Validation of SOBI components from high-density EEG

Tang, Akaysha C.; Sutherland, Matthew T.; McKinney, Christopher J.

doi:10.1016/j.neuroimage.2004.11.027

Cited by 160 publications

(114 citation statements)

References 69 publications

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“…We showed that decomposing the CEPs using MMP prior to inverse modelling (namely MUSIC) is superior to some blind source separation (BSS) methods, namely Independent Component Analysis (ICA) and Second-Order Blind Identification (SOBI), which are typically used for CEP signal decomposition prior to source analysis. These decomposition methods are described in detail elsewhere [58][59][60][61][62][63][64][65][66] . Additionally, we showed that MMP prior to MUSIC was much more accurate than MUSIC on the instantaneous data on both simulated and empirical CEPs [67] .…”

Section: Inverse Modelling Of Cortical Evoked Potentialsmentioning

confidence: 99%

New technologies to investigate the brain-gut axis

Sharma¹,

Lelic²,

Brock³

et al. 2009

WJG

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Section: Inverse Modelling Of Cortical Evoked Potentialsmentioning

confidence: 99%

New technologies to investigate the brain-gut axis

Sharma¹,

Lelic²,

Brock³

et al. 2009

WJG

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“…Independent components analysis (ICA) (Comon, 1994) and second order blind identification (SOBI) (Belouchrani et al, 1997;Tang et al, 2005) as implemented in Fieldtrip (Oostenveld et al, 2011).…”

Section: Component-based Artifact Reductionmentioning

confidence: 99%

Signal-to-noise ratio of the MEG signal after preprocessing

Gonzalez-Moreno

Aurtenetxe

Lopez-Garcia

et al. 2014

Journal of Neuroscience Methods

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Signal-to-noise ratio of the MEG signal after preprocessing HIGHLIGHTSThe signal-to-noise ratio of event-related fields is used to evaluate the effectiveness of various preprocessing algorithms for magnetoencephalography data. Signal Space Separation algorithms provide approximately a 100% increase in signal to noise ratio. Epoch-based artifact rejection and decomposition methods such as independent component analysis yielded a signal to noise ratio increase of 5-10% and 35% respectively. The use of decomposition methods seems advisable. The evaluation of the signal-to-noise ratio increase can help to guide the choice of preprocessing methods. ABSTRACTBackground: Magnetoencephalography (MEG) provides a direct measure of brain activity with high combined spatiotemporal resolution. Preprocessing is necessary to reduce contributions from environmental interference and biological noise. New method: The effect on the signal-to-noise ratio of different preprocessing techniques is evaluated. The signal-to-noise ratio (SNR) was defined as the ratio between the mean signal amplitude (evoked field) and the standard error of the mean over trials. Results: Recordings from 26 subjects obtained during and event-related visual paradigm with an Elekta MEG scanner were employed. Two methods were considered as first-step noise reduction: Signal Space Separation and temporal Signal Space Separation, which decompose the signal into components with origin inside and outside the head. Both algorithm increased the SNR by approximately 100%. Epoch-based methods, aimed at identifying and rejecting epochs containing eye blinks, muscular artifacts and sensor jumps provided an SNR improvement of 5-10%. Decomposition methods evaluated were independent component analysis (ICA) and second-order blind identification (SOBI). The increase in SNR was of about 36% with ICA and 33% with SOBI. Comparison with existing methods:No previous systematic evaluation of the effect of the typical preprocessing steps in the SNR of the MEG signal has been performed. Conclusions: The application of either SSS or tSSS is mandatory in Elekta systems. No significant differences were found between the two. While epoch-based methods have been routinely applied the less often considered decomposition methods were clearly superior and therefore their use seems advisable.

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“…SOBI is an extension of the ICA method based upon the concept of joint diagonalization of time lagged covariance matrices and is chosen because it is known to be effective at separating neuronal sources and artifacts in the EEG [16]. Each independent component (IC) derived by SOBI is then band-pass filtered (fourth-order, Butterworth) between 1-10 Hz to create filtered ICs .…”

Section: E Artifact Reductionmentioning

confidence: 99%

On the Automated Removal of Artifacts Related to Head Movement From the EEG

Daly

Billinger

Scherer

et al. 2013

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-Contamination of the electroencephalogram (EEG)by artifacts related to head movement is a major cause of reduced signal quality. This is a problem in both neuroscience and other uses of the EEG. To attempt to reduce the influence, on the EEG, of artifacts related to head movement, an accelerometer is placed on the head and independent component analysis is applied to attempt to separate artifacts which are statistically related to head movements. To evaluate the method, EEG and accelerometer measurements are made from 14 individuals with Cerebral palsy attempting to control a sensorimotor rhythm based brain-computer interface. Results show that the approach significantly reduces the influence of head movement related artifacts in the EEG.Index Terms-Automated artifact removal, brain-computer interface (BCI), electroencephalogram, head movement, independent component analysis (ICA).

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Validation of SOBI components from high-density EEG

Cited by 160 publications

References 69 publications

New technologies to investigate the brain-gut axis

New technologies to investigate the brain-gut axis

Signal-to-noise ratio of the MEG signal after preprocessing

On the Automated Removal of Artifacts Related to Head Movement From the EEG

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