Real-time microcontroller based brain computer interface for mental task classifications using wireless eeg signals from two channels

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Conference Proceeding
Proceedings of the 9th IASTED International Conference on Biomedical Engineering, BioMed 2012, 2012, pp. 336 - 342
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A brain computer interface (BCI) using electroencephalography (EEG) to measure brain activities could provide severely disabled people with alternative means of control and communication. In a practical system, portability, low power and real-time operation are the keys requirements. This could be accomplished by using an embedded microcontroller based system. The main contribution of this paper shows the development of a real-time BCI prototype system to classify groups of mental tasks based on such a system. The relevant mental tasks used are mental arithmetic, figure rotation, letter composing, visual counting and eyes closed action. Moreover, the system uses a separate two channels only wireless EEG measurement module with the active positions at parietal and occipital lobes. The result shows the wireless EEG module has a good performance with a CMRR of more than 95dB. In addition, the size of the module is small (36×36 mm 2) and current consumption is low enough to operate off a 3V coin cell battery. The mental tasks were classified using a feed-forward back-propagation artificial neural network (ANN) trained with the Levenberg-Marquardt algorithm. An accuracy of around 70% was achieved with bit rate at around 0.4 bits/trial for six subjects tested to select between three separate mental tasks.
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