Presentation of electromagnetic multichannel data: The signal space separation method

By Samu Taulu, Matti J. Kajola. 2005

391 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Nuclear and High Energy Physics, Signal Processing

View Paper: https://www.semanticscholar.org/paper/8c96d58399503285ed5cf2c7f4bd258a67f747b5

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. Matti J. Kajola
5. E. Bullmore

In Nuclear and High Energy Physics

1. Juha Simola
2. Samu Taulu
3. Matti J. Kajola
4. Manfred G. Kitzbichler

In Signal Processing

1. Juha Simola
2. Samu Taulu
3. Marie L. Smith
4. Manfred G. Kitzbichler
5. E. Bullmore
6. Richard N. Henson
7. Matti J. Kajola
8. Riitta Hari

Citations in the corpus

• Removal of magnetoencephalographic artifacts with temporal signal‐space separation: Demonstration with single‐trial auditory‐evoked responses (2009, neutral): “Taulu and coworkers [Taulu and Kajola, 2005; Taulu et al., 2005] recently introduced a new multichannel signalprocessing method, signal-space separation (SSS), based on physical properties of the magnetic fields and on the possibility with the present-day whole-scalp coverage neuromagnetometers to…”
• Applications of the signal space separation method (2005, methodology): “In reality, however, the effect of SSS on the noise of MEG data is not as significant as this simulation indicates because here, we only take into account the sensor noise, whereas real noise also contains contributions from the brain and the radiation shields.”
• Cognitive Effort Drives Workspace Configuration of Human Brain Functional Networks (2011, methodology): “These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.”
• Spatiotemporal signal space separation method for rejecting nearby interference in MEG measurements (2006, methodology): “Signal space separation is a purely spatial method to transform electromagnetic multichannel data into uncorrelated basic components, e.g. magnetic multipole moments (Taulu and Kajola 2005, Taulu et al 2005).”
• Distinct neural oscillations predict individual differences in feedback-guided cognitive flexibility (2026, unknown): “Maxfilter software (Elekta Neuromag) was used for temporal signal space separation to suppress extra-cranial noise in MEG data, to adjust for head position changes during the measurements, and to interpolate bad channels ( Taulu and Kajola 2005).”
• Distinct neural oscillations predict covert switching between stable and unstable cognitive representations (2025, unknown): “Maxfilter software (Elekta Neuromag) was used for temporal signal space separation (tSSS) to suppress extra-cranial noise in MEG data, to adjust for head position changes during the measurements, and to interpolate bad channels (Taulu & Kajola, 2005).”
• Aperiodic and oscillatory systems underpinning human domain-general cognition (2024, unknown): “First, we applied the Signal-Space Separation (SSS) to the raw MEG data to reduce environmental artefacts 94 .”