An Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials

2015 ◽  
Vol 62 (7) ◽  
pp. 1696-1705 ◽  
Author(s):  
Hendrik Woehrle ◽  
Mario M. Krell ◽  
Sirko Straube ◽  
Su Kyoung Kim ◽  
Elsa A. Kirchner ◽  
...  
Keyword(s):  
Event Related Potentials ◽  
Spatial Filter ◽  
Single Trial ◽  
Related Potentials ◽  
Adaptive Spatial Filter

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

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.

Multilinear Discriminant Analysis With Subspace Constraints for Single-Trial Classification of Event-Related Potentials

2016 ◽  
Vol 10 (7) ◽  
pp. 1295-1305 ◽  
Author(s):  
Hiroshi Higashi ◽  
Tomasz M. Rutkowski ◽  
Toshihisa Tanaka ◽  
Yuichi Tanaka
Keyword(s):  
Discriminant Analysis ◽  
Event Related Potentials ◽  
Single Trial ◽  
Related Potentials ◽  
Subspace Constraints

Single-trial event-related potentials to significant stimuli

2009 ◽  
Vol 74 (2) ◽  
pp. 120-131 ◽  
Author(s):  
Jacqueline A. Rushby ◽  
Robert J. Barry
Keyword(s):  
Event Related Potentials ◽  
Single Trial ◽  
Related Potentials

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.

30 Relationship between EEG delta response and P300 analysed in single trial event-related potentials (ERPS)

1998 ◽  
Vol 30 (1-2) ◽  
pp. 14 ◽  
Author(s):  
M. Schürmarnn ◽  
C. Basar-Eroglu ◽  
V. Kolev ◽  
E. Basar
Keyword(s):  
Event Related Potentials ◽  
Single Trial ◽  
Related Potentials
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