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Results and Conclusion

  • Swagata Das
  • Devashree Tripathy
  • Jagdish Lal Raheja
Chapter
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)

Abstract

This section summarizes the quantitative results of the experiments carried out during the assembling of the final prototype. The major participant algorithm in the system is the neural network that classifies the electroencephalographic signals received from the user’s brain and makes the decision of what was intended through the process. Therefore, the numerical results in this section include the classification accuracies obtained by using various classes of the electroencephalographic signals (based on frequency bands). In the second phase of the results, support vector machine has been used to measure accuracies for multiple subjects while performing various operations. This section also summarizes the same. Five types of operations were directly considered for classification using support vector machine. Classification accuracies obtained were not as good as a neural network, but acceptable. Based on the results in this section, the end algorithm was obtained which gave the highest accuracies in overall performance. Future work will include betterment of the algorithms and inclusion of a higher number of subjects for data collection and corroboration.

Copyright information

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Swagata Das
    • 1
  • Devashree Tripathy
    • 1
  • Jagdish Lal Raheja
    • 1
  1. 1.Machine Vision LaboratoryCSIR-Central Electronics Engineering Research Institute (CSIR-CEERI)PilaniIndia

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