scholarly journals An on-line BCI system for hand movement control using real-time recurrent probabilistic neural network

2009 ◽  
Author(s):  
Mohammad Ahmadi ◽  
Abbas Erfanian
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Probabilistic Neural Network ◽  
Hand Movement ◽  
Movement Control ◽  
On Line ◽  
Bci System

scholarly journals Left and Right Hand Movements EEG Signals Classification Using Wavelet Transform and Probabilistic Neural Network

2015 ◽  
Vol 5 (1) ◽  
pp. 92 ◽  
Author(s):  
A. B. M. Aowlad Hossain ◽  
Md. Wasiur Rahman ◽  
Manjurul Ahsan Riheen
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Probabilistic Neural Network ◽  
Hand Movement ◽  
Hand Movements ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Beta Band ◽  
Right Hand ◽  
Left And Right

Electroencephalogram (EEG) signals have great importance in the area of brain-computer interface (BCI) which has diverse applications ranging from medicine to entertainment. BCI acquires brain signals, extracts informative features and generates control signals from the knowledge of these features for functioning of external devices. The objective of this work is twofold. Firstly, to extract suitable features related to hand movements and secondly, to discriminate the left and right hand movements signals finding effective classifier. This work is a continuation of our previous study where beta band was found compatible for hand movement analysis. The discrete wavelet transform (DWT) has been used to separate beta band of the EEG signal in order to extract features.  The performance of a probabilistic neural network (PNN) is investigated to find better classifier of left and right hand movements EEG signals and compared with classical back propagation based neural network. The obtained results shows that PNN (99.1%) has better classification rate than the BP (88.9%). The results of this study are expected to be helpful in brain computer interfacing for hand movements related bio-rehabilitation applications.

On-Line Bayesian Classifier Design for Measurement Fusion

2012 ◽  
Vol 461 ◽  
pp. 826-829 ◽  
Author(s):  
Li Wei Fong ◽  
Pi Ching Lou ◽  
Kung Ting Lin
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Radial Basis Function Network ◽  
Tracking Accuracy ◽  
Compression Technique ◽  
Adaptive Estimator ◽  
Classifier Design ◽  
Multiple Sensors ◽  
Estimation Scheme ◽  
On Line

A neural-network-based classifier design for adaptive Kalman filtering is introduced to fuse the measurements extracted from multiple sensors to improve tracking accuracy. The proposed method consists of a group of parallel Kalman filters and a classifier based on Radial Basis Function Network (RBFN). By incorporating Markov chain into Bayesian estimation scheme, a RBFN is used as a probabilistic neural network for classification. Based upon data compression technique and on-line classification algorithm, an adaptive estimator to measurement fusion is developed that can handle the switching plant in the multi-sensor environment. The simulation results are presented which demonstrate the effectiveness of the proposed method.

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