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

Abstract

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.

Dissociable auditory mismatch response and connectivity patterns in adolescents with schizophrenia and adolescents with bipolar disorder with psychosis: A magnetoencephalography study

“…Moreover, individual MR images were not available, which might have reduced the accuracy of the DCM estimates. Also, and this is a general problem of electrophysiological recordings, some artefacts (e.g., muscular) might not have been removed during pre-processing (e.g., Gonzalez-Moreno et al, 2014).…”

“…Because of the strength (rms) property of gradiometers, the notions of ERP amplitude and neuronal activity will be used interchangeably in the following wherever possible. The data were pre-processed using signal space separation (Taulu et al, 2004) and standard projection methods to remove ocular and cardiac artefacts (e.g., Gonzalez-Moreno et al, 2014). A small number of trials (< 0.5%) were excluded because of other artefacts (muscle tension, sensor jumps, etc.)…”

Mind the Noise When Identifying Computational Models of Cognition from Brain Activity

“…Consequently, the effects of measurement error remain subject to variation which—as shown—affects the validity of the model selection. Several methods have been suggested for cleaning data (e.g., Turetsky et al, 1989; Effern et al, 2000; Quiroga, 2000; He et al, 2004; Gonzalez-Moreno et al, 2014; Ouyang et al, 2015). However, even though the average data quality can be improved, this does not compensate the insufficiency of the data in potentially many studies.…”

Effects of reconstructed magnetic field from sparse noisy boundary measurements on localization of active neural source

“…Herein, nonlinear parametric models are formulated as an optimization problem (that minimizes the difference between measurements and model predictions) and solved with a least-square (LS) method for the six parameters required to characterize the position vector L and dipole moment Q of the ECD [11,12]. In ANS localization studies, the extremely weak MFD measured outside of the head is several orders smaller than its external interference; for instance, the power ratio of MEG signals to noise equaling 1 is common for clinical event-related measurements [7,8,10].…”

“…Blind-source separation methods, such as independent component analysis, rely on the assumption that the noise is stable across time and can be described with a limited number of spatial components like eye and cardiac artifacts, and requires time-consuming visual inspection [10]. For spatial filtering, such as signal space projection [16,20] and signal space separation [17,19], distortion of spatial features representing real MEG signals will be involved [10,23]. Reduction in unrelated noise (independent, identically distributed under normal distribution, and free from physiological noises) is important for single-trial or stimulus-absent analysis.…”