Smartphone household wireless electroencephalogram hat

Authors:
Harold Szu;Charles Hsu;Gyu Moon;Takeshi Yamakawa;Binh Q. Tran;Tzyy Ping Jung;Joseph Landa
Affiliations:
Department of Biomedical Engineering, The Catholic University of America, Washington, DC;Trident Systems Inc., Fairfax, VA;Department of Electronic Engineering, Hallym University, Gangwon, Republic of Korea;Department of Electronic Engineering, Hallym University, Gangwon, Republic of Korea;Department of Biomedical Engineering, The Catholic University of America, Washington, DC;Swartz Center, University of California, San Diego, CA;Briartek Inc., Alexandria, VA
Venue:
Applied Computational Intelligence and Soft Computing
Year:
2013

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Abstract

Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphonebased electroencephalogram (EEG) system for homecare applications. The systemuses 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 N active among 10N passive electrodes to form m-organized random linear combinations of readouts, m ≪ N ≪ 10N. 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).