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Abstract

Abstract

Recent advances in computer hardware and signal processing have made possible the use of EEG signals or ‘brain waves’ for communication between humans and computers. Locked-in patients now have a way to communicate with the outside world, but even with the latest techniques, such systems still suffer communication rates of the order of 2-3 tasks/minute. In addition, existing systems are not likely to be designed with flexibility in mind, leading to slow systems that are difficult to improve.

This work classifies different mental tasks through the use of the electroencephalogram (EEG). EEG signals from several subjects have been studied during the performance of five mental tasks: a baseline task for which the subjects were asked to relax as much as possible, a multiplication task for which the subjects were given nontrivial multiplication problem without vocalizing or making any other movements, a letter composing task for which the subjects were instructed to mentally compose a letter without vocalizing (imagine writing a letter to a friend in their head), a rotation task for which the subjects were asked to visualize a particular three-dimensional block figure being rotated about its axis, and a counting task for which the subjects were asked to imagine a blackboard and to visualize numbers being written on the board sequentially.

The work presented here can be viewed as part of a larger project, whose goal is to classify EEG signals belonging to a varied set of mental activities in a real time brain-computer interface, in order to investigate the feasibility of using different mental tasks as a wide communication channel between people and computers.

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/content/papers/10.5339/qfarf.2010.CSP16
2010-12-13
2019-10-23
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References

  1. Yehia 2010. M.M. Yehia. , Mental task discrimination using digital signal processing. , QFARF Proceedings , 2010 , CSP16 .
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http://instance.metastore.ingenta.com/content/papers/10.5339/qfarf.2010.CSP16
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