scholarly journals Taking into account latency, amplitude, and morphology: improved estimation of single-trial ERPs by wavelet filtering and multiple linear regression

2011 ◽  
Vol 106 (6) ◽  
pp. 3216-3229 ◽  
Author(s):  
L. Hu ◽  
M. Liang ◽  
A. Mouraux ◽  
R. G. Wise ◽  
Y. Hu ◽  
...  
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Pain Perception ◽  
Event Related Potentials ◽  
Relevant Information ◽  
Single Trial ◽  
Data Set ◽  
Wavelet Filtering ◽  
Sensory Stimuli ◽  
Related Potentials

Across-trial averaging is a widely used approach to enhance the signal-to-noise ratio (SNR) of event-related potentials (ERPs). However, across-trial variability of ERP latency and amplitude may contain physiologically relevant information that is lost by across-trial averaging. Hence, we aimed to develop a novel method that uses 1) wavelet filtering (WF) to enhance the SNR of ERPs and 2) a multiple linear regression with a dispersion term (MLRd) that takes into account shape distortions to estimate the single-trial latency and amplitude of ERP peaks. Using simulated ERP data sets containing different levels of noise, we provide evidence that, compared with other approaches, the proposed WF+MLRd method yields the most accurate estimate of single-trial ERP features. When applied to a real laser-evoked potential data set, the WF+MLRd approach provides reliable estimation of single-trial latency, amplitude, and morphology of ERPs and thereby allows performing meaningful correlations at single-trial level. We obtained three main findings. First, WF significantly enhances the SNR of single-trial ERPs. Second, MLRd effectively captures and measures the variability in the morphology of single-trial ERPs, thus providing an accurate and unbiased estimate of their peak latency and amplitude. Third, intensity of pain perception significantly correlates with the single-trial estimates of N2 and P2 amplitude. These results indicate that WF+MLRd can be used to explore the dynamics between different ERP features, behavioral variables, and other neuroimaging measures of brain activity, thus providing new insights into the functional significance of the different brain processes underlying the brain responses to sensory stimuli.

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

2004 ◽  
Vol 14 (02) ◽  
pp. 719-726 ◽  
Author(s):  
JENS KOHLMORGEN ◽  
BENJAMIN BLANKERTZ
Keyword(s):  
Event Related Potentials ◽  
Computer Interface ◽  
Support Vector ◽  
Single Trial ◽  
Data Set ◽  
Classifier Design ◽  
Event Class ◽  
Related Potentials ◽  
Straightforward Approach

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.

Spatial Representations as an Emergent Feature of Perceptual Processing

2010 ◽  
Vol 24 (3) ◽  
pp. 161-172 ◽  
Author(s):  
Edmund Wascher ◽  
C. Beste
Keyword(s):  
Common Ground ◽  
Event Related Potentials ◽  
Vertical Axis ◽  
Relevant Information ◽  
Sources Of Information ◽  
Integrated Network ◽  
Perceptual Maps ◽  
Emergent Feature ◽  
Related Potentials ◽  
Selection Of

Spatial selection of relevant information has been proposed to reflect an emergent feature of stimulus processing within an integrated network of perceptual areas. Stimulus-based and intention-based sources of information might converge in a common stage when spatial maps are generated. This approach appears to be inconsistent with the assumption of distinct mechanisms for stimulus-driven and top-down controlled attention. In two experiments, the common ground of stimulus-driven and intention-based attention was tested by means of event-related potentials (ERPs) in the human EEG. In both experiments, the processing of a single transient was compared to the selection of a physically comparable stimulus among distractors. While single transients evoked a spatially sensitive N1, the extraction of relevant information out of a more complex display was reflected in an N2pc. The high similarity of the spatial portion of these two components (Experiment 1), and the replication of this finding for the vertical axis (Experiment 2) indicate that these two ERP components might both reflect the spatial representation of relevant information as derived from the organization of perceptual maps, just at different points in time.

