Analysis of pattern reversal visual evoked potentials (PRVEP's) by spline wavelets

Reprint of "A new approach to analyze data from EEG-based concealed face recognition system"

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

A new approach to analyze data from EEG-based concealed face recognition system

The purpose of this study is to extend a feature set with non-linear features to improve classification rate of guilty and innocent subjects. Non-linear features can provide extra information about phase space. The Event-Related Potential (ERP) signals were recorded from 49 subjects who participated in concealed face recognition test. For feature extraction, at first, several morphological characteristics, frequency bands, and wavelet coefficients (we call them basic-features) are extracted from each single-trial ERP. Recurrence Quantification Analysis (RQA) measures are then computed as non-linear features from each single-trial. We apply Genetic Algorithm (GA) to select the best feature set and this feature set is used for classification of data using Linear Discriminant Analysis (LDA) classifier. Next, we use a new approach to improve classification results based on introducing an adaptive-threshold. Results indicate that our method is able to correctly detect 91.83% of subjects (45 correct detection of 49 subjects) using combination of basic and non-linear features, that is higher than 87.75% for basic and 79.59% for non-linear features. This shows that combination of non-linear and basic- features could improve classification rate.

A novel algorithm to enhance P300 in single trials: application to lie detection using F-score and SVM

The investigation of lie detection methods based on P300 potentials has drawn much interest in recent years. We presented a novel algorithm to enhance signal-to-noise ratio (SNR) of P300 and applied it in lie detection to increase the classification accuracy. Thirty-four subjects were divided randomly into guilty and innocent groups, and the EEG signals on 14 electrodes were recorded. A novel spatial denoising algorithm (SDA) was proposed to reconstruct the P300 with a high SNR based on independent component analysis. The differences between the proposed method and our/other early published methods mainly lie in the extraction and feature selection method of P300. Three groups of features were extracted from the denoised waves; then, the optimal features were selected by the F-score method. Selected feature samples were finally fed into three classical classifiers to make a performance comparison. The optimal parameter values in the SDA and the classifiers were tuned using a grid-searching training procedure with cross-validation. The support vector machine (SVM) approach was adopted to combine with an F-score because this approach had the best performance. The presented model F-score_SVM reaches a significantly higher classification accuracy for P300 (specificity of 96.05%) and non-P300 (sensitivity of 96.11%) compared with the results obtained without using SDA and compared with the results obtained by other classification models. Moreover, a higher individual diagnosis rate can be obtained compared with previous methods, and the presented method requires only a small number of stimuli in the real testing application.

A novel concealed information test method based on independent component analysis and support vector machine

The concealed information test (CIT) has drawn much attention and has been widely investigated in recent years. In this study, a novel CIT method based on denoised P3 and machine learning was proposed to improve the accuracy of lie detection. Thirty participants were chosen as the guilty and innocent participants to perform the paradigms of 3 types of stimuli. The electroencephalogram (EEG) signals were recorded and separated into many single trials. In order to enhance the signal noise ratio (SNR) of P3 components, the independent component analysis (ICA) method was adopted to separate non-P3 components (i.e., artifacts) from every single trial. In order to automatically identify the P3 independent components (ICs), a new method based on topography template was proposed to automatically identify the P3 ICs. Then the P3 waveforms with high SNR were reconstructed on Pz electrodes. Second, the 3 groups of features based on time,frequency, and wavelets were extracted from the reconstructed P3 waveforms. Finally, 2 classes of feature samples were used to train a support vector machine (SVM) classifier because it has higher performance compared with several other classifiers. Meanwhile, the optimal number of P3 ICs and some other parameter values in the classifiers were determined by the cross-validation procedures. The presented method achieved a balance test accuracy of 84.29% on detecting P3 components for the guilty and innocent participants. The presented method improves the efficiency of CIT in comparison with previous reported methods.

