A comparison of navigation system based on P300 BCI and SSVEP BCI

A navigation system based on P300 brain computer interface system (BCIs) and steady-state visual evoked potentials (SSVEP) BCIs respectively was designed in this paper. In the experiment, subjects were required to move a ball on the computer screen to the target position by P300 BCI system and SSVEP BCI system. Bayesian linear discriminant analysis (BLDA) is used to detect P300 potentials and canonical correlation analysis (CCA) is used to detect SSVEP. The aim of this paper is to show the drawbacks and advantages of these two BCIs, when they were used in navigation task. The online experimental results show that P300 BCIs is more robust for subjects compared to SSVEP BCIs.

[1]  U. Hoffmann,et al.  A Boosting Approach to P300 Detection with Application to Brain-Computer Interfaces , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[2]  N. Birbaumer,et al.  An auditory oddball (P300) spelling system for brain-computer interfaces. , 2009, Psychophysiology.

[3]  Xingyu Wang,et al.  A new P300 stimulus presentation pattern for EEG-based spelling systems , 2010, Biomedizinische Technik. Biomedical engineering.

[4]  Xingyu Wang,et al.  P300 Chinese input system based on Bayesian LDA , 2010, Biomedizinische Technik. Biomedical engineering.

[5]  H. Hotelling.  Relations Between Two Sets of Variates , 1936 .

[6]  Fusheng Yang,et al.  BCI competition 2003-data set IIb: enhancing P300 wave detection using ICA-based subspace projections for BCI applications , 2004, IEEE Transactions on Biomedical Engineering.

[7]  Ying Sun,et al.  Asynchronous P300 BCI: SSVEP-based control state detection , 2010, 2010 18th European Signal Processing Conference.

[8]  Rajesh P. N. Rao,et al.  Control of a humanoid robot by a noninvasive brain–computer interface in humans , 2008, Journal of neural engineering.

[9]  E. Donchin,et al.  Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. , 1988, Electroencephalography and clinical neurophysiology.

[10]  Xingyu Wang,et al.  An adaptive P300-based control system , 2011, Journal of neural engineering.

[11]  Jonathan R Wolpaw,et al.  Brain–computer interfaces as new brain output pathways , 2007, The Journal of physiology.

[12]  T. Sejnowski,et al.  Analysis and visualization of single‐trial event‐related potentials , 2001, Human brain mapping.

[13]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

[14]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[15]  E. W. Sellers,et al.  Toward enhanced P300 speller performance , 2008, Journal of Neuroscience Methods.

[16]  B. Allison,et al.  BCI Demographics: How Many (and What Kinds of) People Can Use an SSVEP BCI? , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[17]  C. Cinel,et al.  P300-Based BCI Mouse With Genetically-Optimized Analogue Control , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  Wei Wu,et al.  Frequency Recognition Based on Canonical Correlation Analysis for SSVEP-Based BCIs , 2006, IEEE Transactions on Biomedical Engineering.

[19]  Jason Farquhar,et al.  Effects of Stimulus Type and of Error-Correcting Code Design on BCI Speller Performance , 2008, NIPS.

[20]  H. Semlitsch,et al.  A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. , 1986, Psychophysiology.

[21]  A. Lenhardt,et al.  An Adaptive P300-Based Online Brain–Computer Interface , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[22]  F. Piccione,et al.  P300-based brain computer interface: Reliability and performance in healthy and paralysed participants , 2006, Clinical Neurophysiology.

[23]  Helge J. Ritter,et al.  2009 Special Issue: The MindGame: A P300-based brain-computer interface game , 2009 .

[24]  Touradj Ebrahimi,et al.  An efficient P300-based brain–computer interface for disabled subjects , 2008, Journal of Neuroscience Methods.

[25]  Xingyu Wang,et al.  Author's Personal Copy Biomedical Signal Processing and Control Lasso Based Stimulus Frequency Recognition Model for Ssvep Bcis , 2022 .

[26]  Xingyu Wang,et al.  Optimized stimulus presentation patterns for an event-related potential EEG-based brain–computer interface , 2011, Medical & Biological Engineering & Computing.