Toward smarter BCIs: extending BCIs through hybridization and intelligent control

This paper summarizes two novel ways to extend brain-computer interface (BCI) systems. One way involves hybrid BCIs. A hybrid BCI is a system that combines a BCI with another device to help people send information. Different types of hybrid BCIs are discussed, along with challenges and issues. BCIs are also being extended through intelligent systems. Software that allows high-level control, incorporates context and the environment and/or uses virtual reality can substantially improve BCI systems. Throughout the paper, we critically address the real benefits of these improvements relative to existing technology and practices. We also present new challenges that are likely to emerge as these novel BCI directions become more widespread.

[1]  Cheng-Zhong Xu,et al.  Model Predictive Feedback Control for QoS Assurance in Webservers , 2008, Computer.

[2]  José del R. Millán,et al.  Error-Related EEG Potentials Generated During Simulated Brain–Computer Interaction , 2008, IEEE Transactions on Biomedical Engineering.

[3]  G Pfurtscheller,et al.  Self-initiation of EEG-based brain-computer communication using the heart rate response. , 2007, Journal of neural engineering.

[4]  Clay B. Holroyd,et al.  The neural basis of human error processing: reinforcement learning, dopamine, and the error-related negativity. , 2002, Psychological review.

[5]  Ricardo Chavarriaga,et al.  A hybrid brain–computer interface based on the fusion of electroencephalographic and electromyographic activities , 2011, Journal of neural engineering.

[6]  Michitaka Hirose,et al.  Brain-Computer Interfaces, Virtual Reality, and Videogames , 2008, Computer.

[7]  Brendan Z. Allison,et al.  A software SSVEP BCI integrating stimuli within motivating and immersive virtual and augmented reality environments , 2010 .

[8]  C Neuper,et al.  A comparison of three brain–computer interfaces based on event-related desynchronization, steady state visual evoked potentials, or a hybrid approach using both signals , 2011, Journal of neural engineering.

[9]  José del R. Millán,et al.  The role of shared-control in BCI-based telepresence , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[10]  G. Vecchiato,et al.  A hybrid platform based on EOG and EEG signals to restore communication for patients afflicted with progressive motor neuron diseases , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  M.M. Moore,et al.  Real-world applications for brain-computer interface technology , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[12]  Alfons Schuster.  Robust Intelligent Systems , 2008 .

[13]  G Pfurtscheller,et al.  Toward a hybrid brain–computer interface based on imagined movement and visual attention , 2010, Journal of neural engineering.

[14]  C. Neuper,et al.  Combining Brain–Computer Interfaces and Assistive Technologies: State-of-the-Art and Challenges , 2010, Front. Neurosci..

[15]  John J. Leggett,et al.  Interaction styles and input/output devices , 1993, Behav. Inf. Technol..

[16]  Christa Neuper,et al.  Walking by Thinking: The Brainwaves Are Crucial, Not the Muscles! , 2006, PRESENCE: Teleoperators and Virtual Environments.

[17]  J.D. Bayliss,et al.  Use of the evoked potential P3 component for control in a virtual apartment , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[18]  G. Pfurtscheller,et al.  EEG-based communication: presence of an error potential , 2000, Clinical Neurophysiology.

[19]  John R. Smith,et al.  Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment , 2005, EURASIP J. Adv. Signal Process..

[20]  Vera Kaiser,et al.  Switching between Manual Control and Brain-Computer Interface Using Long Term and Short Term Quality Measures , 2011, Front. Neurosci..

[21]  Yi Li,et al.  A hybrid brain-computer interface control strategy in a virtual environment , 2011, Journal of Zhejiang University SCIENCE C.

[22]  Thorsten O. Zander,et al.  Combining Eye Gaze Input With a Brain–Computer Interface for Touchless Human–Computer Interaction , 2010, Int. J. Hum. Comput. Interact..

[23]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[24]  Benjamin Blankertz,et al.  Designing for uncertain, asymmetric control: Interaction design for brain-computer interfaces , 2009, Int. J. Hum. Comput. Stud..

