Design and Implementation of EEG based Brain Computer Interfaces for Neuroprosthetics and Neurorehabilitation

  • Publsiher : Anonim
  • Release : 28 February 2021
  • ISBN : OCLC:1132289832
  • Page : 137 pages
  • Rating : 4/5 from 21 voters

Download or read online book entitled Design and Implementation of EEG based Brain Computer Interfaces for Neuroprosthetics and Neurorehabilitation written by Reza Abiri and published by Anonim. This book was released on 28 February 2021 with total page 137 pages. Available in PDF, EPUB and Kindle. Get best books that you want by click Get Book Button and Read as many books as you like. Book Excerpt : Brain Computer Interface (BCI) technology motivates interesting and promising results in forward/feedback control consistent with human intention. It holds great promise for advancements in patient care and applications to neuroprosthetics and neurorehabilitation. Here, as forward control in BCI neuroprosthetics, a fundamental testbed for controlling a computer cursor was designed using noninvasive Electroencephalography (EEG) technology. In order to reduce the training time for subjects, a new paradigm called "Imagined Body Kinematics" was adopted in designed experimental protocols. Twenty-eight subjects were trained (about 10 minutes) to perform the cursor control task. The subjects were asked to answer a pre- and a post-questionnaire before and after the experiment, respectively. Several confounding variables were investigated to evaluate their correlation with subjects' performance in training and control task. Thereafter, the developed cursor control platform was applied in Brain Machine Interfaces to control different robotic devices to confirm the potential application of investigated paradigm in neuroprosthetics control. As another interesting area in BCI, a new EEG-based BCI platform was developed to evaluate attentional states in six subjects (as pilot study) and in thirty-eight subjects (as extended study) for feedback control in neurorehabilitation. For the first time, the features from whole brain were employed in two-class classification of attentional states. By introducing a new experimental paradigm for stimuli and neurofeedback, it was discussed how the platform could have the potential application in attention training of people with cognitive deficit.

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