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                        <p><font color="#3333ff"><b>Due to a number of
                              extension requests, the deadline is
                              extended to February 15th, 2019.</b></font></p>
                        <p><b>__________________________</b></p>
                        <p><b>Special Issue on Advances in EEG Signal
                            Processing and Machine Learning for
                            Epileptic Seizure Detection and Prediction</b></p>
                        <b>Journal of Biomedical Research </b><br>
                        <u>Submission deadline: <font color="#3333ff">February</font></u><u><font
                            color="#3333ff"> 15th, 2019</font></u><b> </b><br>
                        <p>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.</p>
                        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.<br>
                        Topics of interest should be related to
                        Epileptic seizure detection and/or prediction,
                        and include (but are not limited to) the
                        following:<br>
                        - EEG signal processing<br>
                        - Time-frequency EEG signal analysis<br>
                        - Non-stationary EEG signal analysis<br>
                        - EEG feature extraction and selection<br>
                        - Machine learning for EEG signals<br>
                        - EEG classification and clustering<br>
                        - Deep learning for EEG<br>
                        - EEG Big Data<br>
                        - EEG-based BCI (Brain-Computer Interface)<br>
                        - Internet of things for prediction<br>
                        - EEG-based computer-aideddiagnosis systems<br>
                        - Related applications<br>
                        <br>
                        <b>Important Dates:</b><br>
                        Submission deadline: <b><font color="#3333ff">February
                            15th, 2019</font></b><br>
                        Completion of first-round reviews: March 15th,
                        2019<br>
                        Submission deadline for revised papers: April
                        15th, 2019<br>
                        Final acceptance/rejection notification: May
                        15th, 2019<br>
                        Publication: May 2019<b><br>
                        </b><br>
                        <b>Submission Guidelines:</b><br>
                        - 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><br>
                        - 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><br>
                        - Authors should select the acronym "Special
                        Issue: <font color="#3333ff"><b>AESPMLESDP</b></font>"
                        as the article type, from the manuscript type
                        menu during the submission process.<br>
                        <br>
                        <b>Guest Editor:</b><br>
                        Dr. Larbi Boubchir, Associate Professor, LIASD
                        research Lab. - University of Paris 8, France <br>
                        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><br>
                        <br>
                        <blockquote type="cite"
                          cite="mid:3f21d3d9-7675-c2f8-283b-13bcc6181f5a@ai.univ-paris8.fr">
                        </blockquote>
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                          <small><span style="color: rgb(102, 102,
                              102);">_____________________________________________________</span><br
                              style="color: rgb(102, 102, 102);">
                            <span style="color: rgb(102, 102, 102);">Larbi
                              Boubchir, PhD, SMIEEE</span><br
                              style="color: rgb(102, 102, 102);">
                            <span style="color: rgb(102, 102, 102);">Associate
                              Professor</span><br style="color: rgb(102,
                              102, 102);">
                            <br style="color: rgb(102, 102, 102);">
                            <span style="color: rgb(102, 102, 102);">LIASD
                              - University of Paris 8</span><br
                              style="color: rgb(102, 102, 102);">
                            <span style="color: rgb(102, 102, 102);">2
                              rue de la Liberté, 93526 Saint-Denis,
                              France</span><br style="color: rgb(102,
                              102, 102);">
                            <span style="color: rgb(102, 102, 102);">Tel.
                              (+33) 1 49 40 67 95</span><br
                              style="color: rgb(102, 102, 102);">
                            <span style="color: rgb(102, 102, 102);">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></span><br
                              style="color: rgb(102, 102, 102);">
                            <span style="color: rgb(102, 102, 102);"><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></span><br
                              style="color: rgb(102, 102, 102);">
                            <span style="color: rgb(102, 102, 102);">_____________________________________________________</span></small><br>
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