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The aim of this study was to design and compare methods for …


Biology Articles » Bioengineering » Evolutionary optimization of classifiers and features for single-trial EEG Discrimination » Conclusion

Conclusion
- Evolutionary optimization of classifiers and features for single-trial EEG Discrimination

The evolutionary design was successful in optimizing classifier parameters and structure, including input features. Higher degrees of tailoring resulted in increased classification accuracies, and non-linear classifiers achieved better results than linear. There was high variation between the resulting features selected for each subject, indicating that a systematic method for accommodating individual variability is useful for single-trial EEG analysis.

Competing interests
The author(s) declare that they have no competing interests.

Authors' contributions
MÅ carried out algorithm implementation and evaluation and drafted the manuscript. JW conceived of the study, and participated in its coordination. Both authors participated in study design and data acquisition and read and approved the final manuscript.

Acknowledgements
This study was supported by the Swedish Research Council (grant 3548), the Sahlgrenska University Hospital (grant ALFGBG 3161), and the foundation of Magnus Bergvall. K. Göthner assisted in data acquisition.


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