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.

scholarly journals Temporal and Spatial Features of Single-Trial EEG for Brain-Computer Interface

2007 ◽  
Vol 2007 ◽  
pp. 1-14 ◽  
Author(s):  
Qibin Zhao ◽  
Liqing Zhang
Keyword(s):  
Brain Computer Interface ◽  
Hand Movement ◽  
Event Related Potentials ◽  
Computer Interface ◽  
Single Trial ◽  
Spatial Filters ◽  
Classification Rate ◽  
Temporal Features ◽  
Related Potentials

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device bypassing conventional motor output pathways of nerves and muscles. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. With respect to the topographic patterns of brain rhythm modulations, the common spatial patterns (CSPs) algorithm has been proven to be very useful to produce subject-specific and discriminative spatial filters; but it didn't consider temporal structures of event-related potentials which may be very important for single-trial EEG classification. In this paper, we propose a new framework of feature extraction for classification of hand movement imagery EEG. Computer simulations on real experimental data indicate that independent residual analysis (IRA) method can provide efficient temporal features. Combining IRA features with the CSP method, we obtain the optimal spatial and temporal features with which we achieve the best classification rate. The high classification rate indicates that the proposed method is promising for an EEG-based brain-computer interface.

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.

Development of emotion classifier based on absolute and differential attributes of averaged signals of visually stimulated event related potentials

2020 ◽  
Vol 42 (11) ◽  
pp. 2057-2067
Author(s):  
Moon Inder Singh ◽  
Mandeep Singh
Keyword(s):  
Event Related Potentials ◽  
Central Line ◽  
Support Vector ◽  
Eeg Signals ◽  
High Arousal ◽  
Brain Signals ◽  
Polynomial Classifier ◽  
Related Potentials ◽  
Daunting Challenge

Analysis and study of abstract human relations have always posed a daunting challenge for technocrats engaged in the field of psychometric analysis. The study on emotion recognition is all the more demanding as it involves integration of abstract phenomenon of emotion causation and emotion appraisal through physiological and brain signals. This paper describes the classification of human emotions into four classes, namely: low valence high arousal (LVHA), high valence high arousal (HVHA), high valence low arousal (HVLA) and low valence low arousal (LVLA) using Electroencephalogram (EEG) signals. The EEG signals have been collected on three EEG electrodes along the central line viz: Fz, Cz and Pz. The analysis has been done on average event related potentials (ERPs) and difference of average ERPs using Support Vector Machine (SVM) polynomial classifier. The four-class classification accuracy of 75% using average ERP attributes and an accuracy of 76.8% using difference of ERPs as attributes has been obtained. The accuracy obtained using differential average ERP attributes is better as compared with the already existing studies.

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