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              <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: </u><u><font color="#3333ff">February
                  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: April 30th, 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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