Denoised P300 and machine learning-based concealed information test method

In this paper, a novel P300-based concealed information test (CIT) method was proposed to improve the efficiency of differentiating deception and truth-telling. Thirty subjects including the guilty and innocent performed the paradigm based on three types of stimuli. In order to reduce the influence from the occasional variability of cognitive states on the CIT, several single-trials from Pz in probe stimuli within each subject were first averaged. Then the three groups of features were extracted from these averaged single-trials. Finally, two classes of feature samples were used to train a support vector machine (SVM) classifier. Meanwhile, the optimal number of averaged Pz waveforms and some other parameter values in the classifiers were determined by the cross validation procedures. Results show that if choosing accuracy of 90% as a detecting standard of P3 component to classify a subject's status (guilty or innocent), our method can achieve individual diagnostic rate of 100%. The individual diagnostic rate of our method was higher than the results of the other related reports. The presented method improves efficiency of CIT, and is more practical, lower fatigue and less countermeasure behavior in comparison with previous report methods, which could extend the laboratory study to the practical application.

A new approach for EEG feature extraction in P300-based lie detection

P300-based Guilty Knowledge Test (GKT) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study was to extend a previously introduced pattern recognition method for the ERP assessment in this application. This extension was done by the further extending the feature set and also the employing a method for the selection of optimal features. For the evaluation of the method, several subjects went through the designed GKT paradigm and their respective brain signals were recorded. Next, a P300 detection approach based on some features and a statistical classifier was implemented. The optimal feature set was selected using a genetic algorithm from a primary feature set including some morphological, frequency and wavelet features and was used for the classification of the data. The rates of correct detection in guilty and innocent subjects were 86%, which was better than other previously used methods.

Wavelet analysis reveals dynamics of rat oscillatory potentials

We characterised the dynamics in the oscillatory potentials (OPs) of the rat electroretinogram (ERG) using a continuous complex Morlet wavelet transform. Dark-adapted (>12h) full field ERG responses were recorded from five anaesthetized (ketamine:xylazine, 60:5mg/kg) adult Long-Evans rats (10-12weeks). Five responses were obtained for brief LED flashes (1-4ms) in a ganzfeld at exposures ranging from -4.2 to 1.58logcdsm(-2). Signals were recorded across a bandwidth of 0.3-1kHz and digitized at 10kHz. Morlet wavelets with frequencies between 50 and 250Hz were correlated with raw ERG signals at 1ms intervals. The amplitude of the correlation at each time and frequency was given by the modulus of the complex wavelet response. Candidate OPs were identified as local peaks within 10% of the maximum amplitude. As flash exposure increased, the amplitude of the OP response increased, the peak OP occurred earlier, and the peak OP frequency increased. OPs at brighter flashes clustered into two groups, peaking at 50ms in the 70 and 130Hz band for moderate intensities, and peaking at 20ms in the 70Hz band and 50ms in the 120Hz band for the highest intensities (>0logcdsm(-2)). These dynamics agree with physiological, pharmacological and clinical studies that suggest several distinct neural mechanisms contribute to OPs. Wavelet analysis reveals important dynamics in OP data that are not evident with traditional analytical approaches.

Updating P300: an integrative theory of P3a and P3b

The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulus-driven frontal attention mechanisms during task processing, whereas P3b originates from temporal-parietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations.

Updating P300: an integrative theory of P3a and P3b

The empirical and theoretical development of the P300 event-related brain potential (ERP) is reviewed by considering factors that contribute to its amplitude, latency, and general characteristics. The neuropsychological origins of the P3a and P3b subcomponents are detailed, and how target/standard discrimination difficulty modulates scalp topography is discussed. The neural loci of P3a and P3b generation are outlined, and a cognitive model is proffered: P3a originates from stimulus-driven frontal attention mechanisms during task processing, whereas P3b originates from temporal-parietal activity associated with attention and appears related to subsequent memory processing. Neurotransmitter actions associating P3a to frontal/dopaminergic and P3b to parietal/norepinephrine pathways are highlighted. Neuroinhibition is suggested as an overarching theoretical mechanism for P300, which is elicited when stimulus detection engages memory operations.

Development of gradient descent adaptive algorithms to remove common mode artifact for improvement of cardiovascular signal quality

BACKGROUND

Common-mode noise degrades cardiovascular signal quality and diminishes measurement accuracy. Filtering to remove noise components in the frequency domain often distorts the signal.

