How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review
Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics,...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10049170/ |
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| author | Jai Kalra Prashasti Mittal Nirmiti Mittal Abhishek Arora Utkarsh Tewari Aviral Chharia Rahul Upadhyay Vinay Kumar Luca Longo |
| author_facet | Jai Kalra Prashasti Mittal Nirmiti Mittal Abhishek Arora Utkarsh Tewari Aviral Chharia Rahul Upadhyay Vinay Kumar Luca Longo |
| author_sort | Jai Kalra |
| collection | DOAJ |
| description | Non-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning. |
| format | Article |
| id | doaj-art-305daea33f7643c9b8529e554f7e7b51 |
| institution | OA Journals |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-305daea33f7643c9b8529e554f7e7b512025-08-20T02:25:16ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102023-01-01311429143910.1109/TNSRE.2023.324658810049170How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic ReviewJai Kalra0https://orcid.org/0000-0003-4420-7903Prashasti Mittal1https://orcid.org/0000-0002-3414-5107Nirmiti Mittal2https://orcid.org/0000-0001-7641-2229Abhishek Arora3https://orcid.org/0000-0002-3202-4387Utkarsh Tewari4https://orcid.org/0000-0002-6077-6260Aviral Chharia5https://orcid.org/0000-0003-4662-9747Rahul Upadhyay6https://orcid.org/0000-0003-0476-4529Vinay Kumar7https://orcid.org/0000-0001-9086-4782Luca Longo8https://orcid.org/0000-0002-2718-5426Department of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaDepartment of Mechanical Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaDepartment of Electronics and Communication Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, IndiaArtificial Intelligence and Cognitive Load Research Laboratory, Applied Intelligence Research Centre, School of Computer Science, Technological University Dublin, Dublin 7, IrelandNon-invasive Visual Stimuli evoked-EEG-based P300 BCIs have gained immense attention in recent years due to their ability to help patients with disability using BCI-controlled assistive devices and applications. In addition to the medical field, P300 BCI has applications in entertainment, robotics, and education. The current article systematically reviews 147 articles that were published between 2006-2021*. Articles that pass the pre-defined criteria are included in the study. Further, classification based on their primary focus, including article orientation, participants’ age groups, tasks given, databases, the EEG devices used in the studies, classification models, and application domain, is performed. The application-based classification considers a vast horizon, including medical assessment, assistance, diagnosis, applications, robotics, entertainment, etc. The analysis highlights an increasing potential for P300 detection using visual stimuli as a prominent and legitimate research area and demonstrates a significant growth in the research interest in the field of BCI spellers utilizing P300. This expansion was largely driven by the spread of wireless EEG devices, advances in computational intelligence methods, machine learning, neural networks and deep learning.https://ieeexplore.ieee.org/document/10049170/Brain–computer interfaceelectroencephalogramP300event related potentialmachine learningdeep learning |
| spellingShingle | Jai Kalra Prashasti Mittal Nirmiti Mittal Abhishek Arora Utkarsh Tewari Aviral Chharia Rahul Upadhyay Vinay Kumar Luca Longo How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review IEEE Transactions on Neural Systems and Rehabilitation Engineering Brain–computer interface electroencephalogram P300 event related potential machine learning deep learning |
| title | How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review |
| title_full | How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review |
| title_fullStr | How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review |
| title_full_unstemmed | How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review |
| title_short | How Visual Stimuli Evoked P300 is Transforming the Brain–Computer Interface Landscape: A PRISMA Compliant Systematic Review |
| title_sort | how visual stimuli evoked p300 is transforming the brain x2013 computer interface landscape a prisma compliant systematic review |
| topic | Brain–computer interface electroencephalogram P300 event related potential machine learning deep learning |
| url | https://ieeexplore.ieee.org/document/10049170/ |
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