Hesham Mahrous

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This paper proposes a compressive sensing (CS) method for multi-channel electroencephalogram (EEG) signals in Wireless Body Area Network (WBAN) applications, where the battery life of sensors is limited. For the single EEG channel case, known as the single measurement vector (SMV) problem, the Block Sparse Bayesian Learning-BO (BSBL-BO) method has been(More)
The energy required for Electroencephalography (EEG) transmission at the sensor node, in Wireless Body Area Networks (WBANs), is high relative to the sensor's battery size. The power consumption is dominated by sensing, processing and data transmission of the EEG. Many successful solutions based on Compressed Sensing (CS) and other techniques have been(More)
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