“When I think of Mehdi, I think of one word: ENERGY. Mehdi is a dynamic and supportive Engineering Manager who works well with the team to deliver on quality results on time. He is a boon to any company/team he joins. I am excited to see where Mehdi will land in his life.”
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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Participating in Jason Calacanis's Founder University's 12-week accelerator has been a transformative growth experience for ARKI (Antler W23) The…
Participating in Jason Calacanis's Founder University's 12-week accelerator has been a transformative growth experience for ARKI (Antler W23) The…
Liked by Mehdi Karamnejad
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🔍 The latest blog explores the evolving demands on architectural software and the need for the integration of diverse tools to boost productivity…
🔍 The latest blog explores the evolving demands on architectural software and the need for the integration of diverse tools to boost productivity…
Liked by Mehdi Karamnejad
Experience
Education
Publications
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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 authorsSee publication
Languages
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English
Full professional proficiency
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Persian
Native or bilingual proficiency
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French
Elementary proficiency
Recommendations received
16 people have recommended Mehdi
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🔐 Enhancing Data Security ARKI (Antler W23) 🔐 We're now on track to achieve SOC-2 compliance, reinforcing our commitment to the highest…
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Swagger.vc is coming to Startupfest next week. I'm unveiling a new version of the game with a ton of new content. - Action cards so you can build…
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Connecting with other founders is one of my favorite parts of starting my own business. We’re lucky that Steve Witmer has been using Magic Patterns…
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Happy Canada Day! 🍁 Wishing everyone a joyful and memorable day as we celebrate the beauty, diversity, and unity of our great nation. Let's take a…
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🏠 Bridging Generations of Expertise in Architecture We're reimagining the future of architecture where we have an 💡all-knowing architect…
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