of whole-night sleep EEG recordings is tedious.

Effect of frequency deviance direction on performance and mismatch negativity.

The mismatch negativity (MMN) component of the auditory event-related potential is associated with automatic perceptual inference concerning changes in auditory stimulation. Recent studies have addressed the question whether performance and MMN is affected by the direction of frequency deviance. In the present study, the frequency MMN and performance is investigated during an auditory identification task.

Classification of Error-Related Negativity (ERN) and Positivity (Pe) potentials using kNN and Support Vector Machines.

Error processing in subjects performing actions has been associated with the Event-Related Potential (ERP) components called Error-Related Negativity (ERN) and Error Positivity (Pe). In this paper, features based on statistical measures of the sample of averaged ERP recordings are used for classifying correct from incorrect actions. Three feature selection techniques were used and compared.

Mismatch task conditions and error related ERPs.

Background: The N200 component of event related potentials (ERPs) is considered an index of monitoring error related responses. The aim of the present work was to study the effect of mismatch conditions on the subjects' responses in an auditory identification task and their relation to the N200 of stimulus-locked ERPs.

Methods: An auditory identification task required to correctly map a horizontal slider onto an active frequency range by selecting a slider position that matched the stimulus tone in each trial.

Classification of event-related potentials associated with response errors in actors and observers based on autoregressive modeling.

Event-Related Potentials (ERPs) provide non-invasive measurements of the electrical activity on the scalp related to the processing of stimuli and preparation of responses by the brain. In this paper an ERP-signal classification method is proposed for discriminating between ERPs of correct and incorrect responses of actors and of observers seeing an actor making such responses. The classification method targeted signals containing error-related negativity (ERN) and error positivity (Pe) components, which are typically associated with error processing in the human brain.

Behavioral and brain pattern differences between acting and observing in an auditory task.

Background: Recent research has shown that errors seem to influence the patterns of brain activity. Additionally current notions support the idea that similar brain mechanisms are activated during acting and observing. The aim of the present study was to examine the patterns of brain activity of actors and observers elicited upon receiving feedback information of the actor's response.

Independent components of sleep spindles.

Sleep spindles are considered a hallmark of stage 2 of the sleep electroencephalogram (EEG) and are used both for sleep staging and for clinical studies of pharmacological agents. Analyses of sleep spindle topography, as well as intracranial source investigations provided evidence for the existence of two distinct sleep spindle types, "slow" and "fast" spindles at approximately 12 and 14 Hz, respectively. The aim of the present study was to apply Independent Component Analysis (ICA) to sleep spindles, for examining the possibility of extracting, through visual analysis of the spindle EEG and selection of Independent Components (ICs), spindle "components" corresponding to separate EEG activity patterns, and to investigate the sources underlying these spindle components.

Sleep spindle detection using artificial neural networks trained with filtered time-domain EEG: a feasibility study.

An artificial neural network (ANN) based on the Multi-Layer Perceptron (MLP) architecture is used for detecting sleep spindles in band-pass filtered electroencephalograms (EEG), without feature extraction. Following optimum classification schemes, the sensitivity of the network ranges from 79.2% to 87.

Long-term abstinence syndrome in heroin addicts: indices of P300 alterations associated with a short memory task.

Attentional deficits have been implicated in the pathophysiology of opioid addicts. The P300 component of event-related potentials (ERPs) is considered as a manifestation of attentional operations. The authors' goal was the comparison of P300 elicited during a short memory test between subjects with prolonged heroin abstinence and current heroin users as well as healthy controls.