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Malekmohammadi, Alireza | 2015

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 47700 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Shabany, Mahdi; Mohammadzadeh, Hoda
  7. Abstract:
  8. Make a connection between brain and computer, or Brain Computer Interface (BCI) for broad applications in areas such as medical and gamming has caused the subject to one of the most important and attractive issues in recent decades. From the perspective of pattern recognition, BCI is a classification issue that should receive signals that relate to the certain decisions of the brain and then after processing, it is concluded that the person has thought to what decision. Decisions that taken by individual, is sent from the brain to the body by signals, which is called Electroencephalogram (EEG). The number of these decisions is further, classified it also becomes more difficult. That is why the steps leading to the classification of decisions are very important. In general, parts such as pre-processing, feature extraction, feature selection, feature reduction, and suitable classifier for detecting a decision are needed to solve a BCI problem. Solving BCI problem includes two Phases called train and test sections. In the train part after recording signal and processind BCI steps, the parameters of the test section can be adjusted. In the test part, the new data is recorded and afterward, it is clear that new data belongs to what class according to the parameters in the test section wich Has been set by train data. To implement this, we need an efficient algorithm. The aim of this project, efficient hardware implementation for a BCI algorithm. Analyzing algorithm and its improvement in the direction a efficient hardware architecture is a part of the activities involved in this project. The proposed algorithm has a better accuracy around 10% compared with other algorithms
  9. Keywords:
  10. Feature Extraction ; Classification ; Electroencphalogram Signal ; Brain-Computer Interface (BCI) ; Hardware Implementation

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