Single-trial analysis of oddball event-related potentials in simultaneous EEG-fMRI

2007 ◽  
Vol 28 (7) ◽  
pp. 602-613 ◽  
Author(s):  
Christian-G. Bénar ◽  
Daniele Schön ◽  
Stephan Grimault ◽  
Bruno Nazarian ◽  
Boris Burle ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Single Trial ◽  
Single Trial Analysis ◽  
Trial Analysis ◽  
Related Potentials

Estimation of Event-Related Potentials from Single-Trial EEG

2021 ◽  
pp. 415-427
Author(s):  
Siyuan Zang ◽  
Changle Zhou ◽  
Fei Chao
Keyword(s):  
Event Related Potentials ◽  
Single Trial ◽  
Related Potentials

Personal Adaptive Method to Assess Mental Tension during Daily Life Using Heart Rate Variability

2012 ◽  
Vol 51 (01) ◽  
pp. 39-44 ◽  
Author(s):  
K. Matsuoka ◽  
K. Yoshino
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Test Data ◽  
Daily Life ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Training Data ◽  
Data Set

SummaryObjectives: The aim of this study is to present a method of assessing psychological tension that is optimized to every individual on the basis of the heart rate variability (HRV) data which, to eliminate the influence of the inter-individual variability, are measured in a long time period during daily life.Methods: HRV and body accelerations were recorded from nine normal subjects for two months of normal daily life. Fourteen HRV indices were calculated with the HRV data at 512 seconds prior to the time of every mental tension level report. Data to be analyzed were limited to those with body accelerations of 30 mG (0.294 m/s2) and lower. Further, the differences from the reference values in the same time zone were calculated with both the mental tension score (Δtension) and HRV index values (ΔHRVI). The multiple linear regression model that estimates Δtension from the scores for principal components of ΔHRVI were then constructed for each individual. The data were divided into training data set and test data set in accordance with the twofold cross validation method. Multiple linear regression coefficients were determined using the training data set, and with the optimized model its generalization capability was checked using the test data set.Results: The subjects’ mean Pearson correlation coefficient was 0.52 with the training data set and 0.40 with the test data set. The subjects’ mean coefficient of determination was 0.28 with the training data set and 0.11 with the test data set.Conclusion: We proposed a method of assessing psychological tension that is optimized to every individual based on HRV data measured over a long period of daily life.

scholarly journals Single-Option P300-BCI Performance Is Affected by Visual Stimulation Conditions

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7198
Author(s):  
Juan David Chailloux Peguero ◽  
Omar Mendoza-Montoya ◽  
Javier M. Antelis
Keyword(s):  
Visual Stimulation ◽  
Event Related Potentials ◽  
Classification Performance ◽  
Single Trial ◽  
Experimental Protocol ◽  
Training Time ◽  
Machine Learning Model ◽  
Visual Frame ◽  
Related Potentials ◽  
The Relationship

The P300 paradigm is one of the most promising techniques for its robustness and reliability in Brain-Computer Interface (BCI) applications, but it is not exempt from shortcomings. The present work studied single-trial classification effectiveness in distinguishing between target and non-target responses considering two conditions of visual stimulation and the variation of the number of symbols presented to the user in a single-option visual frame. In addition, we also investigated the relationship between the classification results of target and non-target events when training and testing the machine-learning model with datasets containing different stimulation conditions and different number of symbols. To this end, we designed a P300 experimental protocol considering, as conditions of stimulation: the color highlighting or the superimposing of a cartoon face and from four to nine options. These experiments were carried out with 19 healthy subjects in 3 sessions. The results showed that the Event-Related Potentials (ERP) responses and the classification accuracy are stronger with cartoon faces as stimulus type and similar irrespective of the amount of options. In addition, the classification performance is reduced when using datasets with different type of stimulus, but it is similar when using datasets with different the number of symbols. These results have a special connotation for the design of systems, in which it is intended to elicit higher levels of evoked potentials and, at the same time, optimize training time.

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