A survey of methods for the construction of a Brain Computer Interface

A brain computer interface is an artificial intelligence system that provides the brain with a way to communicate with the outside environment. This paper aims to provide guidelines for biomedical researchers by discussing each phase of the construction of a Brain Computer Interface. It explains invasive and noninvasive BCI. Then, it presents the most commons methods employed for designing a BCI and discusses the artifacts removal. Finally, this paper summarizes the advantages and disadvantages of the presented methods and discusses the future steps into this field of research.

[1]  Fabien Lotte,et al.  Study of Electroencephalographic Signal Processing and Classification Techniques towards the use of Brain-Computer Interfaces in Virtual Reality Applications , 2008 .

[2]  Ned Horning,et al.  Random Forests : An algorithm for image classification and generation of continuous fields data sets , 2010 .

[3]  Kenneth J. Pope,et al.  Surface Laplacian of Central Scalp Electrical Signals is Insensitive to Muscle Contamination , 2013, IEEE Transactions on Biomedical Engineering.

[4]  T. Sejnowski,et al.  Removing electroencephalographic artifacts by blind source separation. , 2000, Psychophysiology.

[5]  Stanisław Adamczak,et al.  INVESTIGATING ADVANTAGES AND DISADVANTAGES OF THE ANALYSIS OF A GEOMETRICAL SURFACE STRUCTURE WITH THE USE OF FOURIER AND WAVELET TRANSFORM , 2010 .

[6]  Eric Laciar Leber,et al.  Noise Removal from EEG Signals in Polisomnographic Records Applying Adaptive Filters in Cascade , 2011 .

[7]  Thierry Denœux.  A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory , 2008 .

[8]  T. Kameswara Rao,et al.  An Exploration on Brain Computer Interface and Its Recent Trends , 2012, ArXiv.

[9]  Shahidan M. Abdullah,et al.  An overview of principal component analysis , 2013 .

[10]  M. Palaniswami,et al.  Limitations of ICA for Artefact Removal , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[11]  Jerome H. Friedman,et al.  On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality , 2004, Data Mining and Knowledge Discovery.

[12]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[13]  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.

[14]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[15]  Kip A Ludwig,et al.  Using a common average reference to improve cortical neuron recordings from microelectrode arrays. , 2009, Journal of neurophysiology.

[16]  Kohji Fukunaga,et al.  Introduction to Statistical Pattern Recognition-Second Edition , 1990 .

[17]  Tan Yee Fan,et al.  A Tutorial on Support Vector Machine , 2009 .

[18]  Klaus-Robert Müller,et al.  Enhanced Performance by a Hybrid Nirs–eeg Brain Computer Interface , 2022 .

[19]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[20]  Rouslan A. Moro,et al.  Support Vector Machines (SVM) as a Technique for Solvency Analysis , 2008 .

[21]  L. Ryd,et al.  On bias. , 1994, Acta orthopaedica Scandinavica.

[22]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.