Classification Of Event-related Potentials Research Articles

Toward an understanding of auditory evoked cortical event-related potentials: Characteristics and classification

Objective: To investigate the characteristics of auditory evoked cortical ERP components P1-N1-P2 and MMN and explore a practical way for ERP waveform identification and classification. Methods: Thirty right-handed normally hearing volunteers participated in the present study, age ranging from 20 to 40 years old, 14 males and 16 females. All the volunteers reported no history of auditory, neurological or mental disorder. The event related potential (ERP) components (i.e. P1-N1-P2 complex and mismatch negativity (MMN)) were measured using the 128-electrode channels EGI system. Results: Three different categories of ERP responses were classified on the basis of waveform configuration, size of the peak amplitude and the number of peaks together with scalp distribution of MMN. Ten participants (33.3%) had well defined ERP responses, 13 (43.3%) showed moderately defined ERP responses, and seven (23.3%) had poorly defined ERP responses. Although there were no significant differences in P1, P2, and MMN latencies, participants with the poorly defined ERP waves had significantly longer N1 latency than that in subjects with well defined ERP waves. In addition, significantly lower MMN amplitudes were also found in this group. Conclusion: Combining a waveform classification method and the MMN scalp distribution pattern, together with quantitative ERP response analysis, may provide more reliable and practical means for clinical application.

Central-tendency estimation and nearest-estimate classification of event related potentials

Event related potentials (ERPs) are modeled as random vectors in order to determine multivariate central-tendency (C-T) estimates of ERPs such as the arithmetic mean, geometric mean, harmonic mean, median, tri-mean, trimmed-mean, and the Winsorized mean. Additionally, it is shown that the C-T estimates can be used to implement various forms of minimum-distance classifiers for individual channels and for single-channel heterogeneous, multi-channel homogeneous, and multi-channel heterogeneous-homogenous ERP classification through decision fusion. The study also focuses on answering the following related questions: (a) How do the C-T ERP estimates compare with each other? (b) How do the performances of nearest-estimate classifiers compare with each other? (c) For a given ERP channel, do the heterogeneous nearest-estimate classifiers offer complementary information for improving performance through decision fusion? (d) Do the homogeneous nearest-estimate classifiers of different channels offer complementary information for improving performance through decision fusion? (e) Can the performance be improved by fusing the decisions of all or a selected subset of the entire classifier ensemble? These questions are answered by designing estimation and classification experiments using real 6-channel ERPs. It is shown that although the operations to compute the vector C-T estimates can be quite different, the ERP estimates are similar with respect to their overall waveform shapes and peak latencies. Furthermore, the results of the classification experiments show that by fusing homogeneous nearest-estimate classifier decisions across multiple channels, the classification accuracy can be improved significantly when compared with the accuracies of individual channel classifiers.

Single-trial analysis and classification of ERP components — A tutorial

Analyzing brain states that correspond to event related potentials (ERPs) on a single trial basis is a hard problem due to the high trial-to-trial variability and the unfavorable ratio between signal (ERP) and noise (artifacts and neural background activity). In this tutorial, we provide a comprehensive framework for decoding ERPs, elaborating on linear concepts, namely spatio-temporal patterns and filters as well as linear ERP classification. However, the bottleneck of these techniques is that they require an accurate covariance matrix estimation in high dimensional sensor spaces which is a highly intricate problem. As a remedy, we propose to use shrinkage estimators and show that appropriate regularization of linear discriminant analysis (LDA) by shrinkage yields excellent results for single-trial ERP classification that are far superior to classical LDA classification. Furthermore, we give practical hints on the interpretation of what classifiers learned from the data and demonstrate in particular that the trade-off between goodness-of-fit and model complexity in regularized LDA relates to a morphing between a difference pattern of ERPs and a spatial filter which cancels non task-related brain activity.

Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling

Classification of Event-Related Potentials Associated with Response Errors in Actors and Observers Based on Autoregressive Modeling

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. Feature extraction consisted of Multivariate Autoregressive modeling combined with the Simulated Annealing technique. The resulting information was subsequently classified by means of an Artificial Neural Network (ANN) using back-propagation algorithm under the “leave-one-out cross-validation” scenario and the Fuzzy C-Means (FCM) algorithm. The ANN consisted of a multi-layer perceptron (MLP). The approach yielded classification rates of up to 85%, both for the actors’ correct and incorrect responses and the corresponding ERPs of the observers. The electrodes needed for such classifications were situated mainly at central and frontal areas. Results provide indications that the classification of the ERN is achievable. Furthermore, the availability of the Pe signals, in addition to the ERN, improves the classification, and this is more pronounced for observers’ signals. The proposed ERP-signal classification method provides a promising tool to study error detection and observational-learning mechanisms in performance monitoring and joint-action research, in both healthy and patient populations.

