Analysis and processing of in-vivo neural signal for artifact detection and removal

This paper analyses different types of artifacts that appear in neural recording experiments and thus a method is proposed to detect and remove artifacts as a part of preprocessing procedures before information decoding. Through modeling and data analysis, we reason that artifacts have different spectrum statistics compared with field potentials and spikes and the frequency bands of 150-400 Hz and >5 kHz are the most prospective regions to detect artifacts. A synthesized database based on recorded neural data and manually labeled artifacts has been built to allow quantitative evaluations of the proposed algorithm. Testing results have shown that over >80% positive detection ratio is achievable for artifacts with magnitude comparable to neural spikes. Quantitative signal-to-distortion ratio (SDR) simulation has shown that it is possible to have 10-30dB SDR improvement at waveform segments that contain artifacts.

[1]  Carlos Guerrero-Mosquera,et al.  Automatic removal of ocular artifacts from EEG data using adaptive filtering and Independent Component Analysis , 2009, 2009 17th European Signal Processing Conference.

[2]  Yan Long Wang,et al.  Automatic Removal of Ocular Artifacts from Electroencephalogram Using Hilbert-Huang Transform , 2008, 2008 2nd International Conference on Bioinformatics and Biomedical Engineering.

[3]  Qiao Xiaoyan,et al.  Motion artifact elimination using adaptive filter based on wavelet transform in pulse wave measurement , 2009, 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[4]  Petros Maragos,et al.  A comparison of the energy operator and the Hilbert transform approach to signal and speech demodulation , 1994, Signal Process..

[5]  Lihong Zhang,et al.  Method of Removing Noise from EEG Signals Based on HHT Method , 2009, 2009 First International Conference on Information Science and Engineering.

[6]  M. Tangermann,et al.  Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals , 2011, Behavioral and Brain Functions.

[7]  Herbert Bauer,et al.  Using ICA for removal of ocular artifacts in EEG recorded from blind subjects , 2005, Neural Networks.

[8]  Antti Savelainen,et al.  An introduction to EEG artifacts , 2010 .

[9]  Petros Maragos,et al.  On amplitude and frequency demodulation using energy operators , 1993, IEEE Trans. Signal Process..

[10]  J. Csicsvari,et al.  Massively parallel recording of unit and local field potentials with silicon-based electrodes. , 2003, Journal of neurophysiology.

[11]  Terrence J. Sejnowski,et al.  Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis , 2007, NeuroImage.

[12]  Bill Scott.  Developments in EEG Analysis, Protocol Selection, and Feedback Delivery , 2011 .

[13]  Rui Fonseca-Pinto,et al.  A New Tool for Nonstationary and Nonlinear Signals: The Hilbert-Huang Transform in Biomedical Applications , 2011 .

[14]  Rodica Strungaru,et al.  Independent Component Analysis Applied in Biomedical Signal Processing , 2004 .

[15]  Jack W. Tsao,et al.  Observed brain dynamics, P.P. Mitra, H. Bokil. Oxford University Press (2008), ISBN-13: 978-0-19-517808-1, 381 pages, $65.00 , 2009 .

[16]  H. Ahmadi-Noubari,et al.  Detection and removal of ocular artifacts using Independent Component Analysis and wavelets , 2009, 2009 4th International IEEE/EMBS Conference on Neural Engineering.

[17]  S. Mukhopadhyay,et al.  A new interpretation of nonlinear energy operator and its efficacy in spike detection , 1998, IEEE Transactions on Biomedical Engineering.