Limits...
Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals.

Demanuele C, James CJ, Sonuga-Barke EJ - Behav Brain Funct (2007)

Bottom Line: It has been acknowledged that the frequency spectrum of measured electromagnetic (EM) brain signals shows a decrease in power with increasing frequency.Applying the proposed method to various exemplary datasets including very low frequency EEG recordings, epileptic seizure recordings, MEG data and Evoked Response data showed that this compensating procedure provides a flat spectral base onto which event related peaks can be clearly observed.Findings suggest that the proposed filter is a useful tool for the analysis of physiological data especially in revealing very low frequency peaks which may otherwise be obscured by the 1/f spectral activity inherent in EEG/MEG recordings.

View Article: PubMed Central - HTML - PubMed

Affiliation: Signal Processing and Control Group, Institute of Sound and Vibration Research, University of Southampton, Southampton, UK. cd3@soton.ac.uk.

ABSTRACT

Background: It has been acknowledged that the frequency spectrum of measured electromagnetic (EM) brain signals shows a decrease in power with increasing frequency. This spectral behaviour may lead to difficulty in distinguishing event-related peaks from ongoing brain activity in the electro- and magnetoencephalographic (EEG and MEG) signal spectra. This can become an issue especially in the analysis of low frequency oscillations (LFOs) - below 0.5 Hz - which are currently being observed in signal recordings linked with specific pathologies such as epileptic seizures or attention deficit hyperactivity disorder (ADHD), in sleep studies, etc.

Methods: In this work we propose a simple method that can be used to compensate for this 1/f trend hence achieving spectral normalisation. This method involves filtering the raw measured EM signal through a differentiator prior to further data analysis.

Results: Applying the proposed method to various exemplary datasets including very low frequency EEG recordings, epileptic seizure recordings, MEG data and Evoked Response data showed that this compensating procedure provides a flat spectral base onto which event related peaks can be clearly observed.

Conclusion: Findings suggest that the proposed filter is a useful tool for the analysis of physiological data especially in revealing very low frequency peaks which may otherwise be obscured by the 1/f spectral activity inherent in EEG/MEG recordings.

No MeSH data available.


Related in: MedlinePlus

The effect of the filter on the spectrum of the signals recorded around the seizure focus. (a) T3 signal spectrum, (b) T9 signal spectrum; Note the removal of the 1/f trend and the clear peak around 4.5 Hz indicating the rhythmic seizure activity.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2235870&req=5

Figure 12: The effect of the filter on the spectrum of the signals recorded around the seizure focus. (a) T3 signal spectrum, (b) T9 signal spectrum; Note the removal of the 1/f trend and the clear peak around 4.5 Hz indicating the rhythmic seizure activity.

Mentions: Figure 12 and Figure 13 show the effect of filtering on the spectrum of selected EEG channels. After filtering the original spectra in Figure 12(a) and (b) are flattened and the 1/f trend is clearly removed. Moreover, the peak around 5 Hz, which is related to the rhythmic seizure activity becomes much more pronounced in the filtered spectra. The spectrum of the frontopolar channel (Fp2) in Figure 13 is also normalised by the differentiator and low frequency peaks due to eye-related activity become clearly visible.


Distinguishing low frequency oscillations within the 1/f spectral behaviour of electromagnetic brain signals.

Demanuele C, James CJ, Sonuga-Barke EJ - Behav Brain Funct (2007)

The effect of the filter on the spectrum of the signals recorded around the seizure focus. (a) T3 signal spectrum, (b) T9 signal spectrum; Note the removal of the 1/f trend and the clear peak around 4.5 Hz indicating the rhythmic seizure activity.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2235870&req=5

Figure 12: The effect of the filter on the spectrum of the signals recorded around the seizure focus. (a) T3 signal spectrum, (b) T9 signal spectrum; Note the removal of the 1/f trend and the clear peak around 4.5 Hz indicating the rhythmic seizure activity.
Mentions: Figure 12 and Figure 13 show the effect of filtering on the spectrum of selected EEG channels. After filtering the original spectra in Figure 12(a) and (b) are flattened and the 1/f trend is clearly removed. Moreover, the peak around 5 Hz, which is related to the rhythmic seizure activity becomes much more pronounced in the filtered spectra. The spectrum of the frontopolar channel (Fp2) in Figure 13 is also normalised by the differentiator and low frequency peaks due to eye-related activity become clearly visible.

Bottom Line: It has been acknowledged that the frequency spectrum of measured electromagnetic (EM) brain signals shows a decrease in power with increasing frequency.Applying the proposed method to various exemplary datasets including very low frequency EEG recordings, epileptic seizure recordings, MEG data and Evoked Response data showed that this compensating procedure provides a flat spectral base onto which event related peaks can be clearly observed.Findings suggest that the proposed filter is a useful tool for the analysis of physiological data especially in revealing very low frequency peaks which may otherwise be obscured by the 1/f spectral activity inherent in EEG/MEG recordings.

View Article: PubMed Central - HTML - PubMed

Affiliation: Signal Processing and Control Group, Institute of Sound and Vibration Research, University of Southampton, Southampton, UK. cd3@soton.ac.uk.

ABSTRACT

Background: It has been acknowledged that the frequency spectrum of measured electromagnetic (EM) brain signals shows a decrease in power with increasing frequency. This spectral behaviour may lead to difficulty in distinguishing event-related peaks from ongoing brain activity in the electro- and magnetoencephalographic (EEG and MEG) signal spectra. This can become an issue especially in the analysis of low frequency oscillations (LFOs) - below 0.5 Hz - which are currently being observed in signal recordings linked with specific pathologies such as epileptic seizures or attention deficit hyperactivity disorder (ADHD), in sleep studies, etc.

Methods: In this work we propose a simple method that can be used to compensate for this 1/f trend hence achieving spectral normalisation. This method involves filtering the raw measured EM signal through a differentiator prior to further data analysis.

Results: Applying the proposed method to various exemplary datasets including very low frequency EEG recordings, epileptic seizure recordings, MEG data and Evoked Response data showed that this compensating procedure provides a flat spectral base onto which event related peaks can be clearly observed.

Conclusion: Findings suggest that the proposed filter is a useful tool for the analysis of physiological data especially in revealing very low frequency peaks which may otherwise be obscured by the 1/f spectral activity inherent in EEG/MEG recordings.

No MeSH data available.


Related in: MedlinePlus