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An Energy Efficient Brain-Computer Interface using Mr.Wolf - Revision history
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Herschmi at 09:09, 21 July 2020
2020-07-21T09:09:19Z
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Description==</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>==Description==</div></td></tr>
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Herschmi
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Herschmi: /* Status: In progress */
2019-10-29T14:16:17Z
<p><span dir="auto"><span class="autocomment">Status: In progress</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:16, 29 October 2019</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Tibor Schneider</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Tibor Schneider</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>: Supervision: [[:User:Herschmi | Michael Hersche]],[[:User:xiaywang|Xiaying Wang]],[[:User:lukasc|Lukas Cavigelli]]</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>: Supervision: [[:User:Herschmi | Michael Hersche]], [[:User:xiaywang|Xiaying Wang]], [[:User:lukasc|Lukas Cavigelli]]</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr>
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Herschmi
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Herschmi: /* Literature */
2019-10-29T14:16:02Z
<p><span dir="auto"><span class="autocomment">Literature</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:16, 29 October 2019</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l30" >Line 30:</td>
<td colspan="2" class="diff-lineno">Line 30:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://ieeexplore.ieee.org/document/8553378] Hersche et al., Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://ieeexplore.ieee.org/document/8553378] Hersche et al., Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://www.ncbi.nlm.nih.gov/pubmed/28782865] Schirrmeister et al., Deep learning with convolutional neural networks for EEG decoding and visualization</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://www.ncbi.nlm.nih.gov/pubmed/28782865] Schirrmeister et al., Deep learning with convolutional neural networks for EEG decoding and visualization</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [http://stacks.iop.org/1741-2552/15/i=5/a=056013?key=crossref.926d8be8d11477a9c1ce0a164d5c869a] Lawhern et al., EEGNet: a compact convolutional neural network for EEG-based</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [http://stacks.iop.org/1741-2552/15/i=5/a=056013?key=crossref.926d8be8d11477a9c1ce0a164d5c869a] Lawhern et al., EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>brain–computer interfaces</div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://ieeexplore.ieee.org/document/8494247/] Pullini et al., Mr. Wolf: A 1 GFLOP/s Energy-Proportional Parallel Ultra Low Power SoC for IOT Edge Processing</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://ieeexplore.ieee.org/document/8494247/] Pullini et al., Mr. Wolf: A 1 GFLOP/s Energy-Proportional Parallel Ultra Low Power SoC for IOT Edge Processing</div></td></tr>
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Herschmi
http://iis-projects.ee.ethz.ch/index.php?title=An_Energy_Efficient_Brain-Computer_Interface_using_Mr.Wolf&diff=4977&oldid=prev
Herschmi: /* Literature */
2019-10-29T14:15:52Z
<p><span dir="auto"><span class="autocomment">Literature</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td>
<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 14:15, 29 October 2019</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l30" >Line 30:</td>
<td colspan="2" class="diff-lineno">Line 30:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://ieeexplore.ieee.org/document/8553378] Hersche et al., Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://ieeexplore.ieee.org/document/8553378] Hersche et al., Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://www.ncbi.nlm.nih.gov/pubmed/28782865] Schirrmeister et al., Deep learning with convolutional neural networks for EEG decoding and visualization</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://www.ncbi.nlm.nih.gov/pubmed/28782865] Schirrmeister et al., Deep learning with convolutional neural networks for EEG decoding and visualization</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>* [http://stacks.iop.org/1741-2552/15/i=5/a=056013?key=</div></td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>* [http://stacks.iop.org/1741-2552/15/i=5/a=056013?key=crossref.926d8be8d11477a9c1ce0a164d5c869a] Lawhern et al., EEGNet: a compact convolutional neural network for EEG-based</div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>crossref.926d8be8d11477a9c1ce0a164d5c869a] Lawhern et al., EEGNet: a compact convolutional neural network for EEG-based</div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>brain–computer interfaces</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>brain–computer interfaces</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://ieeexplore.ieee.org/document/8494247/] Pullini et al., Mr. Wolf: A 1 GFLOP/s Energy-Proportional Parallel Ultra Low Power SoC for IOT Edge Processing</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>* [https://ieeexplore.ieee.org/document/8494247/] Pullini et al., Mr. Wolf: A 1 GFLOP/s Energy-Proportional Parallel Ultra Low Power SoC for IOT Edge Processing</div></td></tr>
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Herschmi
http://iis-projects.ee.ethz.ch/index.php?title=An_Energy_Efficient_Brain-Computer_Interface_using_Mr.Wolf&diff=4976&oldid=prev
Herschmi: Created page with "Category:Digital Category:Human Intranet Category:In progress Category:Semester Thesis Category:2019 Category:HerschmiCategory:LukascCategory:xia..."
