Classification of electroencephalography (EEG) signals for different mental activities using Kullback Leibler (KL) divergence

@article{Gupta2009ClassificationOE,
  title={Classification of electroencephalography (EEG) signals for different mental activities using Kullback Leibler (KL) divergence},
  author={Anjum Gupta and Shibin Parameswaran and Cheng-Han Lee},
  journal={2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
  year={2009},
  pages={1697-1700}
}
Automatic classification of electroencephalography (EEG) signals, for different type of mental activities, is an active area of research and has many applications such as brain computer interface (BCI) and medical diagnoses. We introduce a simple yet effective way to use Kullback-Leibler (KL) divergence in the classification of raw EEG signals. We show that k-nearest neighbor (k-NN) algorithm with KL divergence as the distance measure, when used using our feature vectors, gives competitive… CONTINUE READING
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