Applications of the signal space separation method

By Samu Taulu, Juha Simola, Matti J. Kajola. IEEE Transactions on Signal Processing, 2005

552 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Radiology, Nuclear Medicine and Imaging, Signal Processing

View Paper: https://www.semanticscholar.org/paper/9af2c8b1a817c7aab8d82fd211c605dc596bd6bb

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

1. Juha Simola
2. Samu Taulu
3. Richard N. Henson
4. Morten L. Kringelbach

In Radiology, Nuclear Medicine and Imaging

1. Morten L. Kringelbach
2. Samu Taulu
3. Joana Cabral
4. Juha Simola
5. Cam-CAN Group
6. Gustavo Deco
7. Mark William Woolrich
8. Jason R. Taylor

In Signal Processing

1. Juha Simola
2. Samu Taulu
3. Richard N. Henson
4. Morten L. Kringelbach
5. Mark William Woolrich
6. Riitta Hari

Citations in the corpus

• Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements (2006, methodology): “As shown in Taulu et al (2005), such a purely spatial algorithm results in an interference rejection factor that grows continuously and monotonously with increasing distance of the interference source.”
• Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations (2014, methodology): “Signal space separation (implemented using MaxFilterTM) was applied to each data-set to reduce interference fromoutside the scanner helmet (Taulu et al., 2005).”
• Removal of magnetoencephalographic artifacts with temporal signal‐space separation: Demonstration with single‐trial auditory‐evoked responses (2009, unknown): “For a typical MEG measurement, SSS can suppress external interference by a factor exceeding 100, even when the interference sources are located inside themagnetically shielded room [Taulu et al., 2005].”
• MNE software for processing MEG and EEG data (2013, unknown): “In addition, some systems offer the possibility for recording the head position continuously during the actual acquisition and compensation methods to bring all the data to a common coordinate frame (see, e.g., Taulu et al., 2005; Uutela et al., 2001).”
• The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: Structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample (2017, methodology): “For each run, temporal signal space separation (tSSS, Taulu et al., 2005; MaxFilter 2.2, Elekta Neuromag Oy, Helsinki, Finland) was applied to continuous MEG data to remove noise from external sources and from HPI coils (correlation threshold 0.98, 10-sec sliding window), for continuous head-motion…”
• Cognitive interference elicits developmental sex differences in inhibitory control (2025, unknown): “Raw MEG data were corrected for head motion and extraneous noise using the signal space separation method with a temporal extension (Taulu et al., 2005; Taulu and Simola, 2006).”
• Inflammation-related aberrations in beta and gamma oscillatory dynamics serving attention processing in typically developing youth (2025, unknown): “42,43”