Fundamentals of Brain Signals and Its Medical Application Using Data Analysis Techniques

In this chapter, the various data analysis techniques devoted to the development of brain signals controlled interface devices for the purpose of rehabilitation in a multi-disciplinary engineering is presented. The knowledge of electroencephalogram (EEG) is essential for the neophytes in the development of algorithms using EEG.Most literatures, demonstrates the application of EEG signals and no much definite study describes the various components that are censorious for development of interface devices using prevalent algorithms in real-time data analysis. Therefore, this chapter covers the EEG generation, various components of EEG used in development of interface devices and algorithms used for identification of information from EEG.

[1]  D. Cheyne.  MEG studies of sensorimotor rhythms: A review , 2013, Experimental Neurology.

[2]  M. Teplan.  FUNDAMENTALS OF EEG MEASUREMENT , 2002 .

[3]  J. Si,et al.  Principle component feature detector for motor cortical control , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  Cuntai Guan,et al.  High performance P300 speller for brain-computer interface , 2004, IEEE International Workshop on Biomedical Circuits and Systems, 2004..

[5]  George Adelman,et al.  Encyclopedia of neuroscience , 2004 .

[6]  Juliusz L. Kulikowski,et al.  Human - Computer Systems Interaction: Backgrounds and Applications 2 Part 1 , 2011 .

[7]  D. Hammond,et al.  What is Neurofeedback: An Update , 2011 .

[8]  D Friedman,et al.  Event‐related potential (ERP) studies of memory encoding and retrieval: A selective review , 2000, Microscopy research and technique.

[9]  Chris Eliasmith,et al.  Neural Engineering , 2020 .

[10]  M. Rugg,et al.  Electrophysiology of Mind: Event-Related Brain Potentials and Cognition , 1995 .

[11]  Charles W. Anderson,et al.  EEG signal classification with different signal representations , 1995, Proceedings of 1995 IEEE Workshop on Neural Networks for Signal Processing.

[12]  Avid Roman-Gonzalez,et al.  EEG Signal Processing for BCI Applications , 2010 .

[13]  Duncan Maitland,et al.  Biomedical Technology and Devices, Second Edition , 2013 .

[14]  Bernie Mulgrew,et al.  IEEE Workshop on Neural Networks for Signal Processing , 1995 .

[15]  Hartmut Heinrich,et al.  Annotation: neurofeedback - train your brain to train behaviour. , 2007, Journal of child psychology and psychiatry, and allied disciplines.

[16]  J. Mendelson,et al.  Electroencephalographic correlates of marihuana-induced euphoria. , 1995, Drug and alcohol dependence.

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

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

[19]  Diego López-de-Ipiña,et al.  Measuring Software Timing Errors in the Presentation of Visual Stimuli in Cognitive Neuroscience Experiments , 2014, PloS one.

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

[21]  Moritz Grosse-Wentrup,et al.  Critical issues in state-of-the-art brain–computer interface signal processing , 2011, Journal of neural engineering.

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

[23]  C. Neuper,et al.  Sensorimotor rhythm-based brain–computer interface training: the impact on motor cortical responsiveness , 2011, Journal of neural engineering.

[24]  Ernst Fernando Lopes Da Silva Niedermeyer,et al.  Electroencephalography, basic principles, clinical applications, and related fields , 1982 .

[25]  G. Oriolo,et al.  Non-invasive brain–computer interface system: Towards its application as assistive technology , 2008, Brain Research Bulletin.

[26]  Po-Lei Lee,et al.  Adaptive SSVEP-Based BCI System With Frequency and Pulse Duty-Cycle Stimuli Tuning Design , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[27]  Kara D. Federmeier,et al.  Event-related brain potentials. , 1990 .

[28]  D. Hammond,et al.  What Is Neurofeedback , 2007 .

[29]  Xiaorong Gao,et al.  Design and implementation of a brain-computer interface with high transfer rates , 2002, IEEE Transactions on Biomedical Engineering.

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

[31]  J. Rohrbaugh,et al.  Beta power in the EEG of alcoholics , 2002, Biological Psychiatry.

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

[33]  Lijuan Cao,et al.  A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine , 2003, Neurocomputing.