Abstract
Objectives
Optimization of Brain-Computer Interface by detecting the minimal number of morphological features of signal that maximize accuracy.
Methods
System of signal processing and morphological features extractor was designed, then the genetic algorithm was used to select such characteristics that maximize the accuracy of the signal’s frequency recognition in offline Brain-Computer Interface (BCI).
Results
The designed system provides higher accuracy results than a previously developed system that uses the same preprocessing methods, however, different results were achieved for various subjects.
Conclusions
It is possible to enhance the previously developed BCI by combining it with morphological features extraction, however, it’s performance is dependent on subject variability.
Research funding: None declared.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Conflict of interest: The authors declare that they have no conflict of interest.
Ethical approval: The conducted research is not related to either human or animal use.
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