An Online Synchronous Brain Wave Signal Pattern Classifier with Parallel Processing Optimization for Embedded System Implementation

Commercial Brain Computer Interface applications are currently expanding due to the success of widespread dis-semination of low cost devices. Reducing the cost of a traditional system requires appropriate resources, such as proper software tools for signal processing and characterization. In this paper, a methodology for classifying a set of attention and meditation brain wave signal patterns is presented by means of unsupervised signal feature clustering with batch Self-Organizing Maps (b-SOM) and supervised classification by Support Vector Machine (SVM). Previous research on this matter did not combine both methods and also required an important amount of computation time. With the use of a small square neuron grid by b-SOM and an RBF kernel SVM, a well delimited classifier was obtained. The recognition rate was 70% after parameter tuning. In terms of optimization, the parallel b-SOM algorithm reduced drastically the computation time, allowing online clustering and classification for full length input data.

@article{Senzio-Savino2017,
  title     = {An Online Synchronous Brain Wave Signal Pattern Classifier with Parallel Processing Optimization for Embedded System Implementation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {8},
  number    = {1},
  year      = {2017},
  publisher = {The Science and Information Organization},
  author    = {Bruno Senzio-Savino and Mohammad Reza Alsharif and Carlos E. Gutierrez and Kamaledin Setarehdan},
  doi       = {10.14569/IJACSA.2017.080152},
  url       = {https://doi.org/10.14569/IJACSA.2017.080152}
}