METHOD

Two adaptive noise canceling (ANC) algorithms were tested to adjust weighted reference signals for optimal subtraction from a primary signal. Update of weight w was based upon the gradient term of the steepest descent equation: [see text], where the error epsilon is the difference between primary and weighted reference signals. nabla was estimated from Deltaepsilon(2) and Deltaw without using a variable Deltaw in the denominator which can cause instability. The Parallel Comparison (PC) algorithm computed Deltaepsilon(2) using fixed finite differences +/- Deltaw in parallel during each discrete time k. The ALOPEX algorithm computed Deltaepsilon(2)x Deltaw from time k to k + 1 to estimate nabla, with a random number added to account for Deltaepsilon(2) . Deltaw--> 0 near the optimal weighting.

RESULTS

Using simulated data, both algorithms stably converged to the optimal weighting within 50-2000 discrete sample points k even with a SNR = 1:8 and weights which were initialized far from the optimal. Using a sharply pulsatile cardiac electrogram signal with added noise so that the SNR = 1:5, both algorithms exhibited stable convergence within 100 ms (100 sample points). Fourier spectral analysis revealed minimal distortion when comparing the signal without added noise to the ANC restored signal.

CONCLUSIONS

ANC algorithms based upon difference calculations can rapidly and stably converge to the optimal weighting in simulated and real cardiovascular data. Signal quality is restored with minimal distortion, increasing the accuracy of biophysical measurement.

A comparison of methods for ERP assessment in a P300-based GKT

P300-based GKT (guilty knowledge test) has been suggested as an alternative approach for conventional polygraphy. The purpose of this study is to evaluate three classifying methods for this approach and compare their performances in a lab analogue. Several subjects went through the designed GKT paradigm and their respective brain signals were recorded. For the analysis of signals, BAD (bootstrapped amplitude difference) and BCD (bootstrapped correlation difference) methods as two predefined methods alongside a new approach consisting of wavelet features and a statistical classifier were implemented. The rates of correct detection in guilty and innocent subjects were 74-80%. The results indicate the potential of P300-based GKT for detecting concealed information, although further research is required to increase its accuracy and precision and evaluating its vulnerability to countermeasures.

Comparative analysis of event-related potentials during Go/NoGo and CPT: Decomposition of electrophysiological markers of response inhibition and sustained attention

Neuropsychological tests target specific cognitive functions; however, numerous cognitive subcomponents are involved in each test. The aim of this study was to decompose the components of two frontal executive function tests, Go/NoGo (GNG) and cued continuous performance task (CPT), by analyzing event-related potentials (ERPs) of 24 subjects both in time and time-frequency domains. In the time domain, P1, N1, P2, N2 and P3 peak amplitudes and latencies and mean amplitudes of 100 ms time windows of the post-P3 time period were measured. For GNG, the N1 amplitude and for both GNG and CPT N2 amplitudes were significantly higher in the NoGo condition compared with the Go condition. P3 had a central maximum in the NoGo conditions of both paradigms in contrast to a parietal maximum in the Go conditions. All peaks except P1 and mean amplitudes of the post-P3 period were more positive in CPT compared to those of GNG. N1, N2 and P3 latencies were longer for the NoGo condition than the Go condition in the CPT. In time-frequency analyses, the NoGo condition evoked higher theta coefficients than the Go condition, whereas the CPT and GNG paradigms differed mainly in the delta band. These results suggest that theta component reflects response inhibition in both GNG and CPT, whereas delta component reflects the more demanding sustained attention requirement of the CPT. The latency prolongation observed with the NoGo condition of the CPT paradigm was thought to be due to perseverance/inhibition conflict enhanced by the primer stimuli in CPT.

Multichannel Fusion Models for the Parametric Classification of Differential Brain Activity

This paper introduces parametric multichannel fusion models to exploit the different but complementary brain activity information recorded from multiple channels in order to accurately classify differential brain activity into their respective categories. A parametric weighted decision fusion model and two parametric weighted data fusion models are introduced for the classification of averaged multichannel evoked potentials (EPs). The decision fusion model combines the independent decisions of each channel classifier into a decision fusion vector and a parametric classifier is designed to determine the EP class from the discrete decision fusion vector. The data fusion models include the weighted EP-sum model in which the fusion vector is a linear combination of the multichannel EPs and the EP-concatenation model in which the fusion vector is a vector-concatenation of the multichannel EPs. The discrete Karhunen-Loeve transform (DKLT) is used to select features for each channel classifier and from each data fusion vector. The difficulty in estimating the probability density function (PDF) parameters from a small number of averaged EPs is identified and the class conditional PDFs of the feature vectors of averaged EPs are, therefore, derived in terms of the PDFs of the single-trial EPs. Multivariate parametric classifiers are developed for each fusion strategy and the performances of the different strategies are compared by classifying 14-channel EPs collected from five subjects involved in making explicit match/mismatch comparisons between sequentially presented stimuli. It is shown that the performance improves by incorporating weights in the fusion rules and that the best performance is obtained using multichannel EP concatenation. It is also noted that the fusion strategies introduced are also applicable to other problems involving the classification of multicategory multivariate signals generated from multiple sources.

