Investigating the effects of visual distractors on the performance of a motor imagery brain-computer interface

[1]  R. Homan,et al.  Cerebral location of international 10-20 system electrode placement. , 1987, Electroencephalography and clinical neurophysiology.

[2]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[3]  S. Makeig Auditory event-related dynamics of the EEG spectrum and effects of exposure to tones. , 1993, Electroencephalography and clinical neurophysiology.

[4]  J. Wolpaw,et al.  Brain-computer communication: unlocking the locked in. , 2001, Psychological bulletin.

[5]  S. Makeig,et al.  Mining event-related brain dynamics , 2004, Trends in Cognitive Sciences.

[6]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[7]  E. Viding,et al.  Load theory of selective attention and cognitive control. , 2004, Journal of experimental psychology. General.

[8]  J. Wolpaw,et al.  Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements , 2004, Brain Topography.

[9]  J. Pineda The functional significance of mu rhythms: Translating “seeing” and “hearing” into “doing” , 2005, Brain Research Reviews.

[10]  Clemens Brunner,et al.  Better than random? A closer look on BCI results , 2008 .

[11]  M. Hallett,et al.  A high performance sensorimotor beta rhythm-based brain–computer interface associated with human natural motor behavior , 2008, Journal of neural engineering.

[12]  N. Lavie Attention, Distraction, and Cognitive Control Under Load , 2010 .

[13]  Kongqiao Wang,et al.  Learning optimal spatial filters by discriminant analysis for brain-computer-interface , 2012, Neurocomputing.

[14]  Paul McCullagh,et al.  Realistic Expectations with Brain Computer Interfaces , 2012 .

[15]  Jonathan Becedas,et al.  Brain–Machine Interfaces: Basis and Advances , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  T. Chau,et al.  A Review of EEG-Based Brain-Computer Interfaces as Access Pathways for Individuals with Severe Disabilities , 2013, Assistive technology : the official journal of RESNA.

[17]  Bin He,et al.  Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives , 2014, IEEE Transactions on Biomedical Engineering.

[18]  Gernot R. Müller-Putz,et al.  Effects of mental workload and fatigue on the P300, alpha and theta band power during operation of an ERP (P300) brain–computer interface , 2014, Biological Psychology.

[19]  Na Lu,et al.  Motor imagery classification via combinatory decomposition of ERP and ERSP using sparse nonnegative matrix factorization , 2015, Journal of Neuroscience Methods.

[20]  T. Chau,et al.  Effects of user mental state on EEG-BCI performance , 2015, Front. Hum. Neurosci..

[21]  Fabio Babiloni,et al.  Investigation of the effect of EEG-BCI on the simultaneous execution of flight simulation and attentional tasks , 2015, Medical & Biological Engineering & Computing.

[22]  S. G. Ponnambalam,et al.  Multi-objective genetic algorithm as channel selection method for P300 and motor imagery data set , 2015, Neurocomputing.

[23]  Stephanie Brandl,et al.  Brain–computer interfacing under distraction: an evaluation study , 2016, Journal of neural engineering.

[24]  Dario Farina,et al.  Classification of EEG signals to identify variations in attention during motor task execution , 2017, Journal of Neuroscience Methods.