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). 1. Introduction A noninvasive electrical response exists near the scalp from neuron ionic transmission among neural network and may be measured via electroencephalography (EEG). Wang and colleagues of UCSD [1, 2] have demonstrated the efficacy of an untethered, wireless brain machine interface (BMI) system using 20 dry electrodes embedded into a head cap. The wireless EEG head cap system has a built-in bandwidth filter for eliminating environmental noise, for example, 60？Hz household utility line and also for pattern noise. Additionally, this pattern noise filter naturally represents a neuron threshold logic which can be used for assessment of cognitive function and for diagnosis. Figure 1 shows various observed EEG state, representative EEG patterns, typical frequency ranges, correlation to different activities, and state of mind/levels of engagement. There is a simple rule of thumb about the mnemonics of brainwaves in terms of “D, T, A, B” phonetic equivalence with “deep tap.” “Deep tap” separates apart D = delta (0–4？Hz); T = theta (4–7？Hz); A = alpha (8–12？Hz); B = beta (13–30？Hz) at about 4？Hz or more
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