TY - JOUR
T1 - On the robustness of EC-PC spike detection method for online neural recording
AU - Zhou, Yin
AU - Wu, Tong
AU - Rastegarnia, Amir
AU - Guan, Cuntai
AU - Keefer, Edward
AU - Yang, Zhi
PY - 2014/9/30
Y1 - 2014/9/30
N2 - Background: Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. New method: We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13. μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Results: Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Comparison with existing methods: Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. Conclusion: The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation.
AB - Background: Online spike detection is an important step to compress neural data and perform real-time neural information decoding. An unsupervised, automatic, yet robust signal processing is strongly desired, thus it can support a wide range of applications. We have developed a novel spike detection algorithm called "exponential component-polynomial component" (EC-PC) spike detection. New method: We firstly evaluate the robustness of the EC-PC spike detector under different firing rates and SNRs. Secondly, we show that the detection Precision can be quantitatively derived without requiring additional user input parameters. We have realized the algorithm (including training) into a 0.13. μm CMOS chip, where an unsupervised, nonparametric operation has been demonstrated. Results: Both simulated data and real data are used to evaluate the method under different firing rates (FRs), SNRs. The results show that the EC-PC spike detector is the most robust in comparison with some popular detectors. Moreover, the EC-PC detector can track changes in the background noise due to the ability to re-estimate the neural data distribution. Comparison with existing methods: Both real and synthesized data have been used for testing the proposed algorithm in comparison with other methods, including the absolute thresholding detector (AT), median absolute deviation detector (MAD), nonlinear energy operator detector (NEO), and continuous wavelet detector (CWD). Comparative testing results reveals that the EP-PC detection algorithm performs better than the other algorithms regardless of recording conditions. Conclusion: The EC-PC spike detector can be considered as an unsupervised and robust online spike detection. It is also suitable for hardware implementation.
KW - ASIC implementation
KW - EC-PC
KW - Precision of detection
KW - Spike detection
UR - http://www.scopus.com/inward/record.url?scp=84907331662&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84907331662&partnerID=8YFLogxK
U2 - 10.1016/j.jneumeth.2014.07.006
DO - 10.1016/j.jneumeth.2014.07.006
M3 - Article
C2 - 25088692
AN - SCOPUS:84907331662
VL - 235
SP - 316
EP - 330
JO - Journal of Neuroscience Methods
JF - Journal of Neuroscience Methods
SN - 0165-0270
ER -