Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy

@article{Combrisson2015ExceedingCL,
  title={Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy},
  author={Etienne Combrisson and Karim Jerbi},
  journal={Journal of Neuroscience Methods},
  year={2015},
  volume={250},
  pages={126-136}
}
Machine learning techniques are increasingly used in neuroscience to classify brain signals. Decoding performance is reflected by how much the classification results depart from the rate achieved by purely random classification. In a 2-class or 4-class classification problem, the chance levels are thus 50% or 25% respectively. However, such thresholds hold for an infinite number of data samples but not for small data sets. While this limitation is widely recognized in the machine learning field… CONTINUE READING
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