Comparison of STFT and wavelet transform methods in determining epileptic seizure activity in EEG signals for real-time application

Electroencephalography (EEG) is widely used in clinical settings to investigate neuropathology. Since EEG signals contain a wealth of information about brain functions, there are many approaches to analyzing EEG signals with spectral techniques. In this study, the short-time Fourier transform (STFT) and wavelet transform (WT) were applied to EEG signals obtained from a normal child and from a child having an epileptic seizure. For this purpose, we developed a program using Labview software. Labview is an application development environment that uses a graphical language G, usable with an online applicable National Instruments data acquisition card. In order to obtain clinically interpretable results, frequency band activities of delta, theta, alpha and beta signals were mapped onto frequency-time axes using the STFT, and 3D WT representations were obtained using the continuous wavelet transform (CWT). Both results were compared, and it was determined that the STFT was more applicable for real-time processing of EEG signals, due to its short process time. However, the CWT still had good resolution and performance high enough for use in clinical and research settings.

Alzheimer's disease: models of computation and analysis of EEGs

In a recent article the authors presented a comprehensive review of research performed on computational modeling of Alzheimer's disease (AD) and its markers with a focus on computer imaging, classification models, connectionist neural models, and biophysical neural models. The popularity of imaging techniques for detection and diagnosis of possible AD stems from the relative ease with which neurological markers can be converted to visual markers. However, due to the expense of specialized experts and equipment involved in the use of imaging techniques, a subject of significant research interest is detecting markers in EEGs obtained from AD patients. In this article, the authors present a state-of-the-art review of models of computation and analysis of EEGs for diagnosis and detection of AD. This review covers three areas: time-frequency analysis, wavelet analysis, and chaos analysis. The vast number of physiological parameters involved in the poorly understood processes responsible for AD yields a large combination of parameters that can be manipulated and studied. A combination of parameters from different investigation modalities seems to be more effective in increasing the accuracy of detection-and diagnosis.

Wavelet-Crosscorrelation Analysis: Non-Stationary Analysis of Neurophysiological Signals

OBJECTIVE

Wavelet-crosscorrelation analysis is a new application of wavelet analysis used to show the propagation of epileptiform discharges and to localize the corresponding lesions. We have shown previously that this analysis can help predict brain conditions statistically (Mizuno-Matsumoto et al. 2002). Our objective was to assess whether wavelet-crosscorrelation analysis reveals the initiation and propagation of epileptiform activity in human patients.

METHODS

The data obtained from three patients with simple partial seizures (SPS) using whole-head magnetoencephalography (MEG) were analyzed by the wavelet-crosscorrelation method. Wavelet-crosscorrelation coefficients (WCC), the coherent structure of each possible pair of signals from 64 MEG channels forvarious periods, and the time lag (TL) in two related signals, were ascertained.

RESULTS

We clearly demonstrated both localization of the irritative zone and propagation of the epileptiform discharges.

CONCLUSIONS

Wavelet-crosscorrelation analysis can help reveal and visualize the dynamic changes of brain conditions. The method of this analysis can compensate for other existing methods for the analysis of MEG, electroencephalography (EEG) or Elecotrocorticography (ECoG).

SIGNIFICANCE

Our proposed method suggests that revealing and visualizing the dynamic changes of brain conditions can help clinicians and even patients themselves better understand such conditions.

