Abstract
The eye events (eye blink, eyes close and eyes open) are usually considered as biological artifacts in the electroencephalographic (EEG) signal. One can con-trol his or her eye blink by proper training and hence can be used as a control signal in Brain Computer Interface (BCI) applications. Support vector ma-chines (SVM) in recent years proved to be the best classification tool. A comparison of SVM with the Artificial Neural Network (ANN) always provides fruitful results. A one-against-all SVM and a multi-layer ANN is trained to detect the eye events. A com-parison of both is made in this paper.
Highlights
The electroencephalogram, or EEG, consists of the electrical activity of relatively large neuronal populations that can be recorded from the scalp
The ‘trainlm’ is often the fastest backpropagation algorithm in the neural network toolbox, and is highly recommended as a first-choice supervised algorithm, it does require more memory than other algorithms
The Feed Forward Back Propagation (FFBP) network is trained in just 23 seconds using the trainlm algorithm and is faster than Artificial Neural Network (ANN) that uses other training algorithms
Summary
The electroencephalogram, or EEG, consists of the electrical activity of relatively large neuronal populations that can be recorded from the scalp. There are five major brain waves distinguished by their different frequency ranges. These frequency bands from low to high frequencies respectively are called delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ). Eye events (eye blink, eyes close and eyes open) are normally considered as physiological artifacts in the EEG. Eye blink signals can be used in BCI applications like virtual keyboard while the eye close and eyes open signals can be used for folding and opening electric foldable hospital beds. The aim of Support Vector classification is to devise a computationally efficient way of learning ‘good’ separating hyperplanes in a high dimensional feature space, where ‘good’ hyperplanes are ones optimizing the generalization bounds, and ‘computationally efficient’ mean algorithms able to deal with sample sizes of the order of 100000 instances [8]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
