<html> <head> <meta http-equiv="content-type" content="text/html; charset=UTF-8"> </head> <body text="#000000" bgcolor="#CCFFFF"> The deadline to submit your proposals is fast approaching <div class="moz-forward-container">*********************************************************************** <div class="moz-forward-container"> <div class="moz-forward-container"> Apologize if you receive multiple copies of this message. Please disseminate this CFP to your colleagues and contacts. *********************************************************************** Special Issue on Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction Journal of Biomedical Research Submission deadline: January 15th, 2019 Epilepsy is the most common neurological disorder of the brain that affects people worldwide at any age from newborn to adult. It is characterized by recurrent seizures, which are brief episodes of signs or symptoms due to abnormal excessive or synchronous neuronal activity in the brain. The electroencephalogram, or EEG, is a physiological method to measure and record the electrical activities generated by the brain from electrodes placed on the surface of the scalp. EEG has become the most used signal for detecting and predicting epileptic seizures. Machine learning for EEG signal processing constitute an important area of artificial intelligence dealing with the setting up of automated computer-aided systems allowing to help the medical staff, e.g. neurophysiologists, for detecting and predicting epileptic seizure activities from EEG signals. It offers solutions to difficult biomedical engineering problems related to detecting and predicting EEG Epileptic seizures. In the light of the rapid development of machine learning tools for signal processing, this special issue aims to solicit original research papers as well as review articles focusing on recent advances in EEG signal processing and machine learning for Epileptic seizure detection and prediction. Topics of interest should be related to Epileptic seizure detection and/or prediction, and include (but are not limited to) the following: - EEG signal processing - Time-frequency EEG signal analysis - Non-stationary EEG signal analysis - EEG feature extraction and selection - Machine learning for EEG signals - EEG classification and clustering - Deep learning for EEG - EEG Big Data - EEG-based BCI (Brain-Computer Interface) - Internet of things for prediction - EEG-based computer-aideddiagnosis systems - Related applications Important Dates: Submission deadline: January 15th, 2019 Completion of first-round reviews: February 15th, 2019 Submission deadline for revised papers: March 15th, 2019 Final acceptance/rejection notification: March30th, 2019 Publication: May 2019 Submission Guidelines: - All submissions have to be prepared according to the Guide for Authors as published in the Journal Web Site: <a class="moz-txt-link-freetext" href="http://www.jbr-pub.org.cn" moz-do-not-send="true">http://www.jbr-pub.org.cn</a> - Submissions should be sent through: <a class="moz-txt-link-freetext" href="https://mc03.manuscriptcentral.com/jbrint" moz-do-not-send="true">https://mc03.manuscriptcentral.com/jbrint</a> - Authors should select the acronym "Special Issue: AESPMLESDP" as the article type, from the manuscript type menu during the submission process. Guest Editor: Dr. Larbi Boubchir, Associate Professor, LIASD research Lab. - University of Paris 8, France Email: <a class="moz-txt-link-abbreviated" href="mailto:larbi.boubchir@ai.univ-paris8.fr" moz-do-not-send="true">larbi.boubchir@ai.univ-paris8.fr</a> <blockquote type="cite" cite="mid:3f21d3d9-7675-c2f8-283b-13bcc6181f5a@ai.univ-paris8.fr"> </blockquote> <div class="moz-signature">-- <meta content="text/html; charset=UTF-8" http-equiv="content-type"> <title></title> _____________________________________________________ Larbi Boubchir, PhD, SMIEEE Associate Professor LIASD - University of Paris 8 2 rue de la Liberté, 93526 Saint-Denis, France Tel. (+33) 1 49 40 67 95 Email. <a class="moz-txt-link-abbreviated" href="mailto:larbi.boubchir@ai.univ-paris8.fr" moz-do-not-send="true">larbi.boubchir@ai.univ-paris8.fr</a> <a class="moz-txt-link-freetext" href="http://www.ai.univ-paris8.fr/~boubchir/" moz-do-not-send="true">http://www.ai.univ-paris8.fr/~boubchir/</a> _____________________________________________________ </div> </div> </div> </div> </body> </html>