2019-10-29T14:15:30Z
<p>Created page with "<a href="/index.php?title=Category:Digital" class="mw-redirect" title="Category:Digital">Category:Digital</a> <a href="/index.php?title=Category:Human_Intranet" class="mw-redirect" title="Category:Human Intranet">Category:Human Intranet</a> <a href="/index.php?title=Category:In_progress" title="Category:In progress">Category:In progress</a> <a href="/index.php?title=Category:Semester_Thesis" title="Category:Semester Thesis">Category:Semester Thesis</a> <a href="/index.php?title=Category:2019&action=edit&redlink=1" class="new" title="Category:2019 (page does not exist)">Category:2019</a> <a href="/index.php?title=Category:Herschmi&action=edit&redlink=1" class="new" title="Category:Herschmi (page does not exist)">Category:Herschmi</a><a href="/index.php?title=Category:Lukasc&action=edit&redlink=1" class="new" title="Category:Lukasc (page does not exist)">Category:Lukasc</a>Category:xia..."</p>
<p><b>New page</b></p><div>[[Category:Digital]] [[Category:Human Intranet]] [[Category:In progress]] [[Category:Semester Thesis]] [[Category:2019]]<br />
[[Category:Herschmi]][[Category:Lukasc]][[Category:xiaywang]]<br />
==Description==<br />
A brain–computer interface is a device that enables communication and control without movement. The device aims to recognize the human’s intentions from spatiotemporal neural activity typically recorded by a large set of electroencephalogram (EEG) electrodes. What makes it particularly challenging, however, is its susceptibility to errors in the recognition of human intentions. To cope with high variability of EEG data, today’s BCI require highly sophisticated classification algorithms. Traditional MI-BCI architectures are divided into feature extraction and a subsequent classifier. EEG signals are typically pre-processed using tunable spectral and spatial filters followed by log-energy feature extraction, with Riemannian covariance matrices [1] achieving the highest classification accuracy of 75.47% on the BCI competition IV-2a dataset. Alternatively, the feature extractor and classifier can<br />
be combined and trained simultaneously with a convolutional neural network (CNN). While being successful in image classification, CNNs are gaining attention in MI-BCIs as well [2]. One of the most popular and compact CNNs is EEGNet [3] achieving an<br />
accuracy of 72%.<br />
<br />
This thesis analyses the feasibility of operating both EEGNet and Riemannian approaches on Mr.Wolf [4], an embedded platform featuring an 8-core fixed-point cluster for efficient computing. After a prior quanitzation analysis of weights and features from floating-point<br />
to fixed-point representation, the classifiers are ported to Mr.Wolf.<br />
<br />
===Status: In progress ===<br />
Tibor Schneider<br />
: Supervision: [[:User:Herschmi | Michael Hersche]],[[:User:xiaywang|Xiaying Wang]],[[:User:lukasc|Lukas Cavigelli]]<br />
<br />
===Prerequisites===<br />
* Deep Learning<br />
* Python (preferably Pytorch)<br />
* Embedded C<br />
<br />
<br />
===Character===<br />
: 20% Theory<br />
: 80% Software Development<br />
<br />
===Professor===<br />
: [http://www.iis.ee.ethz.ch/portrait/staff/lbenini.en.html Luca Benini]<br />
[[#top|↑ top]]<br />
<br />
===Literature===<br />
* [https://ieeexplore.ieee.org/document/8553378] Hersche et al., Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features<br />
* [https://www.ncbi.nlm.nih.gov/pubmed/28782865] Schirrmeister et al., Deep learning with convolutional neural networks for EEG decoding and visualization<br />
* [http://stacks.iop.org/1741-2552/15/i=5/a=056013?key=<br />
crossref.926d8be8d11477a9c1ce0a164d5c869a] Lawhern et al., EEGNet: a compact convolutional neural network for EEG-based<br />
brain–computer interfaces<br />
* [https://ieeexplore.ieee.org/document/8494247/] Pullini et al., Mr. Wolf: A 1 GFLOP/s Energy-Proportional Parallel Ultra Low Power SoC for IOT Edge Processing</div>
Herschmi