Model comparison for automatic characterization and classification of average ERPs using visual oddball paradigm

To determine whether automated classifiers can be used for correctly identifying target categorization responses from averaged event-related potentials (ERPs) along with identifying appropriate features and classification models for computer-assisted investigation of attentional processes. ERPs were recorded during a target categorization task. Automated classification of average target ERPs versus average non-target ERPs was performed by extracting different combinations of features from the P300 and N200 components, which were used to train six classifiers: Euclidean classifier (EC), Mahalanobis discriminant (MD), quadratic classifier (QC), Fisher linear discriminant (FLD), multi-layer perceptron neural network (MLP) and support vector machine (SVM). The best classification performance (accuracy: 91-92%; sensitivity: 85-86%; specificity: 95-99%) was provided by QC, MLP, SVM on feature vectors extracted from P300 recorded at multiple sites. In general, non-linear and non-parametric classifiers (QC, MLP, SVM) performed better than linear classifiers (EC, MD, FLD). The N200 did not explain variance beyond that of P300 recorded at multiple sites. The results suggest that automatic characterization and classification of average target and non-target ERPs is feasible. Features of P300 recorded at multiple sites used to train non-linear classifiers are recommended for optimal classification performance. Automatic characterization of target ERPs can provide an objective approach for detecting and diagnosing abnormalities and evaluating interventions for clinical populations, paving the way for future real-time monitoring of attentional processes.

BAYESIAN CLASSIFICATION OF SINGLE-TRIAL EVENT-RELATED POTENTIALS IN EEG

We present a systematic and straightforward approach to the problem of single-trial classification of event-related potentials (ERP) in EEG. Instead of using a generic classifier off-the-shelf, like a neural network or support vector machine, our classifier design is guided by prior knowledge about the problem and statistical properties found in the data. In particular, we exploit the well-known fact that event-related drifts in EEG potentials, albeit hard to detect in a single trial, can well be observed if averaged over a sufficiently large number of trials. We propose to use the average signal and its variance as a generative model for each event class and use Bayes' decision rule for the classification of new and unlabeled data. The method is successfully applied to a data set from the NIPS*2001 Brain–Computer Interface post-workshop competition. Our result turned out to be competitive with the best result of the competition.

Classification of event-related potentials using multivariate autoregressive modeling combined with simulated annealing

In the present work, a new method for the classification of Event Related Potentials (ERPs) is proposed. The proposed method consists of two modules: the feature extraction module and the classification module. The feature extraction module comprises the implementation of the Multivariate Autoregressive model in conjunction with the Simulated Annealing technique, for the selection of optimum features from ERPs. The classification module is implemented with a single three-layer neural network, trained with the back-propagation algorithm and classifies the data into two classes: patients and control subjects. The method, in the form of a Decision Support System (DSS), has been thoroughly tested to a number of patient data (OCD, FES, depressives and drug users), resulting successful classification up to 100%.

Parallel principal component neural networks for classification of event-related potential waveforms

Artificial neural networks (ANNs) are discussed in terms of classification of brain auditory event-related potentials (ERPs). A new ANN architecture for the classification of ERPs is proposed. The new architecture is called the parallel principal component neural network (PPCNN). The use of the PPCNN for classification of ERP data obtained from both normal control subjects and chronic schizophrenic patients is discussed. Experimental results are given.

An Artificial Neural-Network Approach to ERP Classification

Artificial neural network-based approaches were developed to classify event-related potential (ERP) waveforms. The networks utilized scalp-recorded ERP returns from six electrode sites. These ERPs were evoked as one individual responded to a series of auditorily presented object names while viewing various objects on a computer screen. The ERPs at the electrode sites were classified as a match decision or a no-match decision. A three-layer backpropagation neural network model was selected to formulate a global and a local classification approach. The backpropagation network in the global approach was designed to operate on a single ERP response which was the average of the ERP responses generated at the six electrode sites. The local ERP classification system consisted of six three-layer backpropagation networks. Each network was designed to operate on the ERPs generated at a single electrode site. A small data base consisting of eight match and eight no-match ERP responses was used to train and test the networks in a variety of ways. The results obtained clearly show that the neural network-based classifiers are able to discriminate with a high degree of accuracy between match and no-match conditions in ERP waveforms.

Multichannel, single trial event related potential classification.

We report on the effect of electrode placement and number of electrodes on the classification of single trial event related potentials (ERP's). The subjects read propositions relating fictitious people and their occupations while ERP's were recorded. The subjects decided if the proposition was correct or incorrect and responded as per instructions. The single trial, multichannel ERP data were classified using various methods, e. g., hold-out, leave-one-out, resubstitution. Several other factors were examined to determine their effect on ERP classification, including taking a majority vote among channels, using the single best channel, and averaging the data across channels for a single ERP. The results from other experiments are compared to those presented here.