Abstract
Rudimentarybrain machine interfacehas 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 sparsenessis addressed by close proximity between active electrodes and desired source locations and using an adaptive selection ofNactive among10Npassive electrodes to formm-organized random linear combinations of readouts,m≪N≪10N.Temporal sparsenessis 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).
Highlights
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
In a similar fashion to how a focal plane array CCD outputs from pixels sensors with m linear combinations of all N sensor data (m ≪ N underdetermined), the advantage of a true medical Compressive sensing (CS) value is that it can be obtained more quickly and at a less exposure to patients. This ill-posed inversion is mathematically possible, because CRT&D proved a CS theorem that the linear combinations readouts coefficients form a purely random bipolar analog CS sampling matrix [Φ] that is pseudoorthogonal among m rows, where m ≅ 1.3k corresponding to the intrinsic sparse degree-of-freedom of the information
Summary
A noninvasive electrical response exists near the scalp from neuron ionic transmission among neural network and may be measured via electroencephalography (EEG). The wireless EEG head cap system has a built-in bandwidth filter for eliminating environmental noise, for example, 60 Hz household utility line and for pattern noise This pattern noise filter naturally represents a neuron threshold logic which can be used for assessment of cognitive function and for diagnosis. In a similar fashion to how a focal plane array CCD outputs from pixels sensors with m linear combinations of all N sensor data (m ≪ N underdetermined), the advantage of a true medical CS value is that it can be obtained more quickly and at a less exposure (i.e., radiation) to patients This ill-posed inversion is mathematically possible, because CRT&D proved a CS theorem that the linear combinations readouts coefficients form a purely random bipolar analog CS sampling matrix [Φ] that is pseudoorthogonal among m rows, where m ≅ 1.3k corresponding to the intrinsic sparse degree-of-freedom of the information.
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