A deep learning framework of quantized compressed sensing for wireless neural recording
B Sun, H Feng, K Chen, X Zhu - IEEE Access, 2016 - ieeexplore.ieee.org
In low-power wireless neural recording tasks, signals must be compressed before
transmission to extend battery life. Recently, compressed sensing (CS) theory has …
transmission to extend battery life. Recently, compressed sensing (CS) theory has …
A deep learning method for improving the classification accuracy of SSMVEP-based BCI
Steady State Motion Visual Evoked Potential (SSMVEP)-based Brain Computer Interface
(BCI) is widely studied and has been used to varies of occasions on account of its good …
(BCI) is widely studied and has been used to varies of occasions on account of its good …
A configurable energy-efficient compressed sensing architecture with its application on body sensor networks
The past decades have witnessed a rapid surge in new sensing and monitoring devices for
well-being and healthcare. One key representative in this field is body sensor networks …
well-being and healthcare. One key representative in this field is body sensor networks …
FB dictionary based SSBL-EM and its application for multi-class SSVEP classification using eight-channel EEG signals
Many applications of signal processing require efficient reconstruction and economical
processing of a signal. A classical approach for efficient reconstruction and economical …
processing of a signal. A classical approach for efficient reconstruction and economical …
Quantized compressive sensing for low-power data compression and wireless telemonitoring
In low-power wireless telemonitoring, physiological signals must be compressed before
transmission to extend battery life. In this paper, we propose a two-stage data compressor …
transmission to extend battery life. In this paper, we propose a two-stage data compressor …
Ultra-low power dynamic knob in adaptive compressed sensing towards biosignal dynamics
Compressed sensing (CS) is an emerging sampling paradigm in data acquisition. Its
integrated analog-to-information structure can perform simultaneous data sensing and …
integrated analog-to-information structure can perform simultaneous data sensing and …
Selective CS: An energy-efficient sensing architecture for wireless implantable neural decoding
The spike classification is a critical step in the implantable neural decoding. The energy
efficiency issue in the sensor node is a big challenge for the entire system. Compressive …
efficiency issue in the sensor node is a big challenge for the entire system. Compressive …
Adaptive compressed sensing architecture in wireless brain-computer interface
Wireless sensor nodes advance the brain-computer interface (BCI) from laboratory setup to
practical applications. Compressed sensing (CS) theory provides a sub-Nyquist sampling …
practical applications. Compressed sensing (CS) theory provides a sub-Nyquist sampling …
A tempo-spatial compressed sensing architecture for efficient high-throughput information acquisition in organs-on-a-chip
As a micro engineered biomimetic system to replicate key functions of living organs, organs-
on-a-chip (OC) technology provides a high-throughput model for investigating complex cell …
on-a-chip (OC) technology provides a high-throughput model for investigating complex cell …
Bayesian De-quantization and Data Compression for Low-Energy Physiological Signal Telemonitoring
We address the issue of applying quantized compressed sensing (CS) on low-energy
telemonitoring. So far, few works studied this problem in applications where signals were …
telemonitoring. So far, few works studied this problem in applications where signals were …