Wavelet analysis for neonatal electroencephalographic seizures

Electroencepholographs (EEGs) of neonatal seizures differ from those of children and adults. This study evaluated whether wavelet transform analysis, a nonstationary frequency analysis of EEG, can recognize and characterize neonatal seizures. Twenty-second segments were analyzed from 69 EEG seizures in 15 neonatal patients whose seizures lasted 10 seconds or longer. The wavelet transform results were examined, as were EEG seizure durations and dominant frequencies. The wavelet transform results were correlated with the occurrence, after an 18-month follow-up, of postneonatal seizures. Wavelet transform analysis identified 40 seizures (58%) with a "sustained dominant frequency component" that lasted 10 seconds or longer and 29 seizures without a sustained dominant frequency component. The mean seizure duration of the 40 seizures with sustained dominant frequency components was 63.3 seconds, longer than the mean duration (33.6 seconds) of the seizures without sustained dominant frequency components, P < 0.01. Eleven patients manifested postneonatal epileptic seizures. Fifty-two EEG seizures in these 11 patients revealed more sustained dominant frequency components (74%) than 17 seizures in the 4 patients without postneonatal seizures (only 12%), P < 0.05. Wavelet transform analysis can identify neonatal EEG seizures and characterize their epileptic components. The presence of sustained dominant frequency components may predict postneonatal epileptic seizures.

A transient dominance of theta event-related brain potential component characterizes stimulus processing in an auditory oddball task

OBJECTIVE

Following external stimulation, electroencephalographic (EEG) responses from different frequency bands occur simultaneously, but little is known about whether and how concurrent multi-frequency responses depend on each other during stimulus information processing. The present study assessed the effects of task stimulus relevance on locally co-existent time-frequency components of event-related brain potentials (ERPs).

METHODS

The wavelet entropy (WE) of ERPs was used as an analytical tool because low entropy values correspond to a narrow-band (mono-frequency) activity characterizing highly ordered (regularized) bioelectric states. The minimum of WE in the ERPs (WEmin) was identified to reflect a transient dominance of one particular frequency ERP component over other frequency components. In an auditory oddball condition, effects of stimulus relevance were analyzed for the timing, rate of decrease, and frequency determinants of WEmin in 10 subjects.

RESULTS

Major results demonstrate that a highly ordered EEG microstate emerged in response to both target and non-target stimuli, as evidenced by the substantial decrement of ERP entropy. This microstate (1) was short lasting as indexed by the transitory entropy decrease, (2) had a functionally specific time-localization as reflected by stimulus and electrode effects on WEmin latency, and (3) for both stimulus types was determined by a pronounced dominance of locally synchronized theta (4-8 Hz) oscillations.

CONCLUSIONS

These results reveal a new neuroelectric correlate of stimulus processing and suggest that a theta-dominated microstate in the ERP may reflect a basic processing stage of stimulus evaluation, during which interfering activations from other frequency networks are minimized.

SIGNIFICANCE

In the framework of event-related brain dynamics, this study provides evidence that during stimulus processing, there is an interaction of locally co-existent multiple frequency ERP components. It is characterized by a transitory dominance of synchronized theta oscillations over other frequency ERP components emerging irrespective of stimulus task relevance and frequency ERP content, which may reflect basic processing mechanisms.

Developmental event-related gamma oscillations: effects of auditory attention

This study describes maturational changes in topographical patterns, stability, and functional reactivity of auditory gamma band (31-63 Hz) responses (GBRs) as brain electrical correlates relevant for cognitive development during childhood. GBRs of 114 healthy children from 9 to 16 years were elicited in an auditory focused attention task requiring motor responding to targets, and analyzed by means of the wavelet transform (WT). The effects of age and task variables (attended side and stimulus type relevance) were examined for GBR power and phase-locking within 120 ms after stimulation. Similar to the spontaneous gamma band power, the power and phase-synchronization of GBRs did not depend on the age. However, the functional reactivity of GBRs at specific locations changed in the course of development. In 9-12-year-old children, GBRs at frontal locations were larger and better synchronized to target than to nontarget stimulus type, and were larger over the left hemisphere (contralateral to the responding hand), thus manifesting sensitivity to external stimulus features and motor task. In 13-16-year-old adolescents, GBRs at parietal sites were enhanced by active attending to the side of stimulation, thus being associated with a maintenance of attentional focus to stimulus location. The results indicate that (i) specific aspects of task-stimulus processing engage distinct spatially localized gamma networks at functionally relevant areas, and (ii) the neuronal substrates of gamma band networks and the ability to synchronize them in relation to task-specific processes are available in all age groups from 9 to 16 years. However, the mode and efficiency with which gamma networks can be entrained depends on the age. This age-dependent reactivity of GBRs to different task variables may reflect a transition in processing strategies emerging at approximately 12-13 years in relation to the maturation of cognitive and executive brain functions.

