TY - JOUR
T1 - How Visual Stimuli Evoked P300 is Transforming the Brain-Computer Interface Landscape
T2 - A PRISMA Compliant Systematic Review
AU - Kalra, Jai
AU - Mittal, Prashasti
AU - Mittal, Nirmiti
AU - Arora, Abhishek
AU - Tewari, Utkarsh
AU - Chharia, Aviral
AU - Upadhyay, Rahul
AU - Kumar, Vinay
AU - Longo, Luca
N1 - Publisher Copyright:
© 2001-2011 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Brain-computer interface
KW - P300
KW - deep learning
KW - electroencephalogram
KW - event related potential
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85149169816&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2023.3246588
DO - 10.1109/TNSRE.2023.3246588
M3 - Article
C2 - 37027569
AN - SCOPUS:85149169816
SN - 1534-4320
VL - 31
SP - 1429
EP - 1439
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
ER -