Brain Signal Classification using Genetic Algorithm for Right-Left Motion Pattern

Brain signals or EEG are non-stationary signals and are difficult to analyze visually. The brain signal has five waves alpha, beta, delta, gamma, and theta. The five waves have their frequency to describe the level of attention, alertness, character and external stimuli. The five waves can be used to analyze stimulation patterns when turning left and right. Giving weight to the five brain waves utilizes genetic algorithms to get one signal. Genetic algorithms can be used to find the best signal for classification. In this paper, the EEG signal will be classified to determine the right or left movement pattern. After combining the five brain waves with genetic algorithms is then classified using the Logistic Regression, Linear Discriminant Analysis, K-Neighbors Classifier, Decision Tree, Naïve Bayes Gaussian, and Support Vector Machine. From the six methods above that have the highest accuracy is 56% and SVM is a method that has better accuracy than others on this problem.

@article{Rahmad2018,
  title     = {Brain Signal Classification using Genetic Algorithm for Right-Left Motion Pattern},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {9},
  number    = {11},
  year      = {2018},
  publisher = {The Science and Information Organization},
  author    = {Cahya Rahmad and Rudy Ariyanto and Dika Rizky Yunianto},
  doi       = {10.14569/IJACSA.2018.091134},
  url       = {https://doi.org/10.14569/IJACSA.2018.091134}
}