The analysis of event-related signals (ERS’s) in neurophysiological studies aims at exploring the information processing in the human brain. When presenting auditory, visual and other stimuli the electromagnetic brain signals recorded as electroencephalogram (EEG) and/or magnetoencephalogram (MEG) (working group 8.21) reflect the corresponding brain activity.
ERS’s are embedded in the spontaneous EEG/MEG activity and background noise and they are typically small in amplitude. Usually, stimulus synchronous averaging is carried out to improve the signal-to-noise ratio (SNR). However, such a procedure does not account for the variability between single ERS’s. In order to avoid this loss of information analysis of single-trial ERS’s has to be carried out. The challenging task for such an analysis is the low SNR together with the fact that single ERS’s and spontaneous EEG/MEG activity have large spectral overlap.
Signal processing procedures, currently developed at PTB, focus on the estimation of the single-trial ERS parameters amplitude and latency. By means of suitable bandpass filtering and application of the Hilbert transform the relevant spectral contents of an ERS can be decomposed into two independent signals, envelope and phase. From these signals then amplitude and latency can be derived.
The bandpass filtering procedure is illustrated using averaged auditory event-related fields (MEG) to sounds of 125 Hz and 1000 Hz. Figure 1 shows the averaged signals and the derived envelope and sine-phase signals. Within the time interval from 100 ms to 150 ms the sine-phase waves can be used to determine latency differences between the event-related fields to sounds of 125 Hz and 1000 Hz.
For EEG/MEG recordings typically an array of spatially distributed sensors is used. This enables the construction of a spatial filter, which can substantially improve the estimation of parameters from single-trial ERS’s. Spatial filtering aims at the suppression of signals from interfering sources, e.g. the spontaneous activity, while leaving the ERS’s unaffected.
The effect of the different filtering steps upon single-trial ERS’s is shown in Figure 2 for one exemplary MEG channel. The spatial filter was constructed from the 93-channel MEG using Noise Adjusted Principal Component Analysis (NAPCA). By spatial filtering a substantial reduction of interfering signal components is achieved and single-trial responses can easily be recognized.
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Title: | Comparing a template approach and complex bandpass filtering for single-trial analysis of auditory evoked M100 |
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Author(s): | A. Link, M. Burghoff, A. Salajegheh, D. Poeppel, L. Trahms and C. Elster |
Journal: | Biomedizinische Technik. Biomedical engineering |
Year: | 2007 |
Volume: | 52 |
Issue: | 1 |
Pages: | 106--10 |
DOI: | 10.1515/BMT.2007.020 |
ISSN: | 0013-5585 |
Web URL: | http://www.ncbi.nlm.nih.gov/pubmed/17313344 |
Keywords: | Algorithms,Auditory,Auditory: physiology,Computer-Assisted,Computer-Assisted: methods,Diagnosis, Computer-Assisted,Diagnosis, Computer-Assisted: methods,Electroencephalography,Electroencephalography: methods,Evoked Potentials, Auditory,Evoked Potentials, Auditory: physiology,Likelihood Functions,Reproducibility of Results,Sample Size,Sensitivity and Specificity,Signal Processing, Computer-Assisted |
Tags: | 8.42, Gehirn |
Abstract: | Two methods for single-trial analysis were compared, an established parametric template approach and a recently proposed non-parametric method based on complex bandpass filtering. The comparison was carried out by means of pseudo-real simulations based on magnetoencephalography measurements of cortical responses to auditory signals. The comparison focused on amplitude and latency estimation of the M100 response. The results show that both methods are well suited for single-trial analysis of the auditory evoked M100. While both methods performed similarly with respect to latency estimation, the non-parametric approach was observed to be more robust for amplitude estimation. The non-parametric approach can thus be recommended as an additional valuable tool for single-trial analysis. |