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Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 241489, 8 pages
http://dx.doi.org/10.1155/2013/241489
Research Article

Smartphone Household Wireless Electroencephalogram Hat

1Department of Biomedical Engineering, The Catholic University of America, Washington, DC 20064, USA
2Trident Systems Inc., Fairfax, VA 22030, USA
3Department of Electronic Engineering, Hallym University, Chuncheon, Gangwon-do 200-702, Republic of Korea
4Fuzzy Logic System Institute, Semiconductor Center, Kitakyushu Science and Research Park, Kitakyushu 808-0135, Fukuoka, Japan
5Swartz Center, University of California, San Diego, CA 92093, USA
6Briartek Inc., Alexandria, VA 22301, USA

Received 25 May 2012; Accepted 21 October 2012

Academic Editor: Soo-Young Lee

Copyright © 2013 Harold Szu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphone-based electroencephalogram (EEG) system for homecare applications. The system uses high-density dry electrodes and compressive sensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal throughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using an adaptive selection of active among passive electrodes to form -organized random linear combinations of readouts, . Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took tethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data centers in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without knowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original tethered data and the speed of compressive image recovery. We have compared our recovery of ill-posed inverse data against results using Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e., facial muscle-related events and wireless environmental electromagnetic interferences).