Brain Control: Human-computer Integration Control Based on Brain-computer Interface Approach

Abstract Recently, a new system called brain control system has been developed rapidly. Brain control system is a human-computer integration control system based on brain-computer interface (BCI), which relies on human's ideas and thinking. Brain control system has been successfully applied in wide fields, assisting disabled patients daily life, training patients with stroke or limb injury, monitoring the state of human operator, as well as entertainment and smart house etc. In this paper, the background, basic principle, system structure and developments are firstly introduced briefly. The current research status focusing on the problems of electroencephalograph (EEG) signal pattern, control signal transfer algorithm and system application is summarized and analyzed in detail. The further research direction and problems are discussed. Finally, the future development of brain control is analyzed and prospects are given.

[1]  D.J. McFarland,et al.  The wadsworth BCI research and development program: at home with BCI , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[2]  Liqing Zhang,et al.  Multilinear generalization of Common Spatial Pattern , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Xingyu Wang,et al.  Multiway Canonical Correlation Analysis for Frequency Components Recognition in SSVEP-Based BCIs , 2011, ICONIP.

[4]  Andrzej Cichocki,et al.  Nonnegative Matrix and Tensor Factorization T , 2007 .

[5]  L.J. Trejo,et al.  Brain-computer interfaces for 1-D and 2-D cursor control: designs using volitional control of the EEG spectrum or steady-state visual evoked potentials , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[6]  Steven Lemm,et al.  BCI competition 2003-data set III: probabilistic modeling of sensorimotor /spl mu/ rhythms for classification of imaginary hand movements , 2004, IEEE Transactions on Biomedical Engineering.

[7]  G. Pfurtscheller,et al.  Evaluation of event-related desynchronization (ERD) preceding and following voluntary self-paced movement. , 1979, Electroencephalography and clinical neurophysiology.

[8]  Andrzej Cichocki,et al.  Whether generic model works for rapid ERP-based BCI calibration , 2013, Journal of Neuroscience Methods.

[9]  Gernot R. Müller-Putz,et al.  Control of an Electrical Prosthesis With an SSVEP-Based BCI , 2008, IEEE Transactions on Biomedical Engineering.

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

[11]  A. Cichocki,et al.  The Changing Face of P300 BCIs: A Comparison of Stimulus Changes in a P300 BCI Involving Faces, Emotion, and Movement , 2012, PloS one.

[12]  Jie Li,et al.  A Prior Neurophysiologic Knowledge Free Tensor-Based Scheme for Single Trial EEG Classification , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[13]  M. Woldorff,et al.  Distortion of ERP averages due to overlap from temporally adjacent ERPs: analysis and correction. , 2007, Psychophysiology.

[14]  Toshihisa Tanaka,et al.  Tensor Based Simultaneous Feature Extraction and Sample Weighting for EEG Classification , 2010, ICONIP.

[15]  Urbano Nunes,et al.  Statistical spatial filtering for a P300-based BCI: Tests in able-bodied, and patients with cerebral palsy and amyotrophic lateral sclerosis , 2011, Journal of Neuroscience Methods.

[16]  E Donchin,et al.  Brain-computer interface technology: a review of the first international meeting. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[17]  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.

[18]  Yan-Ning Cai,et al.  On Multiple Kernel Learning Methods: On Multiple Kernel Learning Methods , 2010 .

[19]  J. Wolpaw,et al.  A novel P300-based brain–computer interface stimulus presentation paradigm: Moving beyond rows and columns , 2010, Clinical Neurophysiology.

[20]  E. John,et al.  Evoked-Potential Correlates of Stimulus Uncertainty , 1965, Science.

[21]  Liqing Zhang,et al.  Noninvasive BCIs: Multiway Signal-Processing Array Decompositions , 2008, Computer.

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

[23]  Gert Pfurtscheller,et al.  Brain-computer interface: a new communication device for handicapped persons , 1993 .

[24]  Xiaorong Gao,et al.  An online multi-channel SSVEP-based brain–computer interface using a canonical correlation analysis method , 2009, Journal of neural engineering.

[25]  S. Silvoni,et al.  Brain-Computer Interface in Stroke: A Review of Progress , 2011, Clinical EEG and neuroscience.

[26]  Norbert Wiener,et al.  Cybernetics: Control and Communication in the Animal and the Machine. , 1949 .

[27]  R. Fazel-Rezai,et al.  Human Error in P300 Speller Paradigm for Brain-Computer Interface , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[28]  Xingyu Wang,et al.  A combined brain–computer interface based on P300 potentials and motion-onset visual evoked potentials , 2012, Journal of Neuroscience Methods.

[29]  P. Sajda,et al.  Cortically coupled computer vision for rapid image search , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[30]  Dennis J. McFarland,et al.  The P300-based brain–computer interface (BCI): Effects of stimulus rate , 2011, Clinical Neurophysiology.

[31]  M Salvaris,et al.  Visual modifications on the P300 speller BCI paradigm , 2009, Journal of neural engineering.

