Short Bio

I am a research scientist at the University of Washington Institute for Learning and Brain Sciences, working primarily with magnetoencephalography (MEG) and electroencephalography (EEG) data.



  • SciPy - A core scientific Python library, including signal processing routines.
  • VisPy - A high performance OpenGL scientific visualization library.
  • MNE - A complete package to process EEG and MEG data: forward and inverse problems, preprocessing, statistics, machine learning.

Multiple other contributions to other open source scientific packages. More on my Github Page

Recent Publications

 author = {Taulu, S. and Larson, E.},
 journal = {IEEE Transactions on Biomedical Engineering},
 title = {Unified expression of the quasi-static electromagnetic field: Demonstration with MEG and EEG signals},
 year = {in press}

 author = {Clarke, M., Larson, E., Tavabi, K. and Taulu, S.},
 doi = {10.1016/j.jneumeth.2020.108700},
 issn = {0165-0270},
 journal = {Journal of Neuroscience Methods},
 month = {May},
 pages = {108700},
 title = {Effectively combining temporal projection noise suppression methods in magnetoencephalography},
 year = {2020}

 author = {Virtanen, P., Gommers, R., Oliphant, T., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S., Brett, M., Wilson, J., Jarrod Millman, K., Mayorov, N., Nelson, A., Jones, E., Kern, R., Larson, E., Carey, C., Polat, I., Feng, Y., Moore, E., Vand erPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E., Harris, C., Archibald, A., Ribeiro, A., Pedregosa, F., van Mulbregt, P. and Contributors, S.},
 doi = {},
 journal = {Nature Methods},
 month = {February},
 title = {{SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python}},
 year = {2020}

 author = {Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M.},
 comment = {[Code]},
 doi = {10.21105/joss.01896},
 issn = {2475-9066},
 journal = {Journal of Open Source Software},
 language = {en},
 month = {December},
 number = {44},
 pages = {1896},
 shorttitle = {{MNE}-{BIDS}},
 title = {{MNE}-{BIDS}: {Organizing} electrophysiological data into
the {BIDS} format and facilitating their analysis},
 url = {},
 urldate = {2019-12-19},
 volume = {4},
 year = {2019}

 abstract = {Active listening involves dynamically switching attention between competing talkers and is essential to following conversations in everyday environments. Previous investigations in human listeners have examined the neural mechanisms that support switching auditory attention within the acoustic featural cues of pitch and auditory space. Here, we explored the cortical circuitry underlying endogenous switching of auditory attention between pitch and spatial cues necessary to discern target from masker words. Because these tasks are of unequal difficulty, we expected an asymmetry in behavioral switch costs for hard-to-easy versus easy-to-hard switches, mirroring prior evidence from vision-based cognitive task-switching paradigms. We investigated the neural correlates of this behavioral switch asymmetry and associated cognitive control operations in the present auditory paradigm. Behaviorally, we observed no switch-cost asymmetry, i.e., no performance difference for switching from the more difficult attend-pitch to the easier attend-space condition (P→S) versus switching from easy-to-hard (S→P). However, left lateral prefrontal cortex activity, correlated with improved performance, was observed during a silent gap period when listeners switched attention from P→S, relative to switching within pitch cues. No such differential activity was seen for the analogous easy-to-hard switch. We hypothesize that this neural switch asymmetry reflects proactive cognitive control mechanisms that successfully reconfigured neurally-specified task parameters and resolved competition from other such “task sets,” thereby obviating the expected behavioral switch-cost asymmetry. The neural switch activity observed was generally consistent with that seen in cognitive paradigms, suggesting that established cognitive models of attention switching may be productively applied to better understand similar processes in audition.},
 author = {McLaughlin, S., Larson, E. and Lee, A.},
 doi = {10.1007/s10162-018-00713-z},
 issn = {1438-7573},
 journal = {Journal of the Association for Research in Otolaryngology},
 keywords = {active listening, auditory attention, dorsolateral prefrontal cortex (DLPFC), EEG, MEG, neural switch asymmetry},
 language = {en},
 month = {April},
 number = {2},
 pages = {205--215},
 title = {Neural {Switch} {Asymmetry} in {Feature}-{Based} {Auditory} {Attention} {Tasks}},
 url = {},
 volume = {20},
 year = {2019}

