Feature extraction of brain event-related potentials using cubic spline technique

Event related potentials (ERPs) provide insight into the neural activity generated in response to motor, sensory and cognitive processes. Despite the increasing use of ERP data in clinical research little is known about the reliability of human manual ERP labelling methods. Intra-rater and inter-rater reliability were evaluated in five electroencephalography (EEG) experts who labelled the peak negativity of averaged movement related cortical potentials (MRCPs) derived from thirty datasets. Each dataset contained 50 MRCP epochs from healthy people performing cued voluntary or imagined movement, or people with stroke performing cued voluntary movement. Reliability was assessed using the intraclass correlation coefficient and standard error of measurement. Excellent intra- and inter-rater reliability was demonstrated in the voluntary movement conditions in healthy people and people with stroke. In comparison reliability in the imagined condition was low to moderate. Post-hoc secondary epoch analysis revealed that the morphology of the signal contributed to the consistency of epoch inclusion; potentially explaining the differences in reliability seen across conditions. Findings from this study may inform future research focused on developing automated labelling methods for ERP feature extraction and call to the wider community of researchers interested in utilizing ERPs as a measure of neurophysiological change or in the delivery of EEG-driven interventions.

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Feature Extraction of Event-Related Potentials Using Wavelets: An Application to Human Performance Monitoring

Brain and Language ◽

10.1006/brln.1998.2026 ◽

1999 ◽

Vol 66 (1) ◽

pp. 89-107 ◽

Cited By ~ 37

Author(s):

Leonard J. Trejo ◽

Mark J. Shensa

Keyword(s):

Feature Extraction ◽

Performance Monitoring ◽

Human Performance ◽

Event Related Potentials ◽

Related Potentials

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Kernel PCA Feature Extraction of Event-Related Potentials for Human Signal Detection Performance

Artificial Neural Networks in Medicine and Biology - Perspectives in Neural Computing ◽

10.1007/978-1-4471-0513-8_49 ◽

2000 ◽

pp. 321-326 ◽

Cited By ~ 9

Author(s):

Roman Rosipal ◽

Mark Girolami ◽

Leonard J. Trejo

Keyword(s):

Feature Extraction ◽

Signal Detection ◽

Event Related Potentials ◽

Detection Performance ◽

Kernel Pca ◽

Related Potentials ◽

Human Signal

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

Journal of Automatic Control ◽

10.2298/jac0301007v ◽

2003 ◽

Vol 13 (1) ◽

pp. 7-11 ◽

Cited By ~ 5

Author(s):

C.E. Vasios ◽

O.K. Matsopoulos ◽

K.S. Nikita ◽

N. Uzunoglu

Keyword(s):

Feature Extraction ◽

Simulated Annealing ◽

Drug Users ◽

Back Propagation ◽

Event Related Potentials ◽

Back Propagation Algorithm ◽

Related Potentials ◽

And Control ◽

Multivariate Autoregressive

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%.

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