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논문검색

A P300-Based BCI Classification Algorithm Using Least Square Support Vector Machine

원문정보

Vanitha Narayan Raju, In-Ho Ra, Ravi Sankar

보안공학연구지원센터(IJSEIA) International Journal of Software Engineering and Its Applications Vol.9 No.5 2015.05 pp.247-254
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초록

영어

In this paper, we propose a classification algorithm for P300-based Brain Computer Interface (BCI). Brain Computer Interface (BCI) with P300 speller helps Amyotrophic Lateral Sclerosis (ALS) patients to spell words with the help of their brain signal activities. Amyotrophic Lateral Sclerosis (ALS) also known as Lou Gehrig's disease in which certain nerve cells in brain and spinal cord also called as motor neurons, slowly die. The main goal of the proposed research is to develop classification algorithms for P300-based Brian Computer Interface (BCI). The proposed model can be used to restore basic communicating ability for Amyotrophic Lateral Sclerosis (ALS) patients in a reliable and fast way.

목차

Abstract
 1. Introduction
 2. Method
  A. Machine Learning Algorithms
  B. Least Square Support Vector Machine
 3. Experimental Results
 4. Conclusion
 5. Helpful Hints
 References

저자정보

  • Vanitha Narayan Raju iCONS Research Lab, Department of Electrical Engineering University of South Florida, Tampa, Florida, USA
  • In-Ho Ra Department of Information & Telecommunication Eng. Kunsan National University, South Korea
  • Ravi Sankar iCONS Research Lab, Department of Electrical Engineering University of South Florida, Tampa, Florida, USA

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자료제공 : 네이버학술정보

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