A closed-loop compressive-sensing-based neural recording system

Journal of Neural Engineering

Volumn 12, Issue 3, 2015, Pages

  Zhang, Jie ^a   Mitra, Srinjoy ^b   Suo, Yuanming ^a   Cheng, Andrew ^c   Xiong, Tao ^a   Michon, Frederic ^b   Welkenhuysen, Marleen ^b   Kloosterman, Fabian ^b   Chin, Peter S ^d   Hsiao, Steven ^c   Tran, Trac D ^a   Yazicioglu, Firat ^b   Etienne Cummings, Ralph ^a  

^a Johns Hopkins University   (United States)

^b IMEC   (Belgium)

^c Johns Hopkins University   (United States)

^d Draper Laboratories   (United States)

Author keywords

compressed sensing; compression; integrated circuit; neural recording; silicon probe

Indexed keywords

APPLICATION SPECIFIC INTEGRATED CIRCUITS; CMOS INTEGRATED CIRCUITS; COMPACTION; COMPRESSED SENSING; ELECTRODES; INTEGRATED CIRCUITS; NEURAL NETWORKS; NEUROPHYSIOLOGY; QUALITY CONTROL; READOUT SYSTEMS; RECORDING INSTRUMENTS; RECOVERY; SIGNAL PROCESSING; SIGNAL RECONSTRUCTION;

COMPRESSION METHODS; COMPRESSION RATES; COMPRESSIVE SENSING; LONG-TERM RECORDING; NEURAL RECORDING SYSTEMS; NEURAL RECORDINGS; SIGNAL PROCESSING CIRCUITS; SILICON PROBE;

DATA COMPRESSION;

ALGORITHM; ANIMAL EXPERIMENT; ARTICLE; CLOSED LOOP COMPRESSIVE SENSING BASED NEURAL RECORDING SYSTEM; ELECTRODE IMPLANT; INFORMATION PROCESSING; INTEGRATED CIRCUIT; MEDICAL INFORMATION SYSTEM; NEUROFEEDBACK; NEUROLOGIC EXAMINATION; NONHUMAN; PRIORITY JOURNAL; RECORDING; RHESUS MONKEY; SIGNAL NOISE RATIO; SIGNAL PROCESSING; SPIKE; WAVEFORM; ANALOG DIGITAL CONVERTER; ANIMAL; BRAIN; DEVICE FAILURE ANALYSIS; DEVICES; ELECTROENCEPHALOGRAPHY; EQUIPMENT DESIGN; EVALUATION STUDY; FEEDBACK SYSTEM; PHYSIOLOGY; POWER SUPPLY; PROCEDURES; RAT; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ANALOG-DIGITAL CONVERSION; ANIMALS; BRAIN; DATA COMPRESSION; ELECTRIC POWER SUPPLIES; ELECTROENCEPHALOGRAPHY; EQUIPMENT DESIGN; EQUIPMENT FAILURE ANALYSIS; FEEDBACK; RATS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SIGNAL-TO-NOISE RATIO;

EID: 84930225020     PISSN: 17412560     EISSN: 17412552     Source Type: Journal    
DOI: 10.1088/1741-2560/12/3/036005     Document Type: Article

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