<div>Greetings James,</div>
<div> </div>
<div>Thanks for your always useful responses on the list!</div>
<div> </div>
<div>There are multiple issues with artifact detection as you know. Groups with large samples and not enough staffing will thank you for </div>
<div>such an automated process. Manousos and Mahesh, if this brings up any thoughts or issues or sample code,</div>
<div>please let us know.</div>
<div> </div>
<div>A few quick thoughts (kind of a wishlist really):</div>
<div> </div>
<div>0. I'd be willing to be a beta tester, and provide data, and "artifact detection"-by human data samples,</div>
<div>for the process. At minimum, the easiest first solution seemed to be (to me), to </div>
<div>epoch-based artifact detection tools of your choice in EEGLAB, and apply them in several sweeps,</div>
<div>to "artificial epochs" in your continous data.By artificial, i mean just making contiguous 200 or 500 or 1000 ms epochs,</div>
<div>regrardless of events. This I sometimes do to clean up ICA decompostions before applying them back to continuous data.</div>
<div> </div>
<div> </div>
<div>1. Arno does have a method, and may eventually share it with us, although</div>
<div>it is not a total solution. I believe it is built principally on spectral</div>
<div>methods for artifact detection used in EEGLAB.</div>
<div> </div>
<div>2. If you haven't, see ADJUST's metrics and logic, something similar to which</div>
<div>would be good to include. For example metrics such as SCADS.</div>
<div>As automated artifact detection methods are crucial, new toolboxes,</div>
<div>and comparisons between methods within these toolboxes, will </div>
<div>become more evident in the literature soon. See also</div>
<div>Dien's toolbox cleaning methods and output to think about</div>
<div>how to clean continuous data. There's also a growing ieee/signal analysis</div>
<div>literature on quasi-automatic ICA based artifact rejection.</div>
<div> </div>
<div>3. Some groups have been working on automated methods</div>
<div>that mimic cleaning by real users.</div>
<div> </div>
<div>4. A method that passes through the data at various </div>
<div>"fake" epoch lengths would be of interest.</div>
<div> </div>
<div>5. a method that catches large, medium, small artifacts would be nice.</div>
<div> </div>
<div>6. a method that allows catching/marking of </div>
<div>eye blinks, eye movements, muscle tension, sweating, neck tension, brow tension, jaw tension, etc.. would be nice.</div>
<div>A method that automatically builds up and uses subject-specific scalp maps or ICs, </div>
<div>or relies on a library of such relatively spatially specific artifact-related scalp maps, would be ideal. </div>
<div>Regarding muscle artifact detection, a recent paper from Davidson's group by Schackmann comes to mind.</div>
<div> </div>
<div>7. catching bad time samples of various lengths, at some or few or all/most channels, would be nice.</div>
<div> </div>
<div>8. Calibrating for global differences in signal across participant data sets and/or recording systems would be good.</div>
<div> </div>
<div>9. Being able to automatically identify different kinds of bad channels automatically would be great.</div>
<div>e.g., noisy (i.e., 60hz) channels, flat channels, abnormal value channels, channels with high variance.</div>
<div>Assumedly this would occur across the sweeps at various epoch sizes mentioned above.</div>
<div>It's possible that channel by channel based interpolation would be good too, automatically, from healthy channels and time periods.</div>
<div>Assuming an automated artifact detection method </div>
<div> </div>
<div><font size="1" face="arial narrow,sans-serif"></font> </div>
<div><font size="1" face="arial narrow,sans-serif"></font> </div>
<div><font size="1" face="arial narrow,sans-serif"></font> </div>
<div><font size="1" face="arial narrow,sans-serif">Tarik Bel-Bahar<br></font></div>
<div><font size="1" face="arial narrow,sans-serif">Swartz Center for Computational Neuroscience</font></div>
<div><font size="1" face="arial narrow,sans-serif">Institute for Neural Computation, University of California, San Diego</font></div>
<div><font size="1" face="arial narrow,sans-serif">San Diego SuperComputer Center, B-1, RM B191E </font></div>
<div><font size="1" face="arial narrow,sans-serif">9500 Gilman Drive, MC: 0559 / La Jolla, CA 92093-0559</font></div>
<div><br></div><br><br><br>
<div class="gmail_quote">On Thu, Jun 9, 2011 at 7:53 AM, James Desjardins <span dir="ltr"><<a href="mailto:jdesjardins@brocku.ca">jdesjardins@brocku.ca</a>></span> wrote:<br>
<blockquote style="BORDER-LEFT: #ccc 1px solid; MARGIN: 0px 0px 0px 0.8ex; PADDING-LEFT: 1ex" class="gmail_quote">Dear list members,<br><br>For my purposes it is beneficial to reject artifacts in the continuous<br>data rather than following segmentation. I find the artifact detection<br>
tools in EEGLAB extremely helpful when working with segmented data.<br><br>I am considering working on a plugin that windows continuous data,<br>takes advantage of some of the currently available artifact detection<br>tools for segmented data, then translates the windowed rejection<br>
information back to time intervals in the continuous data.<br><br>Are there already methods for doing this... or is this something that<br>someone else is already working on?<br><br><br>James Desjardins<br>Technician, MA Student<br>
Department of Psychology, Behavioural Neuroscience<br>Cognitive and Affective Neuroscience Lab<br>Brock University<br>500 Glenridge Ave.<br>St. Catharines, ON, Canada<br>L2S 3A1<br><a href="tel:905-688-5550%20x4676" value="+19056885550">905-688-5550 x4676</a><br>
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