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Investigation of Multidimensional Recording Brain Signal (ECoG) For Estimation of 3D Arm Trajectory

Babolhavaeji, Ali | 2013

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 46902 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Vosughi Vahdat, Bijan
  7. Abstract:
  8. The main idea in this project is investigation of multidimensional recording brain signal (ECoG) for estimation of 3D arm trajectory. First we introduce a general structure with variable blocks, in this structure we have many ways to estimate hand trajectory and obtain different result. By statistical test we find the best state of this structure and apply it on other dtae set trials. Electrocorticography (ECoG) has seen recent use in this regard because it offers a higher spatiotemporal resolution than non-invasive EEG and is less invasive than intracortical microelectrodes and have been shown to contain reliable information about the direction of arm Trajectory and movements. We using signal processing methods to extract best feature that able to perdict movement data from multi dimentional brain signal. In this project we use Time-Frequency Features and Functional connectivity Feature for estimation hand trajectory. These findings indicate that the ECoG is a potential basis for a brain-cpmputer interface (BCI) for application in paralyzed patients to control neuromotor prostheses and for neurorehabilitation in the hopes of providing a means to restore lost motor function. Datasets used in this project: The monkey was tracking food rewards with the hand contralateral to the implant side. ECoG data and motion data were recorded simultaneously during the task (details in the reference). There was no eye tracking. ECoG and motion data were sampled at 1 KHz and 120 Hz, respectively, with time stamps synchronized
  9. Keywords:
  10. Estimation Hand Trajectory ; Brain Signal Decoding ; Electrocorticography (ECOG)

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