Classification Of P300 Signals In Brain-Computer Interface Using Neural Networks With Adjustable Activation Functions
Brain-Computer Interface (BCI) employs brain’s Electroencephalograms (EEG) signals and Event-related potentials (ERP) such as P300 to provide a direct communication between human brain and computer. P300 speller application is a BCI that finds the location of target character using P300 signal...
محفوظ في:
المؤلف الرئيسي: | Aslarzanagh, Seyed Aliakbar Mousavi |
---|---|
التنسيق: | أطروحة |
اللغة: | English |
منشور في: |
2013
|
الموضوعات: | |
الوصول للمادة أونلاين: | http://eprints.usm.my/43934/1/Seyed%20Aliakbar%20Mousavi%20Aslarzanagh24.pdf |
الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
مواد مشابهة
-
Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network
بواسطة: Ting, Fung Fung
منشور في: (2019) -
Hybridization Of Optimized Support
Vector Machine And Artificial Neural
Network For The Diabetic Retinopathy
Classification Problem
بواسطة: Kader, Nur Izzati Ab
منشور في: (2019) -
Covid-19 Misinformation Classification On Twitter In Malaysia Using A Hybrid Adaptive Neuro-Fuzzy Inferences System (Anfis) And Deep Neural Network (Dnn)
بواسطة: Ravichandran, Bhavani Devi
منشور في: (2023) -
Usability and user experience in touchless hand gesture interfaces with mapping
بواسطة: Shanmugam, Mohana
منشور في: (2023) -
White-Matter Lesion Segmentation In Brain Mri Using Adaptive Trimmed Mean Approach
بواسطة: Ong , Kok Haur
منشور في: (2011)