Wavelet entropy analysis of event-related potentials indicates modality-independent theta dominance

Sensory/cognitive stimulation elicits multiple electroencephalogram (EEG)-oscillations that may be partly or fully overlapping over the time axis. To evaluate co-existent multi-frequency oscillations, EEG responses to unimodal (auditory or visual) and bimodal (combined auditory and visual) stimuli were analyzed by applying a new method called wavelet entropy (WE). The method is based on the wavelet transform (WT) and quantifies entropy of short segments of the event-related brain potentials (ERPs). For each modality, a significant transient decrease of WE emerged in the post-stimulus EEG epoch indicating a highly-ordered state in the ERP. WE minimum was always determined by a prominent dominance of theta (4-8 Hz) ERP components over other frequency bands. Event-related 'transition to order' was most pronounced and stable at anterior electrodes, and after bimodal stimulation. Being consistently observed across different modalities, a transient theta-dominated state may reflect a processing stage that is obligatory for stimulus evaluation, during which interfering activations from other frequency networks are minimized.

Wavelet Transform in the analysis of the frequency composition of evoked potentials

This technical paper deals with the application of the Wavelet Transform to the study of evoked potentials. In particular, Wavelet Transform gives an optimal time-dependent frequency decomposition of the evoked responses, something difficult to be achieved with previous methods such as the Fourier Transform. We describe in detail the protocol for implementing the decomposition based on the Wavelet Transform and apply it to two different types of evoked potentials. In the first case we study alpha responses in pattern visual evoked potentials and in the second case, we study gamma responses to bimodal (auditory and visual) stimulation. Although in this study we focus on methodological issues, we briefly discuss physiological implications of the present time-frequency analysis. Furthermore, we show examples of the better performance of the wavelet decomposition in comparison with Fourier-based methods.

Decomposition of event-related brain potentials into multiple functional components using wavelet transform

Event related brain potential (ERP) waveforms consist of several components extending in time, frequency and topographical space. Therefore, an efficient processing of data which involves the time, frequency and space features of the signal, may facilitate understanding the plausible connections among the functions, the anatomical structures and neurophysiological mechanisms of the brain. Wavelet transform (WT) is a powerful signal processing tool for extracting the ERP components occurring at different time and frequency spots. A technical explanation of WT in ERP processing and its four distinct applications are presented here. The first two applications aim to identify and localize the functional oddball ERP components in terms of certain wavelet coefficients in delta, theta and alpha bands in a topographical recording. The third application performs a similar characterization that involves a three stimulus paradigm. The fourth application is a single sweep ERP processing to detect the P300 in single trials. The last case is an extension of ERP component identification by combining the WT with a source localization technique. The aim is to localize the time-frequency components in three dimensional brain structure instead of the scalp surface. The time-frequency analysis using WT helps isolate and describe sequential and/or overlapping functional processes during ERP generation, and provides a possibility for studying these cognitive processes and following their dynamics in single trials during an experimental session.

Abnormal early stages of task stimulus processing in children with attention-deficit hyperactivity disorder – evidence from event-related gamma oscillations

OBJECTIVES

Attention-related differences in early stages of stimulus processing were assessed in healthy controls and children with attention-deficit hyperactivity disorder (ADHD) by analyzing phase-locked gamma band (31-63 Hz) responses to auditory stimuli in a selective-attention task.

METHODS

A total of 28 children aged 9-12 years (ADHD and matched healthy controls) pressed a button in response to each target stimulus presented at the attended side (right or left). Auditory gamma band responses (GBRs) within 0-120 ms were analyzed at 8 electrodes with wavelet transform. Effects of attended channel, stimulus type, and group were evaluated for GBR power and phase-locking.

RESULTS

For both groups, GBRs had a frontal-central distribution, were significantly larger and more strongly phase-locked to target than to non-target stimuli, and did not differentiate the attended from the unattended channel. ADHD children produced larger and more strongly phase-locked GBRs than controls only to right-side stimuli, irrespective of whether these were the attended or the ignored stimuli.

