Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphonebased\r\nelectroencephalogram(EEG) system for homecare applications.Thesystem uses high-density dry electrodes and compressive\r\nsensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal\r\nthroughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using\r\nan adaptive selection of N active among 10 N passive electrodes to form m-organized random linear combinations of readouts,\r\nm �« N �« 10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took\r\ntethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data\r\ncenters in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without\r\nknowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original\r\ntethered data and the speed of compressive image recovery.We have compared our recovery of ill-posed inverse data against results\r\nusing Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e.,\r\nfacial muscle-related events and wireless environmental electromagnetic interferences).
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