Combining sparsity with rank-deficiency for energy efficient EEG sensing and transmission over Wireless Body Area Network

In Wireless Body Area Networks (WBAN) the energy consumption is dominated by sensing and communication. Previous techniques exploited the sparsity of the signal (in transform domains) to reduce communication costs for EEG transmission. For the first time, in this work, we propose to jointly exploit sparsity and rank-deficiency of the multi-channel signal ensemble in order to reduce both sensing and communication power consumptions. We test our method with state-of-the-art recovery techniques and find that the reconstruction accuracy from our method is considerably better and that too at lower energy consumption.