Commenced in January 2007
Paper Count: 30988
Functional Near Infrared Spectroscope for Cognition Brain Tasks by Wavelets Analysis and Neural Networks
Abstract:Brain Computer Interface (BCI) has been recently increased in research. Functional Near Infrared Spectroscope (fNIRs) is one the latest technologies which utilize light in the near-infrared range to determine brain activities. Because near infrared technology allows design of safe, portable, wearable, non-invasive and wireless qualities monitoring systems, fNIRs monitoring of brain hemodynamics can be value in helping to understand brain tasks. In this paper, we present results of fNIRs signal analysis indicating that there exist distinct patterns of hemodynamic responses which recognize brain tasks toward developing a BCI. We applied two different mathematics tools separately, Wavelets analysis for preprocessing as signal filters and feature extractions and Neural networks for cognition brain tasks as a classification module. We also discuss and compare with other methods while our proposals perform better with an average accuracy of 99.9% for classification.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1082245Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1539
 Bunce S.C., Izzetoglu M., Izzetoglu, K., Onaral, B., and Pourrezaei, K., Functional near-infrared spectroscopy, IEEE Eng. Medicine and Biology Magazine 25 (2006) 54.
 Izzetoglu M., Izzetoglu K., Bunce S., Ayaz H., Devaraj A., Onaral B., and Pourrezaei K., Functional near-infrared neuroimaging, IEEE Trans. Neural Systems and Rehabilitation Eng. 13 (2005) 153.
 Izzetoglu M., Devaraj A., Bunce S., and Onaral B., Motion artifact cancellation in NIR spectroscopy using Wiener filtering, IEEE Trans. Biomedical Eng. 52 (2005) 934.
 K. Izzetoglu, S. Bunce, B. Onaral, K. Pourrezaei, and B. Chance, Functional optical brain imaging using near-infrared during cognitive tasks, Int. J. Human-Comp. Int. 17 (2004) 211.
 Ranganatha Sitaram, Haihong Zhang, Cuntai Guan, Manoj Thulasidas, Yoko Hoshi, Akihiro Ishikawa, Koji Shimizu and Niels Birbaumer, Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface, J. NeuroImage, 34 (2007) 1416.
 S.Sitharama Lyengar, E.C. Cho, Vir V. Phoha: Foundations of Wavelet networks and applications, (Chapman&Hall CRC, 2002) Chap. 2, p. 44.
 S. Haykin, Neural Networks: A Comprehensive Foundation, Second Edition, (Macmillan, New York, 1999) Chap. 4, p. 178.
 Montri Phothisonothai and Masahiro Nakagawa, EEG-Based Classification of New Imagery Tasks Using Three-Layer Feedforward Neural Network Classifier for Brain-Computer Interface, J. Phys. Soc. Jpn 75 (2006) 104801.
 K.Ogo and M.Nakagawa, On the Chaos and Fractal Properties in EEG Data, J. Electron. Commun. Jpn Fundamentals, 78 (1995) 27.
 Joaqu├¡n Fuster, Michael Guiou, Allen Ardestani, Andrew Cannestra, Sameer Sheth, Yong-Di Zhou, Arthur Toga and Mark Bodner, Nearinfrared spectroscopy (NIRS) in cognitive neuroscience of the primate brain, J. NeuroImage, 26 (2005) 215.
 Truong Quang Dang Khoa and Masahiro Nakagawa, Modeling Chaos Neural Networks for Classification of EEG Signals, Proc. AROB, Oita, Japan, 12 (2007) GS6-3.