[32]  M. Thulasidas,et al.  Robust classification of EEG signal for brain-computer interface , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[33]  Carsten Thomsen,et al.  A comparative study between a simplified Kalman filter and Sliding Window Averaging for single trial dynamical estimation of event-related potentials , 2010, Comput. Methods Programs Biomed..

[34]  A. Graser,et al.  Spelling with Steady-State Visual Evoked Potentials , 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

[35]  Xingyu Wang,et al.  Targeting an efficient target-to-target interval for P300 speller brain–computer interfaces , 2012, Medical & Biological Engineering & Computing.

[36]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[37]  B. Rockstroh,et al.  Slow potentials of the cerebral cortex and behavior. , 1990, Physiological reviews.

[38]  Reinhold Scherer,et al.  Study of On-Line Adaptive Discriminant Analysis for EEG-Based Brain Computer Interfaces , 2007, IEEE Transactions on Biomedical Engineering.

[39]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

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

[41]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[42]  M Congedo,et al.  sw-SVM: sensor weighting support vector machines for EEG-based brain–computer interfaces , 2011, Journal of neural engineering.

[43]  Vladimir Bostanov,et al.  BCI competition 2003-data sets Ib and IIb: feature extraction from event-related brain potentials with the continuous wavelet transform and the t-value scalogram , 2004, IEEE Transactions on Biomedical Engineering.

[44]  M S Treder,et al.  Gaze-independent brain–computer interfaces based on covert attention and feature attention , 2011, Journal of neural engineering.

[45]  Wang Xing.  AUTOMATIC CONTROL: VIRTUALITY VS. REALITY , 2002 .

[46]  Mikko Sams,et al.  EEG-Based Brain-Computer Interface for Tetraplegics , 2007, Comput. Intell. Neurosci..

[47]  R. Palaniappan,et al.  Brain Computer Interface Design Using Band Powers Extracted During Mental Tasks , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

[48]  G. Pfurtscheller,et al.  Walking through a virtual city by thought , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[49]  Dean J Krusienski,et al.  A comparison of classification techniques for the P300 Speller , 2006, Journal of neural engineering.

[50]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[51]  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.

[52]  Christa Neuper,et al.  An asynchronously controlled EEG-based virtual keyboard: improvement of the spelling rate , 2004, IEEE Transactions on Biomedical Engineering.

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

[54]  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.

[55]  Christa Neuper,et al.  Hidden Markov models for online classification of single trial EEG data , 2001, Pattern Recognit. Lett..

[56]  Shangkai Gao,et al.  An online brain–computer interface using non-flashing visual evoked potentials , 2010, Journal of neural engineering.

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

[58]  W. Walter,et al.  Contingent Negative Variation : An Electric Sign of Sensori-Motor Association and Expectancy in the Human Brain , 1964, Nature.

[59]  Haixian Wang.  Optimizing spatial filters for single-trial EEG classification via a discriminant extension to CSP: the Fisher criterion , 2011, Medical & Biological Engineering & Computing.

[60]  Bart Wyns,et al.  Adding Machine Vision to Enhance P300 BCI Speller , 2011 .

[61]  Dana H. Ballard,et al.  Single Trial P300 Recognition in a Virtual Environment , 1998 .

[62]  G. Pfurtscheller,et al.  Could the beta rebound in the EEG be suitable to realize a “brain switch”? , 2009, Clinical Neurophysiology.

[63]  Gert Pfurtscheller,et al.  Characterization of four-class motor imagery EEG data for the BCI-competition 2005 , 2005, Journal of neural engineering.

[64]  A. Kübler,et al.  Flashing characters with famous faces improves ERP-based brain–computer interface performance , 2011, Journal of neural engineering.

[65]  Yuanqing Li,et al.  A Hybrid Brain Computer Interface to Control the Direction and Speed of a Simulated or Real Wheelchair , 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[66]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces to restore motor function and probe neural circuits , 2003, Nature Reviews Neuroscience.

[67]  Mark E. Dohring,et al.  Feasibility of a New Application of Noninvasive Brain Computer Interface (BCI): A Case Study of Training for Recovery of Volitional Motor Control After Stroke , 2009, Journal of neurologic physical therapy : JNPT.

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

[69]  Dario Farina,et al.  Identification of movement-related cortical potentials with optimized spatial filtering and principal component analysis , 2008, Biomed. Signal Process. Control..

[70]  D. Yao,et al.  Multiple Frequencies Sequential Coding for SSVEP-Based Brain-Computer Interface , 2012, PloS one.

[71]  Brendan Z. Allison,et al.  Journal of Neuroscience Methods , 2022 .

[72]  Helge J. Ritter,et al.  BCI competition 2003-data set IIb: support vector machines for the P300 speller paradigm , 2004, IEEE Transactions on Biomedical Engineering.

[73]  Seungjin Choi,et al.  PCA+HMM+SVM for EEG pattern classification , 2003, Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings..

