EEG Mouse:A Machine Learning-Based Brain Computer Interface

The main idea of the current work is to use a wireless Electroencephalography (EEG) headset as a remote control for the mouse cursor of a personal computer. The proposed system uses EEG signals as a communication link between brains and computers. Signal records obtained from the PhysioNet EEG dataset were analyzed using the Coif lets wavelets and many features were extracted using different amplitude estimators for the wavelet coefficients. The extracted features were inputted into machine learning algorithms to generate the decision rules required for our application. The suggested real time implementation of the system was tested and very good performance was achieved. This system could be helpful for disabled people as they can control computer applications via the imagination of fists and feet movements in addition to closing eyes for a short period of time. Keywords—EEG; BCI; Data Mining; Machine Learning; SVMs; NNs; DWT; Feature Extraction

[1]  Y.M. Kadah,et al.  Machine Learning Methodologies in Brain-Computer Interface Systems , 2008, 2008 Cairo International Biomedical Engineering Conference.

[2]  A. Phinyomark,et al.  An optimal wavelet function based on wavelet denoising for multifunction myoelectric control , 2009, 2009 6th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology.

[3]  R. Ranta,et al.  EEG Ocular Artefacts and Noise Removal , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  G. Pfurtscheller,et al.  Evidence for distinct beta resonance frequencies in human EEG related to specific sensorimotor cortical areas , 2001, Clinical Neurophysiology.

[5]  T. Tamura,et al.  27th Annual Inter national Conference of the IEEE Engineering in Medicine and Biology Society , 2005 .

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

[7]  Franjo Jović,et al.  CLASSIFICATION OF WAVELET TRANSFORMED EEG SIGNALS WITH NEURAL NETWORK FOR IMAGINED MENTAL AND MOTOR TASKS , 2013 .

[8]  M. Al-Omari,et al.  Machine Leaning-Based Investigation of the Associations between CMEs and Filaments , 2010 .

[9]  L. Deecke,et al.  Magnetic fields of the human brain accompanying voluntary movement: Bereitschaftsmagnetfeld , 2004, Experimental Brain Research.

[10]  Jason Sleight,et al.  Classification of Executed and Imagined Motor Movement EEG Signals , 2009 .

[11]  Brendan Z. Allison,et al.  Brain-Computer Interfaces: A Gentle Introduction , 2009 .

[12]  S. P. Levine,et al.  Identification of electrocorticogram patterns as the basis for a direct brain interface. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[13]  Christian Lebiere,et al.  The Cascade-Correlation Learning Architecture , 1989, NIPS.

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

[15]  Bao-Liang Lu,et al.  Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[16]  Emad A. Awada,et al.  Subject-Independent EEG-based Discrimination between Imagined and Executed, Right and Left Fists Movements , 2014 .

[17]  Gavriel Salvendy,et al.  Human Interface and the Management of Information. Methods, Techniques and Tools in Information Design, Symposium on Human Interface 2007, Held as Part of HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings Part I , 2007, HCI.

[18]  Pornchai Phukpattaranont,et al.  EMG AMPLITUDE ESTIMATORS BASED ON PROBABILITY DISTRIBUTION FOR MUSCLE–COMPUTER INTERFACE , 2013 .

[19]  S. Premrudeepreechacharn,et al.  Harmonic Detection in Distribution Systems Using Wavelet Transform and Support Vector Machine , 2007, 2007 IEEE Lausanne Power Tech.

[20]  Qing Yang Liang,et al.  Method of Harmonic Detection Based on the Wavelet Transform , 2013 .

[21]  G. Pfurtscheller,et al.  EEG-based discrimination between imagination of right and left hand movement. , 1997, Electroencephalography and clinical neurophysiology.

[22]  G. Pfurtscheller,et al.  Prosthetic Control by an EEG-based Brain-Computer Interface (BCI) , 2001 .

[23]  A. Mohamed,et al.  Towards improved EEG interpretation in a sensorimotor BCI for the control of a prosthetic or orthotic hand. , 2011 .

[24]  Francisco Sepulveda,et al.  Brain-actuated Control of Robot Navigation , 2011 .

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

[26]  Marie-Françoise Lucas,et al.  Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters , 2007, Journal of Neuroscience Methods.

[27]  Klaus-Robert Müller,et al.  Machine Learning and Applications for Brain-Computer Interfacing , 2007, HCI.

[28]  John P. Donoghue,et al.  Connecting cortex to machines: recent advances in brain interfaces , 2002, Nature Neuroscience.

[29]  Mohammad H. Alomari,et al.  Automated Classification of L/R Hand Movement EEG Signals using Advanced Feature Extraction and Machine Learning , 2013, ArXiv.

[30]  Sunyoung Cho,et al.  Single trial discrimination between right and left hand movement with EEG signal , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[31]  Fernando Lopes da Silva,et al.  Comprar Niedermeyer's Electroencephalography, 6/e (Basic Principles, Clinical Applications, and Related Fields ) | Fernando Lopes Da Silva | 9780781789424 | Lippincott Williams & Wilkins , 2010 .

[32]  A. Phinyomark,et al.  Optimal Wavelet Functions in Wavelet Denoising for Multifunction Myoelectric Control , 2009, ECTI Transactions on Electrical Engineering, Electronics, and Communications.

[33]  Yijun Wang,et al.  Implementation of a Brain-Computer Interface Based on Three States of Motor Imagery , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[34]  Bin He,et al.  BRAIN^COMPUTER INTERFACE , 2007 .

[35]  Dr. Sanjay Vasant Dudul,et al.  Daubechies Wavelet Neural Network Classifier for the Diagnosis of Epilepsy , 2013 .

[36]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[37]  N. Ellouze,et al.  Optimal segments selection for EEG classification , 2012, 2012 6th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT).

[38]  M. Al-Omari,et al.  Automated Prediction of CMEs Using Machine Learning of CME – Flare Associations , 2008 .

[39]  Emad A. Awada,et al.  Wavelet-Based Feature Extraction for the Analysis of EEG Signals Associated with Imagined Fists and Feet Movements , 2014, Comput. Inf. Sci..