IEEE Transactions on Circuits and Systems I Regular Papers · 2013 · 129 citations · 27 references
EngineeringBiomedical EngineeringElectrical EngineeringAnalog ElectronicsInstrumentationNeural EngineeringAnalog Integrated CircuitsBiomedical InstrumentationImplantable Biomedical DevicesImplantable Neural DevicesNeural InterfacesNeural Recording TechnologiesAnalog Front-end CircuitsImplantable Neural InterfacesInput-referred NoiseNeural RecordingNeural Recording SystemsNeural Signal AmplificationNeural Signal RecordingPower Management Circuits
This paper presents a fully implantable 100-channel neural interface IC for neural activity monitoring. It contains 100-channel analog recording front-ends, 10 multiplexing successive approximation register ADCs, digital control modules and power management circuits. A dual sample-and-hold architecture is proposed, which extends the sampling time of the ADC and reduces the average power per channel by more than 50% compared to the conventional multiplexing neural recording system. A neural amplifier (NA) with current-reuse technique and weak inversion operation is demonstrated, consuming 800 nA under 1-V supply while achieving an input-referred noise of 4.0 μV <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rms</sub> in a 8-kHz bandwidth and a NEF of 1.9 for the whole analog recording chain. The measured frequency response of the analog front-end has a high-pass cutoff frequency from sub-1 Hz to 248 Hz and a low-pass cutoff frequency from 432 Hz to 5.1 kHz, which can be configured to record neural spikes and local field potentials simultaneously or separately. The whole system was fabricated in a 0.18-μm standard CMOS process and operates under 1 V for analog blocks and ADC, and 1.8 V for digital modules. The number of active recording channels is programmable and the digital output data rate changes accordingly, leading to high system power efficiency. The overall 100-channel interface IC consumes 1.16-mW total power, making it the optimum solution for multi-channel neural recording systems.
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Neuronal ensemble control of prosthetic devices by a human with tetraplegia
Leigh R. Hochberg, Mijail D. Serruya, Gerhard M. Friehs et al. · Nature · 2006 · 3.3K citations
Christian Enz, Fran�ois Krummenacher, Eric A. Vittoz · Analog Integrated Circuits and Signal Processing · 1995 · 1.2K citations · Full text
Engineering, Electrical Engineering, Electronic Materials +13