[25]  Dieter Schmalstieg,et al.  An Application Framework for Controlling an Avatar in a Desktop-Based Virtual Environment via a Software SSVEP BrainComputer Interface , 2010, PRESENCE: Teleoperators and Virtual Environments.

[26]  G. Pfurtscheller,et al.  Self-Paced Operation of an SSVEP-Based Orthosis With and Without an Imagery-Based “Brain Switch:” A Feasibility Study Towards a Hybrid BCI , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  P. Sajda,et al.  Response error correction-a demonstration of improved human-machine performance using real-time EEG monitoring , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[28]  Brendan Z. Allison,et al.  Non-invasive Brain-Computer Interfaces for Semi-autonomous Assistive Devices , 2008 .

[29]  Francisco Velasco-Álvarez,et al.  A two-class brain computer interface to freely navigate through virtual worlds / Ein Zwei-Klassen-Brain-Computer-Interface zur freien Navigation durch virtuelle Welten , 2009, Biomedizinische Technik. Biomedical engineering.

[30]  Brendan Z. Allison,et al.  Context-Awareness as an Enhancement of Brain-Computer Interfaces , 2011, IWAAL.

[31]  Bin He,et al.  EEG Control of a Virtual Helicopter in 3-Dimensional Space Using Intelligent Control Strategies , 2010, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[33]  Giuseppe Oriolo,et al.  A multimode navigation system for an assistive robotics project , 2008, Auton. Robots.

[34]  José del R. Millán,et al.  Simultaneous Real-Time Detection of Motor Imagery and Error-Related Potentials for Improved BCI Accuracy , 2008 .

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

[36]  Brendan Z. Allison,et al.  A hybrid ERD/SSVEP BCI for continuous simultaneous two dimensional cursor control , 2012, Journal of Neuroscience Methods.

[37]  G. Pfurtscheller,et al.  Brain–Computer Communication: Motivation, Aim, and Impact of Exploring a Virtual Apartment , 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[38]  Horst Bischof,et al.  Toward Self-Paced Brain–Computer Communication: Navigation Through Virtual Worlds , 2008, IEEE Transactions on Biomedical Engineering.

[39]  Michael W. Haas,et al.  Navigating through virtual flight environments using brain-body-actuated control , 1997, Proceedings of IEEE 1997 Annual International Symposium on Virtual Reality.

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

[41]  Brendan Z. Allison,et al.  The Hybrid BCI , 2010, Frontiers in Neuroscience.

[42]  Gernot R. Müller-Putz,et al.  Self-Paced (Asynchronous) BCI Control of a Wheelchair in Virtual Environments: A Case Study with a Tetraplegic , 2007, Comput. Intell. Neurosci..

[43]  Brendan Z. Allison,et al.  Non-invasive Brain-Computer Interfaces: Enhanced Gaming and Robotic Control , 2011, IWANN.

[44]  Yuanqing Li,et al.  An EEG-Based BCI System for 2-D Cursor Control by Combining Mu/Beta Rhythm and P300 Potential , 2010, IEEE Transactions on Biomedical Engineering.

[45]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[46]  A. Buttfield,et al.  Towards a robust BCI: error potentials and online learning , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[47]  Gert Pfurtscheller,et al.  Self-paced exploration of the Austrian National Library through thought , 2007 .

[48]  Brice Rebsamen,et al.  A brain controlled wheelchair to navigate in familiar environments. , 2010, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[49]  José del R. Millán,et al.  Noninvasive brain-actuated control of a mobile robot by human EEG , 2004, IEEE Transactions on Biomedical Engineering.

[50]  William J Tyler,et al.  A quantitative overview of biophysical forces impinging on neural function , 2013, Physical biology.

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

[52]  Brendan Z. Allison,et al.  Improved signal processing approaches in an offline simulation of a hybrid brain–computer interface , 2010, Journal of Neuroscience Methods.

[53]  Desney S. Tan,et al.  Brain-Computer Interfacing for Intelligent Systems , 2008, IEEE Intelligent Systems.

[54]  K.-R. Muller,et al.  Boosting bit rates and error detection for the classification of fast-paced motor commands based on single-trial EEG analysis , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[55]  P R Kennedy,et al.  Direct control of a computer from the human central nervous system. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.