Notice of Violation of IEEE Publication Principles: Low power 16-channel electronic neuronal brain interface design

Notice of Violation of IEEE Publication Principles

"Low Power 16-channel Electronic Neuronal Brain Interface Design"
by Wei Cai and NanSong Wu
in the Proceedings of the IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), January 2018, pp.786-795

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.

This paper contains original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

"Automatic Spike Sorting for Extracellular Electrophysiological Recording using Unsupervised Single Linkage Clustering Based on Grey Relational Analysis"
by Hsin-Yi Lai, You-Yin Chen, Sheng-Huang Lin, Yu-Chun Lo, Siny Tsang, Shin-Yuan Chen, Wan-Ting Zhao, Wen-Hung Chao, Yao-Chuan Chang, RobbyWu, Yen-Yu I Shih, Sheng-Tsung Tsai and Fu-Shan Jaw
in the Journal of Neural Engineering, 8, 2011


Objective: The purpose of this study is to provide a connection between the neural behavior and external behavior of animals for potential brain machine interface (BMI) and other further applications. Methods: The present work proposes a 16-channel neural recording based on hardware-software co-design. The spike detection and feature extraction of the neural signal processor on this system is implemented in hardware design, and the clustering and classification of the neural spike sorter is designed in software. The two-stage spike detection, combining the benefit of threshold method and nonlinear energy operator, is presented as the initial step of the spike sorting process. The adaptive threshold and the feedback rule are utilized for adaptively adjusting the threshold and the discriminate factor with fluctuant surroundings. Results: The results suggest a significant improvement for spike separation on feature space, with the help of the discrete derivative method. Conclusion: The proposed spike sorting system is feasible as the first step for neuroscientist to figure out the brain function of animals. The accuracy of the spike sorting was enhanced on an indistinguishable data set from the result. Significance: The proposed spike sorting system could simultaneously record 16 channel neuronal activities on real-time and the whole spike sorting process is automatic without any supervision.