A Brain-Computer Interface Based on Abstract Visual and Auditory Imagery: Evidence for an Effect of Artistic Training

Various kinds of mental imagery have been employed in controlling a brain-computer interface (BCI). BCIs based on mental imagery are typically designed for certain kinds of mental imagery, e.g., motor imagery, which have known neurophysiological correlates. This is a sensible approach because it is much simpler to extract relevant features for classifying brain signals if the expected neurophysiological correlates are known beforehand. However, there is significant variance across individuals in the ability to control different neurophysiological signals, and insufficient empirical data is available in order to determine whether different individuals have better BCI performance with different types of mental imagery. Moreover, there is growing interest in the use of new kinds of mental imagery which might be more suitable for different kinds of applications, including in the arts.

[1]  G. McArthur,et al.  Validation of the Emotiv EPOC® EEG gaming system for measuring research quality auditory ERPs , 2013, PeerJ.

[2]  Aleksandra Vuckovic,et al.  Using a motor imagery questionnaire to estimate the performance of a Brain–Computer Interface based on object oriented motor imagery , 2013, Clinical Neurophysiology.

[3]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

[5]  Gary E. Birch,et al.  A brain-controlled switch for asynchronous control applications , 2000, IEEE Trans. Biomed. Eng..

[6]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[7]  Jaime Gómez Gil,et al.  Brain Computer Interfaces, a Review , 2012, Sensors.

[8]  N. Birbaumer,et al.  Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. , 2001, Archives of physical medicine and rehabilitation.

[9]  Christian Mühl,et al.  Flaws in current human training protocols for spontaneous Brain-Computer Interfaces: lessons learned from instructional design , 2013, Front. Hum. Neurosci..

[10]  Brendan Z. Allison,et al.  Could Anyone Use a BCI? , 2010, Brain-Computer Interfaces.

[11]  Hsuan-Tien Lin,et al.  A note on Platt’s probabilistic outputs for support vector machines , 2007, Machine Learning.

[12]  John Platt,et al.  Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods , 1999 .

[13]  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.

[14]  Sriram Subramanian,et al.  Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns , 2015, PloS one.

[15]  C. Neuper,et al.  The effect of distinct mental strategies on classification performance for brain-computer interfaces. , 2012, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[16]  G. Pfurtscheller,et al.  Motor imagery activates primary sensorimotor area in humans , 1997, Neuroscience Letters.

[17]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[18]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[19]  G. Pfurtscheller,et al.  Optimal spatial filtering of single trial EEG during imagined hand movement. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[20]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[21]  J. Mourino,et al.  Asynchronous BCI and local neural classifiers: an overview of the adaptive brain interface project , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[23]  José del R. Millán,et al.  An Introduction to Brain-Computer Interfacing , 2007 .

[24]  D H Brainard,et al.  The Psychophysics Toolbox. , 1997, Spatial vision.

[25]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[26]  Adriane B. Randolph.  Not All Created Equal: Individual-Technology Fit of Brain-Computer Interfaces , 2012, 2012 45th Hawaii International Conference on System Sciences.

[27]  Klaus-Robert Müller,et al.  Machine-Learning-Based Coadaptive Calibration for Brain-Computer Interfaces , 2011, Neural Computation.

[28]  Gernot R. Müller-Putz,et al.  Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability , 2015, PloS one.

[29]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[30]  A. Frolov,et al.  Brain-Computer Interface Based on Generation of Visual Images , 2011, PloS one.

[31]  Gary E. Birch,et al.  Brain-computer interface design for asynchronous control applications: improvements to the LF-ASD asynchronous brain switch , 2004, IEEE Transactions on Biomedical Engineering.

[32]  Feng Wan,et al.  Implementation of SSVEP based BCI with Emotiv EPOC , 2012, 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS) Proceedings.

[33]  Klaus-Robert Müller,et al.  Co-adaptive calibration to improve BCI efficiency , 2011, Journal of neural engineering.

[34]  Maureen Clerc,et al.  An analysis of performance evaluation for motor-imagery based BCI , 2013, Journal of neural engineering.

[35]  Chih-Jen Lin,et al.  Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..

[36]  Melody Moore Jackson,et al.  Individual Characteristics and Their Effect on Predicting Mu Rhythm Modulation , 2010, Int. J. Hum. Comput. Interact..

[37]  Fuhui Long,et al.  Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[38]  Scott Makeig,et al.  Emotion Recognition from EEG during Self-Paced Emotional Imagery , 2013, 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction.

[39]  F. Carrino,et al.  A self-paced BCI system to control an electric wheelchair: Evaluation of a commercial, low-cost EEG device , 2012, 2012 ISSNIP Biosignals and Biorobotics Conference: Biosignals and Robotics for Better and Safer Living (BRC).

[40]  G. Pfurtscheller,et al.  Designing optimal spatial filters for single-trial EEG classification in a movement task , 1999, Clinical Neurophysiology.

[41]  Moritz Grosse-Wentrup,et al.  Multisubject Learning for Common Spatial Patterns in Motor-Imagery BCI , 2011, Comput. Intell. Neurosci..

[42]  D Thompson,et al.  Optimizing the P300-based brain–computer interface: current status, limitations and future directions , 2011, Journal of neural engineering.

[43]  S. Swinnen,et al.  Kinesthetic, but not visual, motor imagery modulates corticomotor excitability , 2005, Experimental Brain Research.

[44]  Christa Neuper,et al.  Neurofeedback Training for BCI Control , 2009 .

[45]  J. Zygierewicz,et al.  Asynchronous BCI Based on Motor Imagery With Automated Calibration and Neurofeedback Training , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[46]  Benjamin Schrauwen,et al.  A P300 BCI for the Masses: Prior Information Enables Instant Unsupervised Spelling , 2012, NIPS.

[47]  David Ewins,et al.  The Emotiv EPOC neuroheadset: an inexpensive method of controlling assistive technologies using facial expressions and thoughts? , 2011 .

[48]  K. Müller,et al.  Psychological predictors of SMR-BCI performance , 2012, Biological Psychology.

[49]  Thierry Dutoit,et al.  Performance of the Emotiv Epoc headset for P300-based applications , 2013, Biomedical engineering online.