 abstract = {Objective: Here, we review the theory of suppression of spatially uncorrelated, sensor-specific noise in electro- and magentoencephalography (EEG and MEG) arrays, and introduce a novel method for suppression. Our method requires only that the signals of interest are spatially oversampled, which is a reasonable assumption for many EEG and MEG systems. Methods: Our method is based on a leave-one-out procedure using overlapping temporal windows in a mathematical framework to project spatially uncorrelated noise in the temporal domain. Results: This method, termed “oversampled temporal projection” (OTP), has four advantages over existing methods. First, sparse channel-specific artifacts are suppressed while limiting mixing with other channels, whereas existing linear, time-invariant spatial operators can spread such artifacts to other channels with a spatial distribution which can be mistaken for one produced by an electrophysiological source. Second, OTP minimizes distortion of the spatial configuration of the data. During source localization (e.g., dipole fitting), many spatial methods require corresponding modification of the forward model to avoid bias, while OTP does not. Third, noise suppression factors at the sensor level are maintained during source localization, whereas bias compensation removes the denoising benefit for spatial methods that require such compensation. Fourth, OTP uses a time-window duration parameter to control the tradeoff between noise suppression and adaptation to time-varying sensor characteristics. Conclusion: OTP efficiently optimizes noise suppression performance while controlling for spatial bias of the signal of interest. Significance: This is important in applications where sensor noise significantly limits the signal-to-noise ratio, such as high-frequency brain oscillations.},
 author = {Larson, E. and Taulu, S.},
 doi = {10.1109/TBME.2017.2734641},
 issn = {0018-9294, 1558-2531},
 journal = {IEEE Transactions on Biomedical Engineering},
 keywords = {Artifact suppression, Bayes methods, Brain, Brain modeling, channel-specific artifacts, EEG recordings, Electroencephalography, interference, magentoencephalography, magnetoencephalography, medical signal processing, MEG, Multichannel measurement, Noise, Noise measurement, noise reduction, noise suppression factors, noise suppression performance, OTP minimizes distortion, outlier, oversampled temporal projection, sensor level, sensor noise reduction, sensor noise suppression, signal denoising, signal processing, signal reconstruction, signal-to-noise ratio, Source localization, spatial bias, spatial configuration, spatial distribution, spatially uncorrelated noise, spatially uncorrelated sensor-specific noise, spatial methods, temporal domain, temporal windows, time-invariant spatial operators, time-varying sensor characteristics, time-window duration parameter},
 month = {May},
 number = {5},
 pages = {1002--1013},
 title = {Reducing {Sensor} {Noise} in {MEG} and {EEG} {Recordings} {Using} {Oversampled} {Temporal} {Projection}},
 volume = {65},
 year = {2018}

 abstract = {The primary goal of the Helsinki VideoMEG Project is to enable magnetoencephalography (MEG) practitioners to record and analyze the video of the subject during an MEG experiment jointly with the MEG data. The project provides: •Hardware assembly instructions and software for setting up video and audio recordings of the participant synchronized to MEG data acquisition.•Basic software tools for analyzing video and audio together with the MEG data. The resulting setup allows reliable recording of video and audio from the subject in various real-world usage scenarios. The Helsinki VideoMEG Project allowed successful establishment of video-MEG facilities in four different MEG laboratories in Finland, Sweden and the United States.},
 author = {Zhdanov, A., Nurminen, J. and Larson, E.},
 doi = {10.1016/j.mex.2018.01.002},
 file = {ScienceDirect Full Text PDF:/Users/larsoner/Library/Application Support/Zotero/Profiles/dlymswnx.default/zotero/storage/WTH9243C/Zhdanov et al. - 2018 - Helsinki VideoMEG Project Augmenting magnetoencep.pdf:application/pdf},
 issn = {2215-0161},
 journal = {MethodsX},
 keywords = {Biomagnetics, Epilepsy, magnetoencephalography, Video-magnetoencephalography (Video-MEG), Video recording},
 language = {en},
 month = {January},
 pages = {234--243},
 shorttitle = {Helsinki {VideoMEG} {Project}},
 title = {Helsinki {VideoMEG} {Project}: {Augmenting} magnetoencephalography with synchronized video recordings},
 url = {},
 volume = {5},
 year = {2018}

Full list of publications