@conference{Daly2010b,

title = {Single trial BCI operation via Wackermann parameters},

author = {Ian Daly and Nitin Williams and Slawomir J. Nasuto and Kevin Warwick and Doug Saddy},

url = {http://www.iandaly.co.uk/wp-content/uploads/2016/01/daly-et-al-Single-trial-BCI-operation-via-Wackermann-parameters.pdf},

year  = {2010},

date = {2010-09-01},

booktitle = {Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP 2010)},

pages = {409-415},

publisher = {IEEE},

address = {Kittila, Finland},

abstract = {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.},

keywords = {BCI, Event-related potentials, Network clustering, Single-trial classification, Wackermann parameters},

pubstate = {published},

tppubtype = {conference}

}