Introducing NPXLab 2010: A tool for the analysis and optimization of P300 based brain-computer interfaces

Brain-Computer Interfaces (BCI) are emerging as a powerful tool for providing an alternative way of communication and environment control to severely disabled people. Among these systems, P300-based BCIs are widely diffused as they are easy to manage and do not require a training for the subjects. These systems, however, are still too slow so that they are actually used only by those patients that are unable to control any muscle. It is possible to improve their performances, but many different analyses need to be performed. Here a set of tools are described for the analysis and optimization of this class of BCI protocols that allow increasing the performances of such systems.

[1]  Brendan Z Allison,et al.  Effects of SOA and flash pattern manipulations on ERPs, performance, and preference: implications for a BCI system. , 2006, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[2]  G. Cardarilli,et al.  Performances Evaluation and Optimization of Brain Computer Interface Systems in a Copy Spelling Task , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  J. Wolpaw,et al.  A P300 event-related potential brain–computer interface (BCI): The effects of matrix size and inter stimulus interval on performance , 2006, Biological Psychology.

[4]  J. Wolpaw,et al.  Toward enhanced P 300 speller performance , 2007 .

[5]  Lucia Rita Quitadamo,et al.  Describing Different Brain Computer Interface Systems Through a Unique Model: A UML Implementation , 2008, Neuroinformatics.

[6]  Lucia Rita Quitadamo,et al.  How the NPX data format handles EEG data acquired simultaneously with fMRI. , 2007, Magnetic resonance imaging.

[7]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  S. G. Mason,et al.  A General Framework for Characterizing Studies of Brain Interface Technology , 2005, Annals of Biomedical Engineering.

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

[10]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[11]  G.F. Inbar,et al.  An improved P300-based brain-computer interface , 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  F Babiloni,et al.  Introducing BF++: AC++ framework for cognitive bio-feedback systems design. , 2003, Methods of information in medicine.

[13]  E. Donchin,et al.  Brain-computer interface research at the university of south Florida cognitive psychophysiology laboratory: the P300 speller , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[14]  J. Wolpaw,et al.  A P300-based brain–computer interface for people with amyotrophic lateral sclerosis , 2008, Clinical Neurophysiology.