A new combination: scale-space filtering of projected brain activities

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Aydin S.

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, vol.47, pp.435-440, 2009 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 47
  • Publication Date: 2009
  • Doi Number: 10.1007/s11517-009-0450-3
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED)
  • Page Numbers: pp.435-440
  • Keywords: Scale-space, Evoked potential, Projection, EVENT-RELATED POTENTIALS, SINGULARITY DETECTION
  • Hacettepe University Affiliated: No



In the present study, well known scale-space filtering (SSF) algorithm is used in combination with a linear mapping approach (LMA) to obtain clear auditory evoked potential (EP) waveform. The proposed combination involves two sequential steps: At first, the EEG noise level is reduced from -5 to 0 dB owing to the LMA based on the singular-value-decomposition. In the secondary process, the EEG noise remaining on the projected data is removed by using the SSF. A small number of sweeps are composed as a raw matrix to project the data without using the ensemble averaging at the beginning of the proposed method. Then, single sweeps are individually filtered in wavelet domain by using the SSF in the secondary step. The experimental results show that the SSF can extract the clear single-sweep auditory EP waveform where the LMA is used as a primary filtering. As well, the results indicate that the EP signal and background EEG noise create different wavelet coefficients due to their different characteristics. However, this characteristic difference can be considered to distinguish the EP signal and the EEG noise when the Signal-to-Noise-Ratio is higher than 0 dB.

Discussion and Conclusion: The proposed algorithm, combines the LMA and the SSF, is presented to estimate clear auditory EP waveforms for a case study. Experimental results show that EEG noise level can be considerably decreased by using the LMA as a primary application. In other words, the input SNR at about −5 dB is increased to 0 dB at least in the first step of the proposed method. Then, clear EP waveforms are provided by the SSF, since the WT coefficients corresponding to the EP signal and the EEG noise becomes distinguishable due to higher SNR conditions. In summary, the SSF is found to be useful algorithm to filter out the EEG noise when the input SNR is higher than or equal to 0 dB. In case of lower SNR conditions, the LMA can be used as a primary process.

As a future work, the presented approach will be performed for both visual and somatosensory EPs. In addition, several simulated component variations will be attempted, as well. Single-trial EP components such as amplitude and latency may vary depending on some psycho-physiological or neuro-physiological disorders. Each type of component variation can be considered separately in the proposed combination. In case of amplitude variations, the rank of the data matrix does not change due to the fact that all raw sweeps are linearly dependent. Besides, this is reverse in case of latency variations such that the rank, i.e. the dimension of the signal subspace is relevant to the number of linearly independent sweeps.