We applied Compressive Sensing to design an affordable, convenient Brain Machine Interface (BMI) measuring the
high spatial density, and real-time process of Electroencephalogram (EEG) brainwaves by a Smartphone. It is useful
for therapeutic and mental health monitoring, learning disability biofeedback, handicap interfaces, and war gaming.
Its spec is adequate for a biomedical laboratory, without the cables hanging over the head and tethered to a fixed
computer terminal. Our improved the intrinsic signal to noise ratio (SNR) by using the non-uniform placement of
the measuring electrodes to create the proximity of measurement to the source effect. We computing a
spatiotemporal average the larger magnitude of EEG data centers in 0.3 second taking on tethered laboratory data,
using fuzzy logic, and computing the inside brainwave sources, by Independent Component Analysis (ICA).
Consequently, we can overlay them together by non-uniform electrode distribution enhancing the signal noise ratio
and therefore the degree of sparseness by threshold. We overcame the conflicting requirements between a high
spatial electrode density and precise temporal resolution (beyond Event Related Potential (ERP) P300 brainwave at
0.3 sec), and Smartphone wireless bottleneck of spatiotemporal throughput rate. Our main contribution in this paper
is the quality and the speed of iterative compressed image recovery algorithm based on a Block Sparse Code (Baranuick et al, IEEE/IT 2008). As a result, we achieved real-time wireless dynamic measurement of EEG brainwaves, matching well with traditionally tethered high density EEG.