Cognitive States Prediction with KNN and TomekLinks

Detecting the cognitive states of pilots from their electroencephalogram signals is a challenging task and has different applications. At present there are a number of methods available for the cognitive states prediction. However imbalance in data is ultimately the challenge impacting the analysis performance. Here we use condensed nearest neighbors under-sampling technique to recover the class balance of training data before implementing k-nearest neighbors for classification of the electroencephalogram signals. The underlying cognitive states of pilots can be detected from integration of these techniques that include also data pre-processing and dimensionality reduction. Results of experiments for a benchmark database are reported with improved performance.