Samu Taulu

Prof. Samu Taulu

1. Institute for Learning & Brain Sciences, University of Washington, Seattle, WA, USA;
+1
orcid

Research Focus & Metrics

Academic Career Length

26Years (2001-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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16 March 2006 in Physics in Medicine & Biology
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Journal Article
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STSamu Taulu
Samu Taulu
JSJ Simola
J Simola
Limitations of traditional magnetoencephalography (MEG) exclude some important patient groups from MEG examinations, such as epilepsy patients with a vagus nerve stimulator, patients with magnetic particles on the head or having magnetic dental materials that cause severe movement-related artefact signals. Conventional interference rejection methods are not able to remove the artefacts originating this close to the MEG sensor array. For example, the reference array method is unable to suppress interference generated by sources closer to the sensors than the reference array, about 20-40 cm. The spatiotemporal signal space separation method proposed in this paper recognizes and removes both external interference and the artefacts produced by these nearby sources, even on the scalp. First, the basic separation into brain-related and external interference signals is accomplished with signal space separation based on sensor geometry and Maxwell's equations only. After this, the artefacts from nearby sources are extracted by a simple statistical analysis in the time domain, and projected out. Practical examples with artificial current dipoles and interference sources as well as data from real patients demonstrate that the method removes the artefacts without altering the field patterns of the brain signals.

1 June 2004 in Brain Topography
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Journal Article
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STSamu Taulu
Samu Taulu
MKMatti Kajola
Matti Kajola
JSJuha Simola
Juha Simola

15 June 2005 in Journal of Applied Physics
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Journal Article
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STSamu Taulu
Samu Taulu
MKMatti Kajola
Matti Kajola
Measurement of external magnetic fields provides information on electric current distribution inside an object. For example, in magnetoencephalography modern measurement devices sample the magnetic field produced by the brain in several hundred distinct locations around the head. The signal space separation (SSS) method creates a fundamental linear basis for all measurable multichannel signal vectors of magnetic origin. The SSS basis is based on the fact that the magnetic field can be expressed as a combination of two separate and rapidly converging expansions of harmonic functions with one expansion for signals arising from inside of the measurement volume of the sensor array and another for signals arising from outside of this volume. The separation is based on the different convergence volumes of the two expansions and on the fact that the sensors are located in a source current-free volume between the interesting and interfering sources. Individual terms of the expansions are shown to contain uncorrelated information of the underlying source distribution. SSS provides a stable decomposition of the measurement into a fundamental device-independent form when used with an accurately calibrated multichannel device. The external interference signals are elegantly suppressed by leaving the interference components out from the reconstruction based on the decomposition. Representation of multichannel data with the SSS basis is shown to provide a large variety of applications for improved analysis of multichannel data.


1 August 2018 in Clinical Neurophysiology
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Journal Article
Open access
RHRiitta Hari
Riitta Hari
SBSylvain Baillet
Sylvain Baillet
GBGareth Barnes
Gareth Barnes
RBRichard Burgess
Richard Burgess
NFNina Forss
Nina Forss
STSamu Taulu
Samu Taulu
Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG.

17 July 2006 in NeuroReport
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Journal Article
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TIToshiaki Imada
Toshiaki Imada
Yang ZhangYang Zhang
Yang Zhang
MCMarie Cheour
Marie Cheour
STSamu Taulu
Samu Taulu
AAAntti Ahonen
Antti Ahonen
PKPatricia K. Kuhl
Patricia K. Kuhl
Discriminative responses to tones, harmonics, and syllables in the left hemisphere were measured with magnetoencephalography in neonates, 6-month-old infants, and 12-month-old infants using the oddball paradigm. Real-time head position tracking, signal space separation, and head position standardization were applied to secure quality data for source localization. Minimum current estimates were calculated to characterize infants' cortical activities for detecting sound changes. The activation patterns observed in the superior temporal and inferior frontal regions provide initial evidence for the developmental emergence early in life of a perceptual-motor link for speech perception that may depend on experience.