@InProceedings{8553102,
  author = {M. J. Abdulaal and A. J. Casson and P. Gaydecki},
  booktitle = {2018 26th European Signal Processing Conference (EUSIPCO)},
  title = {Performance of Nested vs. Non-Nested SVM Cross-Validation Methods in Visual BCI: Validation Study},
  year = {2018},
  pages = {1680-1684},
  abstract = {Brain-Computer Interface (BCI) is a technology that utilizes brainwaves to link the brain with external machines for either medical analysis, or to improve quality of life such as control and communication for people affected with paralysis. The performance of BCI systems depends on classification accuracy, which influences the Information Transfer Rate. This motivates researchers to improve their classification accuracy as best possible. A bias problem in reporting accuracies by using non-nested cross-validation methods was thought to increase accuracy. The aim of this paper was to validate and quantify such a concept by using a low-cost commercial EEG recorder to classify visually evoking face vs scrambled pictures, and report high accuracy using non-nested cross validation. The algorithm employed Independent Component Analysis followed by feature extraction with sample covariance matrices. The data were then classified using Support Vector Machines. The accuracy was tested with nested and non-nested cross-validation methods; accuracies obtained were 63% and 76%, respectively.},
  keywords = {brain-computer interfaces;covariance matrices;electroencephalography;feature extraction;independent component analysis;medical signal processing;pattern classification;support vector machines;brain-computer interface;information transfer rate;scrambled pictures;independent component analysis;nested SVM cross-validation methods;brainwaves;paralysis;feature extraction;covariance matrices;support vector machines;visually evoking face classification;low-cost commercial EEG recorder;medical analysis;external machines;visual BCI;nonnested SVM cross-validation methods;Electroencephalography;Electrodes;Covariance matrices;Software;Face;Testing;Support vector machines},
  doi = {10.23919/EUSIPCO.2018.8553102},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2018/papers/1570437154.pdf},
}

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This motivates researchers to improve their classification accuracy as best possible. A bias problem in reporting accuracies by using non-nested cross-validation methods was thought to increase accuracy. The aim of this paper was to validate and quantify such a concept by using a low-cost commercial EEG recorder to classify visually evoking face vs scrambled pictures, and report high accuracy using non-nested cross validation. The algorithm employed Independent Component Analysis followed by feature extraction with sample covariance matrices. The data were then classified using Support Vector Machines. 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