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Functional near infrared spectroscopy based congitive task classification using support vector machines

Functional near infrared spectroscopy based congitive task classification using support vector machines
Abibullaev, BerdakhKang, Won SeokLee, Seung HyunAn, Jin Ung
DGIST Authors
Abibullaev, BerdakhKang, Won SeokAn, Jin Ung
Issued Date
Article Type
Conference Paper
the present study analyzes brain hemodynamic concentration of frontal cortex during four cognitive mental tasks. The analysis procedure consists of three sequential steps. First, the strong brain activation regions have been investigated thoroughly from all subjects in order to And a proper electrode location that generates important brain stimuli. Second, a feature extraction method that is based on wavelet transforms and denoising technique for extraction of important task-relevant features. Finally, support vector machines have been using in the classification of mental tasks with wavelet input coefficients. By applying the methodology for 4-subjects in average we achieved 92 % classification rates. However, the results depend on the type of the task that subject were performing. It is expect that the proposed method can be a basic technology for brain-computer interface by combining wavelets with support vector machines. ©2009 IEEE.
Institute of Electrical and Electronics Engineers
Related Researcher
  • 강원석 Kang, Won-Seok 지능형로봇연구부
  • Research Interests Data Mining & Machine Learning for Text & Multimedia; Brain-Sense-ICTConvergence Computing; Computational Olfaction Measurement; Simulation&Modeling
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Appears in Collections:
Convergence Research Center for Wellness 2. Conference Papers
Division of Intelligent Robotics Brain Robot Augmented InteractioN(BRAIN) Laboratory 2. Conference Papers


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