Mehdi Karamnejad

Mehdi Karamnejad

Vancouver, British Columbia, Canada
2K followers 500+ connections

About

Tell me about the change you want to make in the world!

Check out my projects and website and if you see anything that interests you, let's chat!

https://www.mehdi.tech

Activity

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Experience

  • Stealth Startup Graphic

    Stealth Startup

    Vancouver, British Columbia, Canada

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    Vancouver, British Columbia, Canada

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    Toronto, Ontario, Canada

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    Vancouver, British Columbia, Canada

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    Vancouver, British Columbia, Canada

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    Vancouver, Canada Area

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    Vancouver, British Columbia, Canada

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    Vancouver, Canada Area

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    Vancouver, Canada Area

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    Vancouver, Canada Area

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    Surrey Campus

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    Tehran, Iran

Education

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Publications

  • Virtual Reality as Analgesia: An alternative approach for managing chronic pain

    IGI Journal

    A chapter in the upcoming special edition of IGI journal "Creative Interfaces and Computer Graphics (IJCICG).

    Other authors
  • Multiple classifier system for EEG signal classification with application to brain–computer interfaces

    NEURAL COMPUTING & APPLICATIONS

    In this paper, we demonstrate the use of a multiple classifier system for classification of electroencephalogram (EEG) signals. The main purpose of this paper is to apply several approaches to classify motor imageries originating from the brain in a more robust manner. For this study, dataset II from BCI competition III was used. To extract features from the brain signal, discrete wavelet transform decomposition was used. Then, several classic classifiers were implemented to be utilized in the…

    In this paper, we demonstrate the use of a multiple classifier system for classification of electroencephalogram (EEG) signals. The main purpose of this paper is to apply several approaches to classify motor imageries originating from the brain in a more robust manner. For this study, dataset II from BCI competition III was used. To extract features from the brain signal, discrete wavelet transform decomposition was used. Then, several classic classifiers were implemented to be utilized in the multiple classifier system, which outperforms the reported results of other proposed methods on the dataset. Also, a variety of classifier combination methods along with genetic algorithm feature selection were evaluated and compared in order to diminish classification error. Our results suggest that an ensemble system can be employed to boost EEG classification accuracy.

    Other authors
    See publication

Languages

  • English

    Full professional proficiency

  • Persian

    Native or bilingual proficiency

  • French

    Elementary proficiency

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