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Brain-Computer Interfaces (BCIs) provide a way to communicate without movement and can offer significant clinical benefits. Electrical brain activity recorded using electroencephalography (EEG) can be automatically interpreted by supervised learning classifiers according to the descriptive features of the signal. This paper investigates the performance of novel feature extraction based on a signal...
In recent years, EEG-based technology has become more popular in producing variety of BMI protocols for wheel chair navigation and communication systems. In this research work, as an initial step towards the development of an intelligent navigation system with a communication aid, a simple EEG data capturing procedure has been introduced using visually evoked potentials. A simple, visually evoked...
Imagined writing is one of the techniques that may improve writing disorder when brain is trained to perform the activity. The imagined writing activity embedded in EEG signal can be extracted and classified using Autoregressive model and Multi Layer Perceptron. This paper describes the classification of imagined writing letters from EEG signals using Multi Layer Perceptron with Autoregression model...
Electroencephalogram (EEG)-based emotion recognition has been an intensely growing field. Yet, how to achieve acceptable accuracy on a practical system with as fewer electrodes as possible is less concerned. This study evaluates a set of subject-independent features, based on differential power asymmetry of symmetric electrode pairs [1], with emphasis on its applicability to subject variability in...
The objective of this work is to explore the potential use of electroencephalography (EEG) as a means for silent communication by way of decoding imagined speech from measured electrical brain waves. EEG signals were recorded at University of California, Irvine (UCI) from 7 volunteer subjects imagining two syllables, /ba/ and /ku/, without speaking or performing any overt actions. Our goal is to classify...
Human motor imagery tasks evoke electroencephalogram (EEG) signal changes. We describe a new technique for the classification of motor imagery electroencephalogram (EEG) recordings. The technique is based on a time-frequency analysis of EEG signals, regarding the relations between the EEG data obtained from the C3/C4 electrodes, the features were reduced according the Fisher distance. This reduced...
A brain-computer interface (BCI) is a system that should in its ultimate form translate a subject's intent into a technical control signal without resorting to the classical neuromuscular communication channels. However, electroencephalogram (EEG ) signal is non-stationary signals, linear analysis methods is not well performance for feature extraction of EEG. Nonlinear analysis methods based kernel...
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