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<a href="https://www.linkedin.com/company/essex-bcine-lab/" target="_blank" id="OWAd025f8db-e9af-47db-ac27-93ad98a4ebf0" class="x_x_x_x_x_x_x_x_OWAAutoLink" title="https://www.linkedin.com/company/essex-bcine-lab/" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="0" data-olk-copy-source="MessageBody" style="color: rgb(70, 120, 134); margin: 0px;"><u>https://www.linkedin.com/company/essex-bcine-lab/</u></a></div>
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The Essex BCI-NE Lab invites you to join our next monthly webinar:</div>
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<b>EEG markers of chronic pain: repeatability challenges</b></div>
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Delivered by</div>
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<b>Prof Aleksandra Vuckovic</b></div>
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James Watt School of Engineering, University of Glasgow, Scotland, United Kingdom</div>
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The webinar will take place over Zoom on Wednesday, 15th April 2026, at 2 PM UK time </div>
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RSVP: <a href="https://www.linkedin.com/events/7447896566465667073?viewAsMember=true" target="_blank" id="OWA0f851412-b372-0f94-ee5f-5177c8813a7d" class="x_OWAAutoLink" title="https://www.linkedin.com/events/7447896566465667073?viewAsMember=true" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="1" style="margin: 0px;">
<u>https://www.linkedin.com/events/7447896566465667073</u></a></div>
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<b>Abstract:</b> Electroencephalography (EEG) holds significant potential for identifying diagnostic, predictive, and prognostic biomarkers across a range of neurological conditions. Most EEG-based biomarkers rely on developing classifiers that can distinguish
individuals with specific neurological disorders. Typically, classifier performance is validated using leave-one-out or N-fold cross-validation, where training and testing datasets are collected under the same conditions—recorded in the same environment, by
the same experimenters, and using the same EEG equipment. However, real-world clinical applications require accurate classification of entirely unseen datasets that do not share hidden common features with the training data. Most classifiers based on N-fold
cross-validation fail to repeat performance on a new dataset. Using the example of central neuropathic pain, Prof. Vuckovic will illustrate the challenges involved in developing a machine learning approach capable of classifying unseen datasets of comparable
size to the training set, collected at different times and under varying environmental conditions.</div>
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<span style="font-size: 12pt;"><b>Speaker Biography</b>: </span><span style="font-size: 16px; background-color: rgb(255, 255, 255);">Aleksandra Vuckovic holds an MEng in Engineering Physics and an MSc in Control Systems from the University of Belgrade. She
received her Ph.D. from the Centre for Sensory-Motor Interaction, Aalborg University, Denmark in 2004, where she has also served as post-doc. Between 2005 and 2007 she worked as a senior research officer at the Brain-Computer Interface Group, Department of
Computer Science, University of Essex. Since 2008 she has been with the James Watt School of Engineering, University of Glasgow. Aleksandra is interested in Brain-Computer Interface (BCI) as a new form of non-muscular channel for communication between patients
and their environment, which compensates a loss of sensory-motor functions or alternatively provides strengthening or modulation of preserved neuro-muscular pathways in patients with impairments of the Central Nervous System. She is in particular interested
in developing and implementing patient-managed Brain-Computer Interface systems for home-based neurorehabilitation. Further areas of interest are improvements of existing rehabilitation therapies combining Brain-Computer Interface and Functional Electrical
Stimulation and exploring a correlation between the recovery and changes in the brain responses i.e., brain plasticity. She also applies BCI for operant conditioning training of the brain (neurofeedback) for the treatment of Central Neuropathic pain following
injuries to the Spinal Cord. Her research further focuses on EEG as a neuroimaging modality to identify markers of neuropathic pain (diagnostic, predictive, and prognostic). She uses machine learning techniques to develop transferable individualized markers
of pain to aid stratified pain prevention and treatment.</span></div>
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The <b>Essex BCI-NE Lab webinars series</b> takes place monthly (usually, on the second or third Wednesday of the month) over Zoom and is open to all. Speakers are invited to talk about their research for 45-50 minutes followed by Q&A.</div>
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<span style="color: rgb(0, 0, 0);">Where speakers allow it, we record the talks and make them available to everyone on our YouTube channel. You can watch previous talks at:
</span><span style="color: rgb(70, 120, 134);"><a href="https://www.youtube.com/@essexbcis" target="_blank" id="OWA587dc2ec-6649-0bce-28a2-74aad46b3583" class="x_x_x_x_x_x_x_x_OWAAutoLink" title="https://www.youtube.com/@essexbcis" rel="noopener noreferrer" data-auth="NotApplicable" data-linkindex="2" style="color: rgb(70, 120, 134); margin: 0px;"><u>https://www.youtube.com/@essexbcis</u></a></span></div>
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If you don’t want to miss our next webinars, please email serafeim.perdikis@essex.ac.uk to ask to be added to our webinars mailing list.</div>
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Best wishes,</div>
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Simis</div>
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<div class="elementToProof" style="color: rgb(0, 0, 0);"><span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">Dr Serafeim Perdikis, Associate Professor (Senior Lecturer)</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt;"><br>
</span><span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">Brain-Computer Interfaces and Neural Engineering Laboratory</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt;"><br>
</span><span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">School of Computer Science and Electronic Engineering</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt;"><br>
</span><span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">University of Essex</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt;"><br>
</span><span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">Wivenhoe Park, Colchester CO4 3SQ</span><span style="font-family: Aptos, Aptos_EmbeddedFont, Aptos_MSFontService, Calibri, Helvetica, sans-serif; font-size: 12pt;"><br>
</span><span style="font-family: Calibri, Arial, Helvetica, sans-serif; font-size: 16px; background-color: rgb(255, 255, 255);">United Kingdom</span></div>
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