CONCLUSIONS

The association between auditory GBR and motor task stimulus in children suggests that phase-locked gamma oscillations may reflect processes of sensory-motor integration. ADHD-related deviations of GBRs indicate that early mechanisms of auditory stimulus processing are altered in ADHD, presumably as a result of impaired motor inhibition.

Bisensory stimulation increases gamma-responses over multiple cortical regions

In the framework of the discussion about gamma (approx. 40 Hz) oscillations as information carriers in the brain, we investigated the relationship between gamma responses in the EEG and intersensory association. Auditory evoked potentials (AEPs) and visual evoked potentials (VEPs) were compared with bisensory evoked potentials (BEPs; simultaneous auditory and visual stimulation) in 15 subjects. Gamma responses in AEPs, VEPs and BEPs were assessed by means of wavelet decomposition. Overall maximum gamma-components post-stimulus were highest in BEPs (P < 0.01). Bisensory evoked gamma-responses also showed significant central, parietal and occipital amplitude-increases (P < 0.001, P < 0.01, P < 0.05, respectively; prestimulus interval as baseline). These were of greater magnitude when compared with the unisensory responses. As a correlate of the marked gamma responses to bimodal stimulation we suggest a process of 'intersensory association', i.e. one of the steps between sensory transmission and perception. Our data may be interpreted as a further example of function-related gamma responses in the EEG.

Single sweep analysis of event related auditory potentials for the monitoring of sedation in cardiac surgery patients

Event-related potentials (ERPs) from the auditory system were investigated in 28 post-operative cardiac patients in order to assess their relevance in the monitoring of patient sedation level. Midazolam (17 patients) and propofol (11 patients) were the sedative agents used. The auditory ERP components of N100 (HAB100) and mismatch negativity (MMN) were considered. A single sweep method based on the AutoRegressive with eXogenous input (ARX) model, which is able to enhance the evoked responses to each single stimulus, was used to process each sweep and to compute traditional parameters on a sweep-by-sweep basis. Differences in the measured parameters were related to variations in the patient sedation levels classified through Ramsay score. Significant differences (P<0.05) in both MMN and HAB100 parameters were found between light sedation (LS) and deep sedation (DS) levels.

Gamma band response in children is related to task-stimulus processing

The present study described the functional characteristics of early phase-locked electroencephalographic gamma band (30-60 Hz) responses (GBRs) in 9- to 12-year-old healthy children. GBRs were elicited in an auditory selective-attention task. Target stimuli required motor responding when presented to the right or to the left (attended side). Effects of stimulus type relevance and attended side were evaluated for GBR power and phase-locking within 0-120 ms. GBRs in children were frontally distributed, were larger and better phase-locked to targets relative to non-targets, but did not depend on the attended side. These results demonstrate that the auditory GBR is related to task-stimulus processing and imply that early target selection in children is guided by a sensory or sensorimotor preparatory set, rather than by an internal attentional focus to the side of stimulation.

Subspace averaging of steady-state visual evoked potentials

A new algorithm for doing signal averaging of steady-state visual evoked potentials (VEP's) is described. The subspace average is obtained by finding the orthogonal projection of the VEP measurement vector onto the signal subspace, which is based on a sinusoidal VEP signal model. The subspace average is seen to out-perform the conventional average using a new signal-to-noise-ratio-based performance measure on simulated and actual VEP data.

Multiple time-frequency components account for the complex functional reactivity of P300

Consecutive and overlapping time-frequency (TF) components of auditory event-related brain potentials (ERPs) were analyzed to examine whether multiple co-existing components may account for the complex functional reactivity of P300. Auditory ERPs of 14 adult subjects were decomposed by means of the wavelet transform (WT), and TF components within P300 were tested in a systematic manner for the effects of major P300 determinants: stimulus probability, active discrimination, and mental count task. The results demonstrated that several partly or fully simultaneous delta, theta, and alpha TF components significantly depend on the factors eliciting P300, and also manifest distinct patterns of task reactivity and scalp distribution. Thus, specific functional processes that underlie the P300 ERP can be distinguished that help to account for its responsiveness to task variables.

