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#DCNNs

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Joint Lab Bioelectronics<p>Automatically detecting emotions from <a href="https://mastodon.social/tags/EEGs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>EEGs</span></a> is expected to become a major task of <a href="https://mastodon.social/tags/BCIs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>BCIs</span></a>. However, inaccuracies, high error rates and a lack of stability still occupy <a href="https://mastodon.social/tags/research" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>research</span></a>. A research group has now succeeded in using Deep Convolutional Neural Networks <a href="https://mastodon.social/tags/DCNNs" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>DCNNs</span></a> to classify positive, neutral and negative <a href="https://mastodon.social/tags/emotions" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>emotions</span></a> from EEG signals with 96% accuracy by having volunteers listen to different music.<br><a href="https://mastodon.social/tags/Bioelectronics" class="mention hashtag" rel="nofollow noopener noreferrer" target="_blank">#<span>Bioelectronics</span></a> </p><p><a href="https://mdpi.com/2079-9292/12/10/2216" rel="nofollow noopener noreferrer" target="_blank"><span class="invisible">https://</span><span class="">mdpi.com/2079-9292/12/10/2216</span><span class="invisible"></span></a></p>
Joint Lab Bioelectronics<p>Emotionen aus <a href="https://social.tchncs.de/tags/EEGs" class="mention hashtag" rel="tag">#<span>EEGs</span></a> automatisch zu erkennen, soll zu einer wesentlichen Aufgabe von <a href="https://social.tchncs.de/tags/BCIs" class="mention hashtag" rel="tag">#<span>BCIs</span></a> werden. Ungenauigkeiten, hohe Fehlerquoten und mangelnde Stabilität beschäftigen allerdings noch die <a href="https://social.tchncs.de/tags/Forschung" class="mention hashtag" rel="tag">#<span>Forschung</span></a>. Einer Arbeitsgruppe gelang es nun durch Deep Convolutional Neural Networks <a href="https://social.tchncs.de/tags/DCNNs" class="mention hashtag" rel="tag">#<span>DCNNs</span></a> positive, neutrale und negative <a href="https://social.tchncs.de/tags/Emotionen" class="mention hashtag" rel="tag">#<span>Emotionen</span></a> aus EEG-Signalen mit 96%iger Genauigkeit zu klassifizieren indem sie Freiwillige verschiedene Musik hören ließen.<br /><a href="https://social.tchncs.de/tags/Bioelektronik" class="mention hashtag" rel="tag">#<span>Bioelektronik</span></a></p><p><a href="https://www.mdpi.com/2079-9292/12/10/2216" target="_blank" rel="nofollow noopener noreferrer" translate="no"><span class="invisible">https://www.</span><span class="">mdpi.com/2079-9292/12/10/2216</span><span class="invisible"></span></a></p>
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