Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification

2014 ◽  
Author(s):  
Elie Bou Assi ◽  
Sandy Rihana ◽  
Mohamad Sawan
Keyword(s):  
Automatic Method ◽  
Artifacts Removal ◽  
Ocular Artifacts

Automatic ocular artifacts removal in EEG using deep learning

2018 ◽  
Vol 43 ◽  
pp. 148-158 ◽  
Author(s):  
Banghua Yang ◽  
Kaiwen Duan ◽  
Chengcheng Fan ◽  
Chenxiao Hu ◽  
Jinlong Wang
Keyword(s):  
Deep Learning ◽  
Artifacts Removal ◽  
Ocular Artifacts

A hybrid ICA Kalman predictor algorithm for ocular artifacts removal

2020 ◽  
Vol 23 (4) ◽  
pp. 727-735
Author(s):  
Shyam Prasad Devulapalli ◽  
Srinivasa Rao Chanamallu ◽  
Satya Prasad Kodati
Keyword(s):  
Artifacts Removal ◽  
Kalman Predictor ◽  
Ocular Artifacts

Real-time ocular artifacts removal of EEG data using a hybrid ICA-ANC approach

2017 ◽  
Vol 31 ◽  
pp. 199-210 ◽  
Author(s):  
Aysa Jafarifarmand ◽  
Mohammad-Ali Badamchizadeh ◽  
Sohrab Khanmohammadi ◽  
Mohammad Ali Nazari ◽  
Behzad Mozaffari Tazehkand
Keyword(s):  
Real Time ◽  
Artifacts Removal ◽  
Eeg Data ◽  
Ocular Artifacts

scholarly journals Ocular Artifacts Removal from EEG Using EMD

2008 ◽  
pp. 831-835 ◽  
Author(s):  
David Looney ◽  
Ling Li ◽  
Tomasz M. Rutkowski ◽  
Danilo P. Mandic ◽  
Andrzej Cichocki
Keyword(s):  
Artifacts Removal ◽  
Ocular Artifacts

Automatic measurement of SAED patterns from asbestos minerals

1988 ◽  
Vol 46 ◽  
pp. 846-847
Author(s):  
J. C. Russ ◽  
T. Taguchi ◽  
P. M. Peters ◽  
E. Chatfield ◽  
J. C. Russ ◽  
...  
Keyword(s):  
Diffraction Pattern ◽  
High Precision ◽  
Diffraction Data ◽  
Automatic Measurement ◽  
Automatic Method ◽  
Important Method ◽  
X Ray ◽  
Integrated Intensity ◽  
Asbestiform Minerals ◽  
True Center

Conventional SAD patterns as obtained in the TEM present difficulties for identification of materials such as asbestiform minerals, although diffraction data is considered to be an important method for making this purpose. The preferred orientation of the fibers and the spotty patterns that are obtained do not readily lend themselves to measurement of the integrated intensity values for each d-spacing, and even the d-spacings may be hard to determine precisely because the true center location for the broken rings requires estimation. We have implemented an automatic method for diffraction pattern measurement to overcome these problems. It automatically locates the center of patterns with high precision, measures the radius of each ring of spots in the pattern, and integrates the density of spots in that ring. The resulting spectrum of intensity vs. radius is then used just as a conventional X-ray diffractometer scan would be, to locate peaks and produce a list of d,I values suitable for search/match comparison to known or expected phases.

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