Identification of hemifield single trial PVEP on the basis of generalized dynamic neural network classifiers

This paper is concerned with the application of generalized dynamic neural networks for the identification of hemifield pattern-reversal visual evoked potentials. The identification process is performed by different networks with time-varying weights using signals from different electrode positions as external inputs. Since dynamic neural networks are able to process time-varying signals, the identification of the stimulated hemiretinae is performed without feature extraction. The performance of the method presented is compared with a reference method based on the values of instantaneous frequency at the occipital electrode positions at P100 latency.

A model for P300 generation based on responses to near-threshold visual stimuli

Near-threshold and suprathreshold visual ERPs and their frequency components were compared with the aim to obtain further information on the generation mechanism of the P300 wave. Decrease of the stimulus energy from suprathreshold to near-threshold level resulted in an increase of the P300 amplitude specifically in the occipital region. This finding was in contrast with the P300 amplitude decrease in central and frontal regions and its constancy in parietal area. Delta and theta responses showed a similar distribution pattern, whereas alpha responses decreased in all regions as the stimulus energy decreased. We conclude that P300 wave may correspond to a delta oscillation during a widespread, transient interruption of afferent inputs from subcortical structures to the cortical neurons including those in the visual sensory area and simultaneous increase of the cortico-cortical interactions. If visual inputs are of suprathreshold strength, they override this effect specifically in the primary visual area and disrupt the cortico-cortical interactions and the emergence of P300 in the occipital cortex.

Functions and sources of event-related EEG alpha oscillations studied with the Wavelet Transform

OBJECTIVES

By using the Wavelet Transform, a time frequency representation with nearly optimal resolution, we studied responses to stimulation in the 'alpha' range (10 Hz).

METHODS

Visual evoked responses of 10 healthy subjects were studied with 3 different stimulus types (no-task VEP, non-target and target stimulus).

RESULTS

Upon all the stimulus types, event-related responses in the 10 Hz ('alpha') range were distributed in the whole scalp, best defined in the occipital locations, the responses on the anterior electrodes being less pronounced and delayed. In some subjects, these event-related responses were prolonged upon target stimulation in posterior locations.

CONCLUSIONS

These results point towards a distributed origin of event-related alpha oscillations with functional relation to sensory processing, and possibly to further processes.

Time-frequency analysis of single-sweep event-related potentials by means of fast wavelet transform

A time-frequency decomposition was applied to the event-related potentials (ERPs) elicited in an auditory oddball condition to assess differences in cognitive information processing. Analysis in the time domain has revealed that cognitive processes are reflected by various ERP components such as N1, P2, N2, P300, and late positive complex. However, the heterogeneous nature of these components has been strongly emphasized due to simultaneously occurring processes. The wavelet transform (WT), which decomposes the signal onto the time-frequency plane, allows the time-dependent and frequency-related information in ERPs to be captured and precisely measured. A four-octave quadratic B-spline wavelet transform was applied to single-sweep ERPs recorded in an auditory oddball paradigm. Frequency components in delta, theta, and alpha ranges reflected specific aspects of cognitive information processing. Furthermore, the temporal position of these components was related to specific cognitive processes.

Detection of P300 waves in single trials by the wavelet transform (WT)

The P300 response is conventionally obtained by averaging the responses to the task-relevant (target) stimuli of the oddball paradigm. However, it is well known that cognitive ERP components show a high variability due to changes of cognitive state during an experimental session. With simple tasks such changes may not be demonstrable by the conventional method of averaging the sweeps chosen according to task-relevance. Therefore, the present work employed a response-based classification procedure to choose the trials containing the P300 component from the whole set of sweeps of an auditory oddball paradigm. For this purpose, the most significant response property reflecting the P300 wave was identified by using the wavelet transform (WT). The application of a 5 octave quadratic B-spline-WT on single sweeps yielded discrete coefficients in each octave with an appropriate time resolution for each frequency range. The main feature indicating a P300 response was the positivity of the 4th delta (0.5-4 Hz) coefficient (310-430 ms) after stimulus onset. The average of selected single sweeps from the whole set of data according to this criterion yielded more enhanced P300 waves compared with the average of the target responses, and the average of the remaining sweeps showed a significantly smaller positivity in the P300 latency range compared with the average of the non-target responses. The combination of sweeps classified according to the task-based and response-based criteria differed significantly. This suggests an influence of changes in cognitive state on the presence of the P300 wave which cannot be assessed by task performance alone.