A realtime brain-computer interface based on PDA

A system that uses the human ability to control a portable video game device through some electroencephalographic (EEG) rhythms has been presented. The neural signals obtained by EEG recording equipment are sent through a Web Service technology connection to a PC that processes it in real time and sends it wirelessly to a mobile gaming device. Web Service has been used as a wireless technology for sending EEG signals to a PC and vice versa, the PDA gets the command from the PC though the Web Service technology, which has been proposed and furthers it showing that the system is reliable and robust enough to work in BCI systems. In this study we have investigated the human's ability to play a video game by manipulating neuronal motor cortex activity in the presence of a visual feedback environment. This paper presents one of these solutions, which control games by BCI based on motor imagery. This type of BCI solution is translating the motor imagery EEG into three control signals: left, right or transmutation. And then use it as an input device of a simple game player to realize the function of operating games.

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

[2]  Huan Nai-Jen,et al.  Classification of mental tasks using fixed and adaptive autoregressive models of EEG signals , 2005, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[3]  Wei Wu,et al.  Frequency recognition based on canonical correlation analysis for SSVEP-based BCIs , 2007, IEEE Transactions on Biomedical Engineering.

[4]  C.W. Anderson,et al.  Multivariate autoregressive models for classification of spontaneous electroencephalographic signals during mental tasks , 1998, IEEE Transactions on Biomedical Engineering.

[5]  D J McFarland,et al.  Brain-computer interface research at the Wadsworth Center. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[7]  Hyung-Cheul Shin,et al.  Development of intracranial brain-computer interface system using non-motor brain area for series of motor functions , 2006 .

[8]  G. Pfurtscheller,et al.  15 years of BCI research at graz university of technology: current projects , 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  F Babiloni,et al.  Introducing BF++: AC++ framework for cognitive bio-feedback systems design. , 2003, Methods of information in medicine.

[10]  R. Palaniappan,et al.  Classification of Mental Tasks Using Fixed and Adaptive Autoregressive Models of EEG Signals , 2005, Conference Proceedings. 2nd International IEEE EMBS Conference on Neural Engineering, 2005..

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

[12]  Ramaswamy Palaniappan,et al.  Neural network classification of autoregressive features from electroencephalogram signals for brain–computer interface design , 2004, Journal of neural engineering.

[13]  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.

[14]  F. Babiloni,et al.  Developing wearable bio-feedback systems: a general-purpose platform , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[15]  G. Pfurtscheller,et al.  Graz-BCI: state of the art and clinical applications , 2003, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

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

[17]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[18]  A Kostov,et al.  Parallel man-machine training in development of EEG-based cursor control. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[19]  Jianfeng Hu,et al.  Classification of Motor Imagery EEG Based on a Time-Frequency Analysis and Second-Order Blind Identification , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.