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dc.contributor.authorRoja, Saima Tasfia
dc.contributor.authorBin Rafique, Sayem
dc.contributor.authorRhaman, Md. Asikur
dc.contributor.authorSakib, Nazmus
dc.contributor.authorIslam, Md Kafiul
dc.date.accessioned2024-06-18T04:36:17Z
dc.date.available2024-06-18T04:36:17Z
dc.date.issued2024-05-03
dc.identifier.citationS. T. Roja, S. B. Rafique, M. A. Rhaman, N. Sakib and M. K. Islam, "EEG-based Mouse Cursor Control using Motor Imagery Brain-Computer Interface," 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT), Dhaka, Bangladesh, 2024, pp. 1042-1047, doi: 10.1109/ICEEICT62016.2024.10534379.en_US
dc.identifier.isbn979-8-3503-8577-9
dc.identifier.issn2769-5700
dc.identifier.urihttp://ar.iub.edu.bd/handle/11348/949
dc.description.abstractBrain-computer interface (BCI) is a system that collects, analyzes, and transforms brain signals into commands. The brain experiences repetitive, oscillatory electrical changes caused by these activities that have a very low voltage of only a few microvolts (µV).The term electroencephalogram (EEG) refers to the non-invasive recording of this electrical activity from the scalp. The signals are then analyzed in a computer to identify the desired action after signal acquisition. Relevant features are gathered and translated into commands that operate an output device or carry out the command. The user is subsequently provided with feedback to confirm that the command has been carried out correctly This work focuses on the development and implementation of a Mouse Cursor Control system using Motor Imagery (MI) BCI, using data recorded with the Emotiv EPOC+ headset and processed using our algorithm in the MATLAB software. While similar works do exist, most tend to focus on one or more aspects of data processing such as classification. We acquired the data from subjects ourselves, and after processing the data using our algorithm, the system was implemented, and the cursor was moved. This makes our system a semi-online system, as opposed to offline systems. The only limitation of our system is that the system is implemented in semi-real time. Furthermore, accuracy was tested for different frequency bands and the highest accuracy of 93.60% was achieved using the offline dataset.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.ispartofseries2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT);
dc.subjectBCIen_US
dc.subjectEEGen_US
dc.subjectMotor Imageryen_US
dc.subjectMouse Cursoren_US
dc.subjectHuman-Computer Interactionen_US
dc.subjectSVMen_US
dc.titleEEG-based Mouse Cursor Control using Motor Imagery Brain-Computer Interfaceen_US
dc.typeArticleen_US


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