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Analysis of event-related brain signals

Working Group 8.42

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.

Auditory evoked ERS using two tone frequencies. Decomposition of averaged event-related fields (MEG) into envelope and sine-phase.
Fig. 1: Auditory evoked ERS using two tone frequencies. Decomposition of averaged event-related fields (MEG) into envelope and sine-phase.

 

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.

Application of spatial and bandpass filtering to single-trial ERS’s. The orange line represents the averaged ERS.
Fig. 2: Application of spatial and bandpass filtering to single-trial ERS’s. The orange line represents the averaged ERS.

 

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.

results of a single-trial latency analysis of MEG recordings upon auditory stimulation. Two different stimulation frequencies were used and the results indicate a mean latency difference between the two stimulation classes. Additional spatial filtering re
Figure 3 shows results of a single-trial latency analysis of MEG recordings upon auditory stimulation. Two different stimulation frequencies were used and the results indicate a mean latency difference between the two stimulation classes. Additional spatial filtering reveals this latency difference already for single-trial ERS’s.
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PhD thesis

Title: Blind separation of dependent source signals for MEG sensory stimulation experiments
Author(s): F. Kohl
Work type: PhD Thesis
Year: 2013
School: TU Berlin
Tags: 8.42,Gehirn,SingleTrial

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