Recent advances in brain-computer interfaces

A brain-computer interface (BCI) is a communication system that translates brain activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain activity, without using peripheral nerves and muscles. The major goal of BCI research is to develop systems that allow disabled users to communicate with other persons, to control artificial limbs, or to control their environment. To achieve this goal, many aspects of BCI systems are currently being investigated. Research areas include evaluation of invasive and noninvasive technologies to measure brain activity, evaluation of control signals (i.e. patterns of brain activity that can be used for communication), development of algorithms for translation of brain signals into computer commands, and the development of new BCI applications. In this paper we give an overview of the aspects of BCI research mentioned above and highlight recent developments and open problems.

[1]  U. Hoffmann,et al.  Application of the evidence framework to brain-computer interfaces , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[3]  Garcia Molina,et al.  Direct brain-computer communication through scalp recorded EEG signals , 2004 .

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

[5]  Robert A. Jacobs,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

[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]  Tzyy-Ping Jung,et al.  Independent Component Analysis of Electroencephalographic Data , 1995, NIPS.

[8]  G. Curio,et al.  Probabilistic Modeling of Sensorimotor μ-Rhythms for Classification of Imaginary Hand Movements ( BCI Competition 2003-Data Set III ) , 2004 .

[9]  Xiaorong Gao,et al.  A BCI-based environmental controller for the motion-disabled , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[10]  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..

[11]  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.

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

[13]  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.

[14]  Klaus-Robert Müller,et al.  Boosting bit rates in noninvasive EEG single-trial classifications by feature combination and multiclass paradigms , 2004, IEEE Transactions on Biomedical Engineering.

[15]  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.

[16]  Ulrich Hoffmann,et al.  Bayesian machine learning applied in a brain-computer interface for disabled users , 2007 .

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

[18]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[19]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[20]  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.

[21]  Touradj Ebrahimi,et al.  Spatial filters for the classification of event-related potentials , 2006, ESANN.

[22]  Rabab K Ward,et al.  A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals , 2007, Journal of neural engineering.

[23]  Klaus-Robert Müller,et al.  Classifying Single Trial EEG: Towards Brain Computer Interfacing , 2001, NIPS.

[24]  E. Gysels,et al.  Phase synchronization for the recognition of mental tasks in a brain-computer interface , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

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

[27]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[28]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[29]  Miguel A. L. Nicolelis,et al.  Brain–machine interfaces: past, present and future , 2006, Trends in Neurosciences.

[30]  Gernot R. Müller-Putz,et al.  EURASIP Journal on Applied Signal Processing 2005:19, 3152–3155 c ○ 2005 Hindawi Publishing Corporation EEG-Based Asynchronous BCI Controls Functional Electrical Stimulation in a Tetraplegic Patient , 2004 .

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

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

[33]  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..

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

[35]  Dawn M. Taylor,et al.  Direct Cortical Control of 3D Neuroprosthetic Devices , 2002, Science.

[36]  Jon A. Mukand,et al.  Neuronal ensemble control of prosthetic devices by a human with tetraplegia , 2006, Nature.

[37]  Touradj Ebrahimi,et al.  Brain-computer interface in multimedia communication , 2003, IEEE Signal Process. Mag..