[74]  C. Braun,et al.  Combination of Brain-Computer Interface Training and Goal-Directed Physical Therapy in Chronic Stroke: A Case Report , 2010, Neurorehabilitation and neural repair.

[75]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[76]  Dezhong Yao,et al.  Frequency detection with stability coefficient for steady-state visual evoked potential (SSVEP)-based BCIs. , 2008, Journal of neural engineering.

[77]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[78]  G Pfurtscheller,et al.  Using time-dependent neural networks for EEG classification. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[80]  A Belitski,et al.  P300 audio-visual speller , 2011, Journal of neural engineering.

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

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

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

[84]  Qi Xu,et al.  Fuzzy support vector machine for classification of EEG signals using wavelet-based features. , 2009, Medical engineering & physics.

[85]  Karla Felix Navarro,et al.  A Comprehensive Survey of Brain Interface Technology Designs , 2007, Annals of Biomedical Engineering.

[86]  I. P. Ganin,et al.  Event-related potentials in a moving matrix modification of the P300 brain–computer interface paradigm , 2011, Neuroscience Letters.

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

[88]  Alexander Maye,et al.  Temporal dynamics of access to consciousness in the attentional blink , 2007, NeuroImage.

[89]  Christa Neuper,et al.  Combined motor imagery and SSVEP based BCI control of a 2 DoF artificial upper limb , 2011, Medical & Biological Engineering & Computing.

[90]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[91]  Stefan Haufe,et al.  Single-trial analysis and classification of ERP components — A tutorial , 2011, NeuroImage.

[92]  Nicole Krämer,et al.  Time Domain Parameters as a feature for EEG-based Brain-Computer Interfaces , 2009, Neural Networks.

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

[94]  G. Pfurtscheller,et al.  Steady-state somatosensory evoked potentials: suitable brain signals for brain-computer interfaces? , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[95]  Andrzej Cichocki,et al.  Fully Online Multicommand Brain-Computer Interface with Visual Neurofeedback Using SSVEP Paradigm , 2007, Comput. Intell. Neurosci..

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

[97]  R. Galamboš,et al.  An electroencephalograph study of classical conditioning. , 1962, The American journal of physiology.

[98]  S. Puthusserypady,et al.  Multilayer perceptrons for the classification of brain computer interface data , 2005, Proceedings of the IEEE 31st Annual Northeast Bioengineering Conference, 2005..

[99]  J. Polich,et al.  P300 amplitude is determined by target-to-target interval. , 2002, Psychophysiology.

[100]  Tao Liu,et al.  N200-speller using motion-onset visual response , 2009, Clinical Neurophysiology.

[101]  N. Bigdely-Shamlo,et al.  Brain Activity-Based Image Classification From Rapid Serial Visual Presentation , 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[102]  G. Pfurtscheller,et al.  ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia , 2003, Neuroscience Letters.

[103]  A. R. Miller,et al.  P300 Amplitude and Topography in an Autobiographical Oddball Paradigm Involving Simulated Amnesia , 2002 .

[104]  Reinhold Scherer,et al.  Steady-state visual evoked potential (SSVEP)-based communication: impact of harmonic frequency components , 2005, Journal of neural engineering.

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

[106]  Haihong Zhang,et al.  A Brain-Controlled Wheelchair Based on P300 and Path Guidance , 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

[107]  A. Cichocki,et al.  A novel BCI based on ERP components sensitive to configural processing of human faces , 2012, Journal of neural engineering.

[108]  Tamara G. Kolda,et al.  Tensor Decompositions and Applications , 2009, SIAM Rev..

[109]  S P Levine,et al.  A direct brain interface based on event-related potentials. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[110]  Alain Rakotomamonjy,et al.  BCI Competition III: Dataset II- Ensemble of SVMs for BCI P300 Speller , 2008, IEEE Transactions on Biomedical Engineering.

[111]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled. , 2003, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[113]  Y. Nakajima,et al.  Visual stimuli for the P300 brain–computer interface: A comparison of white/gray and green/blue flicker matrices , 2009, Clinical Neurophysiology.

[114]  G E Birch,et al.  Brain-computer interface research at the Neil Squire Foundation. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[115]  Fei-Yue Wang,et al.  Hierarchical Dirichlet processes and their applications: a survey , 2011 .

[116]  Xiaorong Gao,et al.  A brain–computer interface using motion-onset visual evoked potential , 2008, Journal of neural engineering.

[117]  Wang Hong.  On Multiple Kernel Learning Methods , 2010 .

[118]  Xingyu Wang,et al.  A Novel Combination of Time Phase and EEG Frequency Components for SSVEP-Based BCI , 2011, ICONIP.

[119]  S M M Martens,et al.  Overlap and refractory effects in a brain–computer interface speller based on the visual P300 event-related potential , 2009, Journal of neural engineering.

[120]  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.

[121]  D. Hu,et al.  Gaze independent brain–computer speller with covert visual search tasks , 2011, Clinical Neurophysiology.

[122]  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..