Self-regulating interval type-2 neuro-fuzzy inference system for non-stationary EEG signal processing
Motor-imagery based Brain Computer Interface (BCI) provides a direct communication pathway between the brain and a computer based on the neural activities generated by the brain. Such a technology enables people with physical disabilities to communicate with the external world without using their pe...
Main Author: | Ankit Kumar Das |
---|---|
Other Authors: | Sundaram Suresh |
Format: | Thesis |
Language: | English |
Published: |
2017
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/69545 |
Similar Items
-
Adaptive neuro-fuzzy inference system for predicting alpha band power of EEG during muslim prayer (SALAT)
by: Doufesh, H., et al.
Published: (2016) -
Detection of Epileptic EEG Signal Using Wavelet Transform and Adaptive Neuro-Fuzzy Inference System
by: Khosropanah, Pegah
Published: (2011) -
An evolving interval type-2 fuzzy inference system for renewable energy prediction intervals
by: Nguyen, Trong Trung Anh
Published: (2018) -
Adaptive Neuro-Fuzzy Inference System for Classification of Background EEG Signals from ESES Patients and Controls
by: Zhixian Yang, et al.
Published: (2014-01-01) -
Complex-valued neuro-fuzzy inference system for wind prediction
by: Suresh, Sundaram, et al.
Published: (2013)