Notice of Violation of IEEE Publication Principles: Software-hardware cosystem brain interface desig

Notice of Violation of IEEE Publication Principles

"Software-Hardware Cosystem Brain Interface Design"
by Wei Cai, NanSong Wu, Frank Shi, and Jialing Tong
in 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), November 2017, pp.115-121

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 copied content 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


Brain Machine Interface (BMI) is a spike sorting provide a connection between the external behavior and neural behavior of animals. Moreover, the spike sorting is significant for stability of the advanced application. To detect neuronal activity, multichannel recording is one of major methods. This paper proposed a software-hardware co-design framework with a 16- channel neural recording. Two-stage spike detection usually included a threshold method and a nonlinear energy operator (NEO). The spike clustering used the feature extraction. This multichannel spike sorting system algorithm were verified by simulations data and experiments results. The results presented a significant improvement on feature space during spike separation, due to the discrete derivative method.