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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Aosen Wang Chen Song Xiaowei Xu Feng Lin Zhanpeng Jin Wenyao Xu |
| Copyright Year | 2015 |
| Description | Author affiliation: ECE Dept., SUNY at Binghamton, Binghamton, NY, USA (Zhanpeng Jin) || CSE Dept., SUNY at Buffalo, Buffalo, NY, USA (Aosen Wang; Chen Song; Xiaowei Xu; Feng Lin; Wenyao Xu) |
| Abstract | The spike classification is a critical step in implantable neural decoding. The energy efficiency issue in the sensor node is a big challenge in the entire system. Compressive sensing (CS) provides a potential way to tackle this problem. However, the overhead of signal reconstruction constrains the compression in sensor node and analysis in remote server. In this paper, we design a new selective CS architecture for wireless implantable neural decoding. We implement all the signal analysis on the compressed domain. To achieve better energy efficiency, we propose a two-stage classification procedure, including a coarse-grained screening module with softmax regression and a fine-grained analysis module based on deep learning. The screening module completes the low-effort classification task in the front-end and transmits the compressed data of high-effort task to remote server for fine-grained analysis. Experimental results indicate that our selective CS architecture can gain more than 50% energy savings, yet keeping the high accuracy as state-of-the-art CS architectures. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 1108555 |
| Page Count | 4 |
| File Format | |
| e-ISBN | 9781479972340 |
| DOI | 10.1109/BioCAS.2015.7348375 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-10-22 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Wireless communication Wireless sensor networks Quantization (signal) Machine learning Decoding Servers Compressed sensing |
| Content Type | Text |
| Resource Type | Article |
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