Single trial BCI operation via Wackermann parameters
Daly, I., Williams, N., Nasuto, S. J., Warwick, K. and Saddy, D. (2010) Single trial BCI operation via Wackermann parameters. In: 2010 IEEE International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, pp. 409-414. ISBN 9781424478750
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To link to this article DOI: 10.1109/MLSP.2010.5588992
Accurate single trial P300 classification lends itself to fast and accurate control of Brain Computer Interfaces (BCIs). Highly accurate classification of single trial P300 ERPs is achieved by characterizing the EEG via corresponding stationary and time-varying Wackermann parameters. Subsets of maximally discriminating parameters are then selected using the Network Clustering feature selection algorithm and classified with Naive-Bayes and Linear Discriminant Analysis classifiers. Hence the method is assessed on two different data-sets from BCI competitions and is shown to produce accuracies of between approximately 70% and 85%. This is promising for the use of Wackermann parameters as features in the classification of single-trial ERP responses.
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