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Brain–machine interfaces are systems that allow the control of a device such as a robot arm through a person’s brain activity; such devices can be used by disabled persons to enhance their life and improve their independence. This paper is an extended version of a work that aims at discriminating between left and right imagined hand movements using a support vector machine (SVM) classifier to control...
Brain-machine interface (BMI) is a system that allows a person to control a device such as a robot arm using only his or her brain activity. This work is aimed at discriminating between left and right imagined hand movements using a Support Vector Machine (SVM) classifier. The main focus here is to search for the best features that efficiently describe the electroencephalogram (EEG) data during such...
P300 is one of the most studied and used event related potentials (ERP) in brain computer interfaces (BCI). The classical oddball paradigm is usually used to evoke the P300 from Electroencephalogram (EEG) signals. However, EEG raw data are noisy which make the P300 detection very difficult. In this paper, we aim to detect the P300 wave as accurate as possible using appropriate feature extraction method...
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