A multichannel integrated circuit for neural spike detection based on EC-PC threshold estimation

2013 ◽  
Author(s):  
Tong Wu ◽  
Zhi Yang
Keyword(s):  
Integrated Circuit ◽  
Threshold Estimation ◽  
Spike Detection ◽  
Neural Spike

scholarly journals Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays

2017 ◽  
Vol 13 (11) ◽  
pp. e1005842 ◽  
Author(s):  
Gonzalo E. Mena ◽  
Lauren E. Grosberg ◽  
Sasidhar Madugula ◽  
Paweł Hottowy ◽  
Alan Litke ◽  
...  
Keyword(s):  
Electrical Stimulus ◽  
Electrode Arrays ◽  
Spike Detection ◽  
Neural Spike ◽  
Stimulus Artifact

scholarly journals 0.6 V, 116 nW Neural Spike Acquisition IC with Self-Biased Instrumentation Amplifier and Analog Spike Extraction

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2460 ◽  
Author(s):  
Jong Kim ◽  
Hankyu Lee ◽  
Hyoungho Ko
Keyword(s):  
Power Consumption ◽  
Integrated Circuit ◽  
Low Voltage ◽  
Energy Operator ◽  
Open Loop ◽  
Instrumentation Amplifier ◽  
Neural Spike ◽  
Self Bias ◽  
Ultralow Power ◽  
Ia Current

This paper presents an ultralow power 0.6 V 116 nW neural spike acquisition integrated circuit with analog spike extraction. To reduce power consumption, an ultralow power self-biased current-balanced instrumentation amplifier (IA) is proposed. The passive RC lowpass filter in the amplifier acts as both DC servo loop and self-bias circuit. The spike detector, based on an analog nonlinear energy operator consisting of a low-voltage open-loop differentiator and an open-loop gate-bulk input multiplier, is designed to emphasize the high frequency spike components nonlinearly. To reduce the spike detection error, the adjacent spike merger is also proposed. The proposed circuit achieves a low IA current consumption of 46.4 nA at 0.6 V, noise efficiency factor (NEF) of 1.81, the bandwidth from 102 Hz to 1.94 kHz, the input referred noise of 9.37 μVrms, and overall power consumption of 116 nW at 0.6 V. The proposed circuit can be used in the ultralow power spike pulses acquisition applications, including the neurofeedback systems on peripheral nerves with low neuron density.

scholarly journals Comparison of Sneo-Based Neural Spike Detection Algorithms for Implantable Multi-Transistor Array Biosensors

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 410
Author(s):  
Gerardo Saggese ◽  
Mattia Tambaro ◽  
Elia A. Vallicelli ◽  
Antonio G. M. Strollo ◽  
Stefano Vassanelli ◽  
...  
Keyword(s):  
Firing Rate ◽  
Energy Operator ◽  
Neuronal Firing ◽  
Capacitive Coupling ◽  
Detection Accuracy ◽  
Noise Power ◽  
Spike Detection ◽  
Low Snr ◽  
Detection Algorithms ◽  
Neural Spike

Real-time neural spike detection is an important step in understanding neurological activities and developing brain-silicon interfaces. Recent approaches exploit minimally invasive sensing techniques based on implanted complementary metal-oxide semiconductors (CMOS) multi transistors arrays (MTAs) that limit the damage of the neural tissue and provide high spatial resolution. Unfortunately, MTAs result in low signal-to-noise ratios due to the weak capacitive coupling between the nearby neurons and the sensor and the high noise power coming from the analog front-end. In this paper we investigate the performance achievable by using spike detection algorithms for MTAs, based on some variants of the smoothed non-linear energy operator (SNEO). We show that detection performance benefits from the correlation of the signals detected by the MTA pixels, but degrades when a high firing rate of neurons occurs. We present and compare different approaches and noise estimation techniques for the SNEO, aimed at increasing the detection accuracy at low SNR and making it less dependent on neurons firing rates. The algorithms are tested by using synthetic neural signals obtained with a modified version of NEUROCUBE generator. The proposed approaches outperform the SNEO, showing a more than 20% increase on averaged sensitivity at 0 dB and reduced dependence on the neuronal firing rate.

Neural Spike Detection Based on 1T1R Memristor

2020 ◽  
Author(s):  
Zhengwu Liu ◽  
Jianshi Tang ◽  
Bin Gao ◽  
He Qian ◽  
Huaqiang Wu
Keyword(s):  
Spike Detection ◽  
Neural Spike
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