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Considering that the latency range of 250 - 500 ms of a given event-related potential suggests a shift in and focusing of attention, the current study was designed to determine the impact of interstimulus interval length on the resulting component (EDAN or CN-375). A paradigm introduced by Harter and his associates was employed. ERPs were recorded from eight scalp locations while participants processed visuospatial information. That is, once focused on a spatial direction cue (an arrow pointing either to the right or the left), the participant was required to lift his/her finger from a time-locked response key when a second stimulus, target stimulus, appeared in the field that was attended. The length of the interstimulus interval was modified to determine if the contralateral effect - that is, a greater negativity, for the given latency range, occurs over the hemisphere opposite the direction of attention -remained. Indeed, this effect emerged for 1S1 lengths of 600, 1000, and 1400 ms. The data revealed that the contralateral effect was inversely related Io ISI length, as predicted. Thai is, the greatest difference between the ipsilateral, compared to the contralateral, response occurred for the 600 ms ISI, next largest for the 1000 ms ISI, and least for the 1400 ms ISI (p < .01). Further analyses revealed that this effect was robust across all cortical regions from which the data were recorded. Despite the differential effects in amplitude as a function of arrow direction, latency remained consistent for the emergence of the CN-375. It was concluded that lhe constant latency, in the presence of the varying amplitude as a function of ISI, reflects an early processing stage in making the attentional adjustments required for maximal preparation to respond to a target stimulus. The accompanying scalp distribution reflects the fact that the attentional information contained in the arrow cue is processed at higher levels involving associational cortex across all cortex regions. [ABSTRACT FROM AUTHOR]

The P300 component is an event-related potential associated with the process of decision making. Its accurate detection in electroencephalographic signal is essential for P300-based brain-computer interfaces. This paper proposes a new method for the P300 detection based on discrete wavelet transform. Its benefits were tested with five healthy subjects using voting classification algorithm. The accuracy of 74.4% was achieved for single trials and over 90% accuracy for averaging. [ABSTRACT FROM PUBLISHER]

The paper presents a software for P300 potential acquisition integrated into system for measurement, acquisition and processing of event-related brain potential records. ERP (event-related potentials) are analyzed from point of view of its appearance and its significance in areas of cognitive neurosciences and neurophysiology. The software implements a maximum potential value detection method of detecting P300 component, but also enables user to mark the point of P300 component in averaged waveform. At first place, the presented software is focused on deviant and standard stimuli management, and accurate time management, because of specific requirements of the research project it is made for. The software is currently localized in Serbian language. [ABSTRACT FROM PUBLISHER]

This paper applies an expectation-maximization (EM) based Kalman smoother (KS) approach for single-trial event-related potential (ERP) estimation. Existing studies assume a Markov diffusion process for the dynamics of ERP parameters which is recursively estimated by optimal filtering approaches such as Kalman filter (KF). However, these studies only consider estimation of ERP state parameters while the model parameters are pre-specified using manual tuning, which is time-consuming for practical usage besides giving suboptimal estimates. We extend the KF approach by adding EM based maximum likelihood estimation of the model parameters to obtain more accurate ERP estimates automatically. We also introduce different model variants by allowing flexibility in the covariance structure of model noises. Optimal model selection is performed based on Akaike Information Criterion (AIC). The method is applied to estimation of chirp-evoked auditory brainstem responses (ABRs) for detection of wave V critical for assessment of hearing loss. Results shows that use of more complex covariances are better estimating inter-trial variability. [ABSTRACT FROM PUBLISHER]

In this work we present a comparative study of Gaussian process models for single-trial event-related potentials (ERPs) in electroencephalography (EEG) recordings. Our data comes from a motor task experiment where an ERP arises before the motor response of the participant to a stimulus. We consider models based on stationary and non-stationary kernel functions. The comparison is done based on two different criteria: model likelihood and model reaction time prediction. We show how models with high likelihoods do not necessarily perform well at predicting reaction time. The non-stationary kernel function achieved the best predictive performance. [ABSTRACT FROM PUBLISHER]

A brain-computer interface (BCI) P300 speller is a novel technique that helps people spell words using the electroencephalography (EEG) without the involvement of muscle activities. However, only time domain ERP features (P300) are used for controlling of the BCI speller. In this paper, we investigated the time-frequency EEG features for the P300-based brain-computer interface speller. A signal preprocessing method integrated ensemble average, principal component analysis, and independent component analysis to remove noise and artifacts in the EEG data. A time-frequency analysis based on wavelet transform was carried out to extract event-related spectral perturbation (ERSP) and inter-trial coherence (ITC) features. Results showed that the proposed signal processing method can effectively extract EEG time-frequency features in the P300 speller, suggesting that ERSP and ITC may be useful for improving the performance of BCI P300 speller. [ABSTRACT FROM PUBLISHER]

The paper presents a system for measurement and processing of event-related brain potential records. ERP (event-related potentials) are described from point of view of its appearance and its significance in areas of cognitive neurosciences and neurophysiology. The system hardware is of modular structure, enabling easier changes of its elements. In the current version, software for stimulation and data processing implements oddball paradigm, with special attention to management of time intervals. [ABSTRACT FROM PUBLISHER]

Event-related potentials in a wide sense (ERPWS) are bioelectric signals that appear associated to certain stimuli or event. The most common way to detect and estimate ERPWS is based on coherent averaging, but alternative techniques have been proposed to overcome certain limitations of the former. A flexible and realistic simulator is designed here to assess, in a controlled scenario, algorithms used to detect/estimate ERPWS. The system generates signals resembling real-world ERPWS, by adjusting waveforms, jitter, width and amplitude modulation, sampling/quantization effects, noise and interference. Having these simulated records, we can apply the algorithm under assessment, and easily estimate parameters as the variance and the bias of the remnant noise, as well as the signal-to-noise ratio. With those parameters as a reference, algorithms to detect/estimate ERPWS can be objectively compared. [ABSTRACT FROM AUTHOR]