Low power consumption per channel and data rate minimization are two key challenges which need to be addressed in future generations of neural recording systems (NRS). Power consumption can be reduced by avoiding unnecessary processing whereas data rate is greatly decreased by sending spike time-stamps along with spike features as opposed to raw digitized data. Dynamic range in NRS can vary with time due to change in electrode-neuron distance or background noise, which demands adaptability. An analog-to-digital converter (ADC) is one of the most important blocks in a NRS. This paper presents an 8-bit SAR ADC in 0.13-mu m CMOS technology along with input and reference buffer. A novel energy efficient digital-to-analog converter switching sch...
This paper presents a novel 64-channel ultra-low power/low noise neural recording System-on-Chip (So...
This paper describes a low-power 25-kS/s successive approximation register (SAR) analog-to-digital c...
Abstract — We present a fully differential 128-channel inte-grated neural interface. It consists of ...
This paper presents a 9-bit 25 kS/s SAR ADC in 0.18 μm CMOS technology for neural signal recording a...
This thesis presents an ultra-low-power 8-bit asynchronous current-modesuccessive approximation (SAR...
This paper presents a fully differential multi-channel neural recording system. The system consists ...
This paper presents a 10-bit successive approximation register analog-to-digital converter with ener...
This paper presents a fully integrated 64-channel neural recording system for local field potential ...
The paper presents a novel topology of a continuous-time analogue-to-digital converter (CT-ADC) feat...
Design of a low power Successive Approximation Register Analog to Digital Converter (SAR ADC) in 45n...
Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces...
This paper reports on a predictive analog-todigital converter (ADC). The proposed ADC employs a line...
This work presents an ultra-low power 10-bit, 1-KS/s successive approximation register (SAR) analog-...
This paper presents an analog-to-digital converter (ADC) dedicated to neural recording systems. By u...
In sensing systems, ADCs are widely used in sensor interface circuits to convert analog signals to d...
This paper presents a novel 64-channel ultra-low power/low noise neural recording System-on-Chip (So...
This paper describes a low-power 25-kS/s successive approximation register (SAR) analog-to-digital c...
Abstract — We present a fully differential 128-channel inte-grated neural interface. It consists of ...
This paper presents a 9-bit 25 kS/s SAR ADC in 0.18 μm CMOS technology for neural signal recording a...
This thesis presents an ultra-low-power 8-bit asynchronous current-modesuccessive approximation (SAR...
This paper presents a fully differential multi-channel neural recording system. The system consists ...
This paper presents a 10-bit successive approximation register analog-to-digital converter with ener...
This paper presents a fully integrated 64-channel neural recording system for local field potential ...
The paper presents a novel topology of a continuous-time analogue-to-digital converter (CT-ADC) feat...
Design of a low power Successive Approximation Register Analog to Digital Converter (SAR ADC) in 45n...
Chronic recording of neural signals is indispensable in designing efficient brain machine interfaces...
This paper reports on a predictive analog-todigital converter (ADC). The proposed ADC employs a line...
This work presents an ultra-low power 10-bit, 1-KS/s successive approximation register (SAR) analog-...
This paper presents an analog-to-digital converter (ADC) dedicated to neural recording systems. By u...
In sensing systems, ADCs are widely used in sensor interface circuits to convert analog signals to d...
This paper presents a novel 64-channel ultra-low power/low noise neural recording System-on-Chip (So...
This paper describes a low-power 25-kS/s successive approximation register (SAR) analog-to-digital c...
Abstract — We present a fully differential 128-channel inte-grated neural interface. It consists of ...