Machine Learning
Brain signals: feature extraction and classification using rough set methods
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
The rough set exploration system
Transactions on Rough Sets III
A POMDP approach to P300-based brain-computer interfaces
Proceedings of the 15th international conference on Intelligent user interfaces
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The goal of P300 wave detection is to extract relevant features from the huge number of electrical signals and to detect the P300 component accurately. This paper introduces a modified approach to P300 wave detection combined with an application of rough set methods and non-rough set based methods to classify P300 signals. The modifications include an averaging method using Mexican hat wavelet coefficients to extract features of signals. The data set has been expanded to include signals from six words and a total of 3960 objects. Experiments with a variety of classifiers were performed. The signal data analysis includes comparisons of error rates, true positives and false negatives performed using a paired t-test. It has been found that the false negatives are better indicators of efficacy of the feature extraction method rather than error rate due to the nature of the signal data. The contribution of this paper is an in-depth study P300 wave detection using a modified averaging method for feature extraction together with rough set-based classification on an expanded data set.