Classification of P300 component in single trial event related potentials

2002 ◽  
Author(s):  
H.O. Gulcar ◽  
Y.K. Yilmaz ◽  
T. Demiralp
Keyword(s):  
Event Related Potentials ◽  
Single Trial ◽  
P300 Component ◽  
Related Potentials

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.

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.

Extracting Spatio-temporal Feature for Classification of Event-related Potentials*

2011 ◽  
Vol 38 (9) ◽  
pp. 866-871 ◽  
Author(s):  
Zhi-Hua HUANG ◽  
Ming-Hong LI ◽  
Yuan-Ye MA ◽  
Chang-Le ZHOU
Keyword(s):  
Event Related Potentials ◽  
Related Potentials ◽  
Spatio Temporal ◽  
Temporal Feature
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