Abstract
The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time.
Highlights
Brain-Computer Interface (BCI) is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements [1,2,3,4]
All estimators were calculated using (1) through (6) for the details cD2, cD3and cD4 of each instance.At the end of these calculations, 9 features of each estimator (3 channels 3 details) were generated for each Coiflets wavelet. These features were numerically represented in a format that is suitable for use with Support Vector Machines (SVMs) and Neural Networks (NNs) algorithms [35, 36] as described
The system extracts the features needed for the SVM and NN decision rules and provides near-real time actions
Summary
Brain-Computer Interface (BCI) is a device that enables the use of the brain’s neural activity to communicate with others or to control machines, artificial limbs, or robots without direct physical movements [1,2,3,4]. BCI captures EEG signals in conjunction with a specific user activity uses different signal processing algorithms to translate these records into control commands for different machine and computer applications [7]. BCI was known for its popular use in helping disabled individuals by providing a new channel of communication with the external environment and offering a feasible tool to control artificial limbs [8]. We integrated these systems into one hybrid application that is based on the imagined fists and feet movements
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
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 callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
