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Fast and Accurate Multiclass Inference for Brain–Computer Interfaces - Revision history
2024-03-28T20:00:19Z
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Abbas at 15:53, 7 August 2018
2018-08-07T15:53:06Z
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Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4017&oldid=prev
Abbas: /* Links */
2018-08-07T15:51:27Z
<p><span dir="auto"><span class="autocomment">Links</span></span></p>
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:51, 7 August 2018</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>* [https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]</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://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]</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>* [https://github.com/MultiScale-BCI/IV-2a Artifact(code)]</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>* [https://github.com/MultiScale-BCI/IV-2a Artifact (code)]</div></td></tr>
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Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4016&oldid=prev
Abbas at 15:51, 7 August 2018
2018-08-07T15:51:18Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:51, 7 August 2018</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>==Links==</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>==Links==</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><del class="diffchange diffchange-inline">: </del>[https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]</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><ins class="diffchange diffchange-inline">* </ins>[https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]</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><del class="diffchange diffchange-inline">: </del>[https://github.com/MultiScale-BCI/IV-2a code]</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><ins class="diffchange diffchange-inline">* </ins>[https://github.com/MultiScale-BCI/IV-2a <ins class="diffchange diffchange-inline">Artifact(</ins>code<ins class="diffchange diffchange-inline">)</ins>]</div></td></tr>
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Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4015&oldid=prev
Abbas at 15:50, 7 August 2018
2018-08-07T15:50:50Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:50, 7 August 2018</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>: [https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]</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://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]</div></td></tr>
<tr><td colspan="2"> </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><ins style="font-weight: bold; text-decoration: none;">: [https://github.com/MultiScale-BCI/IV-2a code]</ins></div></td></tr>
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Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4014&oldid=prev
Abbas at 15:49, 7 August 2018
2018-08-07T15:49:41Z
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<td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 15:49, 7 August 2018</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>EEG, motor imagery, brain–computer interfaces, multiclass classification, multiscale features, SVM</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>EEG, motor imagery, brain–computer interfaces, multiclass classification, multiscale features, SVM</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>
<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><del class="diffchange diffchange-inline">=</del>==Status: Completed <del class="diffchange diffchange-inline">=</del>==</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>==Status: Completed==</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>: Semester thesis by Michael Hersche and Tino Rellstab</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>: Semester thesis by Michael Hersche and Tino Rellstab</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>: Supervision: Abbas Rahimi, Lukas Cavigelli, Pasquale Davide Schiavone</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>: Supervision: Abbas Rahimi, Lukas Cavigelli, Pasquale Davide Schiavone</div></td></tr>
<tr><td colspan="2"> </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><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </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><ins style="font-weight: bold; text-decoration: none;">==Links==</ins></div></td></tr>
<tr><td colspan="2"> </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><ins style="font-weight: bold; text-decoration: none;">: [https://iis-people.ee.ethz.ch/~arahimi/papers/EUSIPCO18.pdf M. Hersche, T. Rellstab, P. D. Schiavone, L. Cavigelli, L. Benini, A. Rahimi, “Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features,” In IEEE European Signal Processing Conference (EUSIPCO), 2018.]</ins></div></td></tr>
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Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4013&oldid=prev
Abbas at 15:47, 7 August 2018
2018-08-07T15:47:49Z
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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;"></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>
<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><del style="font-weight: bold; text-decoration: none;"></del></div></td><td colspan="2"> </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><del style="font-weight: bold; text-decoration: none;">=Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features=</del></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;"></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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Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4012&oldid=prev
Abbas at 15:47, 7 August 2018
2018-08-07T15:47:05Z
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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;"></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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Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4011&oldid=prev
Abbas: /* Title: Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features */
2018-08-07T15:46:54Z
<p><span dir="auto"><span class="autocomment">Title: Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features</span></span></p>
<table class="diff diff-contentalign-left" data-mw="interface">
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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 15:46, 7 August 2018</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l8" >Line 8:</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;"></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>
<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>=<del class="diffchange diffchange-inline">Title: </del>Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features=</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> </div></td></tr>
<tr><td colspan="2"> </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>=Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features =<ins class="diffchange diffchange-inline">=</ins></div></td></tr>
<tr><td colspan="2"> </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> </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>==Abstract==</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>==Abstract==</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>Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70±15.90% (mean± standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6±14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27±15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47±12.8% accuracy with 1.6x faster testing than CSP.</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>Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70±15.90% (mean± standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6±14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27±15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47±12.8% accuracy with 1.6x faster testing than CSP.</div></td></tr>
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Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4010&oldid=prev
Abbas at 15:44, 7 August 2018
2018-08-07T15:44:21Z
<p></p>
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<tr><td colspan="2"> </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><ins style="font-weight: bold; text-decoration: none;">[[Category:Semester Thesis]]</ins></div></td></tr>
<tr><td colspan="2"> </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><ins style="font-weight: bold; text-decoration: none;">[[Category:Master Thesis]]</ins></div></td></tr>
<tr><td colspan="2"> </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><ins style="font-weight: bold; text-decoration: none;">__NOTOC__</ins></div></td></tr>
<tr><td colspan="2"> </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><ins style="font-weight: bold; text-decoration: none;"></ins></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>=Title: 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>=Title: 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>==Abstract==</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>==Abstract==</div></td></tr>
</table>
Abbas
http://iis-projects.ee.ethz.ch/index.php?title=Fast_and_Accurate_Multiclass_Inference_for_Brain%E2%80%93Computer_Interfaces&diff=4008&oldid=prev
Abbas: Created page with "=Title: Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features= ==Abstract== Accurate, fast, and reliable multiclass classifi..."
2018-08-07T15:43:09Z
<p>Created page with "=Title: Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features= ==Abstract== Accurate, fast, and reliable multiclass classifi..."</p>
<p><b>New page</b></p><div>=Title: Fast and Accurate Multiclass Inference for MI-BCIs Using Large Multiscale Temporal and Spectral Features=<br />
==Abstract==<br />
Accurate, fast, and reliable multiclass classification of electroencephalography (EEG) signals is a challenging task towards the development of motor imagery brain-computer interface (MI-BCI) systems. We propose enhancements to different feature extractors, along with a support vector machine (SVM) classifier, to simultaneously improve classification accuracy and execution time during training and testing. We focus on the well-known common spatial pattern (CSP) and Riemannian covariance methods, and significantly extend these two feature extractors to multiscale temporal and spectral cases. The multiscale CSP features achieve 73.70±15.90% (mean± standard deviation across 9 subjects) classification accuracy that surpasses the state-of-the-art method [1], 70.6±14.70%, on the 4-class BCI competition IV-2a dataset. The Riemannian covariance features outperform the CSP by achieving 74.27±15.5% accuracy and executing 9x faster in training and 4x faster in testing. Using more temporal windows for Riemannian features results in 75.47±12.8% accuracy with 1.6x faster testing than CSP.<br />
<br />
==Keywords==<br />
EEG, motor imagery, brain–computer interfaces, multiclass classification, multiscale features, SVM<br />
<br />
===Status: Completed ===<br />
: Semester thesis by Michael Hersche and Tino Rellstab<br />
: Supervision: Abbas Rahimi, Lukas Cavigelli, Pasquale Davide Schiavone</div>
Abbas