Effective Brain–Computer Interface Based on the Adaptive-Rate Processing and Classification of Motor Imagery Tasks
dc.contributor.author | Mian Qaisar, Saeed | |
dc.contributor.author | Oudah, Reem Fuad | |
dc.contributor.author | Nisar, Humaira | |
dc.date.accessioned | 2022-11-03T11:57:24Z | |
dc.date.available | 2022-11-03T11:57:24Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | In the context of cloud-based mobile healthcare systems, continuous multichannel electroencephalogram (EEG) signals acquisition, processing, transmission, and analysis is required. The conventional BCI (Brain to computer interface) systems are time-invariant and can have the disadvantage of capturing redundant information. It leads to a waste of system ressources and power consumption. In this context, this chapter presents an original approach of realizing the adaptive rate signal acquisition and processing chain for the cloud-based BCI. The objective is to achieve a real-time compression gain in order to attain effective EEG signal processing, transmission, and analysis in the context of realizing a proficient cloud-based BCI framework. The signal is enhanced in this study by the use of the method of adaptive rate filtering. Attributes are derived from the conditioned signal using a hybrid method. Afterward, robust classifiers are used to classify different intended EEG signal classes. The performance of the system is studied by using a standard data set of motor imagery tasks. The devised approach is able to achieve appreciable compression gain and computational improvement when compared with the traditional methods. The system also produces a good classification accuracy. | |
dc.identifier.uri | http://hdl.handle.net/20.500.14131/164 | |
dc.description.abstract | In the context of cloud-based mobile healthcare systems, continuous multichannel electroencephalogram (EEG) signals acquisition, processing, transmission, and analysis is required. The conventional BCI (Brain to computer interface) systems are time-invariant and can have the disadvantage of capturing redundant information. It leads to a waste of system ressources and power consumption. In this context, this chapter presents an original approach of realizing the adaptive rate signal acquisition and processing chain for the cloud-based BCI. The objective is to achieve a real-time compression gain in order to attain effective EEG signal processing, transmission, and analysis in the context of realizing a proficient cloud-based BCI framework. The signal is enhanced in this study by the use of the method of adaptive rate filtering. Attributes are derived from the conditioned signal using a hybrid method. Afterward, robust classifiers are used to classify different intended EEG signal classes. The performance of the system is studied by using a standard data set of motor imagery tasks. The devised approach is able to achieve appreciable compression gain and computational improvement when compared with the traditional methods. The system also produces a good classification accuracy. | |
dc.language.iso | en_US | |
dc.publisher | CRC Press | |
dc.title | Effective Brain–Computer Interface Based on the Adaptive-Rate Processing and Classification of Motor Imagery Tasks | |
dc.type | Book chapter | |
dc.source.booktitle | Brain and Behavior Computing | |
dc.contributor.researcher | Electrical and Computer Engineering |