A 0.6-107 μw energy-scalable processor for directly analyzing compressively-sensed EEG

IEEE Transactions on Circuits and Systems I: Regular Papers

Volumn 61, Issue 4, 2014, Pages 1105-1118

  Shoaib, Mohammed ^a   Lee, Kyong Ho ^a   Jha, Niraj K ^a   Verma, Naveen ^a  

^a Princeton University   (United States)

Author keywords

Circuits; compressive sensing; energy efficiency; machine learning; seizure detection; Signal processing

Indexed keywords

APPROXIMATION ALGORITHMS; BIOELECTRIC PHENOMENA; COMPRESSED SENSING; COST BENEFIT ANALYSIS; ELECTROENCEPHALOGRAPHY; ENERGY EFFICIENCY; ERRORS; LEARNING SYSTEMS; NETWORKS (CIRCUITS); SIGNAL ANALYSIS; SIGNAL PROCESSING;

APPLICATION-LEVEL PERFORMANCE; APPROXIMATION ERRORS; BASE-LINE PERFORMANCE; COMMUNICATION CONSTRAINTS; COMPRESSED DOMAIN PROCESSING; COMPRESSIVE SENSING; HARDWARE ARCHITECTURE; SEIZURE DETECTION;

BIOMEDICAL SIGNAL PROCESSING;

EID: 84897961689     PISSN: 15498328     EISSN: None     Source Type: Journal    
DOI: 10.1109/TCSI.2013.2285912     Document Type: Article

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