GDL

Brain Computer Interface with Genetic Algorithm

2012
Journal from gdlhub / 2012-06-22 16:48:54
Oleh : Abdolreza. Asadi Ghanbari, Ali Broumandnia, Hamidreza Navidi, Ali.Ahmadi , STIKOM Dinamika Bangsa Jambi
Dibuat : 2012-06-22, dengan 1 file

Keyword : Brain Computer Interfaces, Redundancy Reduction, Genetic Algorithm, artifact
Subjek : Brain Computer Interface with Genetic Algorithm
Url : http://esjournals.org/journaloftechnology/archive/vol2no1/vol2no1_11.pdf
Sumber pengambilan dokumen : Internet

Brain Computer Interfaces (BCIs) measure brain signals of brain activity intentionally and unintentionally induced by the

user, and thus provide a promising communication channel that does not depend on the brain‟s normal output pathway

consisting of peripheral nerves and muscles. Present-day Brain Computer Interfaces determine the intent of the user from a

variety of different electrophysiological signals. They translate these signals in real-time commands that operate a

computer display or other device. Successful operation requires that the user encode commands in these signals and that

the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially

and continually so as to ensure stable performance. Current BCIs have low information transfer rates (e.g. up to 10–25

bits/min). This is limited capacity for many possible applications of BCI technology, such as neuroprosthesis control, this

device require higher information transfer rates. In non-invasive BCI, Signals from the brain are acquired by channels (i.e.

electrodes) on the scalp. In new BCI systems for increase accuracy, increased number of electrodes. In this case the

increased number of electrodes causes a non-linear increase in computational complexity (i.e. decrease transfer rate). This

article used Genetic Algorithm for select the effective number of electrodes and Redundancy Reduction.

Deskripsi Alternatif :

Brain Computer Interfaces (BCIs) measure brain signals of brain activity intentionally and unintentionally induced by the

user, and thus provide a promising communication channel that does not depend on the brain‟s normal output pathway

consisting of peripheral nerves and muscles. Present-day Brain Computer Interfaces determine the intent of the user from a

variety of different electrophysiological signals. They translate these signals in real-time commands that operate a

computer display or other device. Successful operation requires that the user encode commands in these signals and that

the BCI derive the commands from the signals. Thus, the user and the BCI system need to adapt to each other both initially

and continually so as to ensure stable performance. Current BCIs have low information transfer rates (e.g. up to 10–25

bits/min). This is limited capacity for many possible applications of BCI technology, such as neuroprosthesis control, this

device require higher information transfer rates. In non-invasive BCI, Signals from the brain are acquired by channels (i.e.

electrodes) on the scalp. In new BCI systems for increase accuracy, increased number of electrodes. In this case the

increased number of electrodes causes a non-linear increase in computational complexity (i.e. decrease transfer rate). This

article used Genetic Algorithm for select the effective number of electrodes and Redundancy Reduction.

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