Title :
A 330μW, 64-channel neural recording sensor with embedded spike feature extraction and auto-calibration
Author :
Rodriguez-Perez, Alberto ; Delgado-Restituto, Manuel ; Darie, Angela ; Soto-Sanchez, Cristina ; Fernandez-Jover, Eduardo ; Rodriguez-Vazquez, Angel
Author_Institution :
Inst. of Microelectron. of Sevilla (IMSE-CNM), Univ. of Sevilla, Sevilla, Spain
Abstract :
This paper reports an integrated 64-channel neural recording sensor. Neural signals are acquired, filtered, digitized and compressed in the channels. Additionally, each channel implements an auto-calibration mechanism which configures the transfer characteristics of the recording site. The system has two transmission modes; in one case the information captured by the channels is sent as uncompressed raw data; in the other, feature vectors extracted from the detected neural spikes are released. Data streams coming from the channels are serialized by an embedded digital processor. Experimental results, including in vivo measurements, show that the power consumption of the complete system is lower than 330μW.
Keywords :
biomedical electronics; biosensors; brain; feature extraction; microprocessor chips; neural chips; prosthetics; autocalibration mechanism; data streams; digital processor; feature vectors; in vivo measurements; integrated neural recording sensor; neural signals; neural spikes; power 330 muW; power consumption; spike feature extraction; Arrays; Calibration; Feature extraction; Noise; Power demand; Wireless communication; Wireless sensor networks;
Conference_Titel :
Solid-State Circuits Conference (A-SSCC), 2014 IEEE Asian
Conference_Location :
KaoHsiung
Print_ISBN :
978-1-4799-4090-5
DOI :
10.1109/ASSCC.2014.7008896
