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SPHARA--a generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG.

Graichen U, Eichardt R, Fiedler P, Strohmeier D, Zanow F, Haueisen J - PLoS ONE (2015)

Bottom Line: Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction.Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary.We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomedical Engineering and Informatics, Faculty of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany.

ABSTRACT
Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.

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Related in: MedlinePlus

Grand average of SEP data.The grand average of the MGFP is marked by the blue line. The range of the grand average ± the standard deviation at the time sample is orange shaded.
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pone.0121741.g004: Grand average of SEP data.The grand average of the MGFP is marked by the blue line. The range of the grand average ± the standard deviation at the time sample is orange shaded.

Mentions: The positions of the EEG electrodes were digitized using an optical ANT Neuro BV (ant-neuro.com) xensor 3D electrode digitizer system, see Fig 2. EEG signals were recorded using an ANT Neuro BV waveguard 256-channel EEG cap with an equidistant electrode layout and two cascaded RefaExt 128 channel amplifiers and transformed to common average reference. Data were sampled at 2048 Hz and software high-pass (24 dB/oct, cutoff-frequency 2 Hz) and notch (50 Hz and two harmonics) filtered. All trials were manually checked for artifacts, the remaining trials were averaged, see S1 Dataset. The mean global field power (MGFP) and topographic maps of the averaged and filtered SEP data of a single volunteer at selected points in time are shown in Fig 3. In summary, the grand average (GA) and the range of ± the standard deviation of the MGFP of all subjects is shown in Fig 4. The exact points in time of the maximum potentials vary between volunteers. For each volunteer, the specific time of the P14, N20 and N30 potentials are manually estimated.


SPHARA--a generalized spatial Fourier analysis for multi-sensor systems with non-uniformly arranged sensors: application to EEG.

Graichen U, Eichardt R, Fiedler P, Strohmeier D, Zanow F, Haueisen J - PLoS ONE (2015)

Grand average of SEP data.The grand average of the MGFP is marked by the blue line. The range of the grand average ± the standard deviation at the time sample is orange shaded.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4401437&req=5

pone.0121741.g004: Grand average of SEP data.The grand average of the MGFP is marked by the blue line. The range of the grand average ± the standard deviation at the time sample is orange shaded.
Mentions: The positions of the EEG electrodes were digitized using an optical ANT Neuro BV (ant-neuro.com) xensor 3D electrode digitizer system, see Fig 2. EEG signals were recorded using an ANT Neuro BV waveguard 256-channel EEG cap with an equidistant electrode layout and two cascaded RefaExt 128 channel amplifiers and transformed to common average reference. Data were sampled at 2048 Hz and software high-pass (24 dB/oct, cutoff-frequency 2 Hz) and notch (50 Hz and two harmonics) filtered. All trials were manually checked for artifacts, the remaining trials were averaged, see S1 Dataset. The mean global field power (MGFP) and topographic maps of the averaged and filtered SEP data of a single volunteer at selected points in time are shown in Fig 3. In summary, the grand average (GA) and the range of ± the standard deviation of the MGFP of all subjects is shown in Fig 4. The exact points in time of the maximum potentials vary between volunteers. For each volunteer, the specific time of the P14, N20 and N30 potentials are manually estimated.

Bottom Line: Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction.Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary.We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.

View Article: PubMed Central - PubMed

Affiliation: Institute of Biomedical Engineering and Informatics, Faculty of Computer Science and Automation, Technische Universität Ilmenau, Ilmenau, Germany.

ABSTRACT
Important requirements for the analysis of multichannel EEG data are efficient techniques for signal enhancement, signal decomposition, feature extraction, and dimensionality reduction. We propose a new approach for spatial harmonic analysis (SPHARA) that extends the classical spatial Fourier analysis to EEG sensors positioned non-uniformly on the surface of the head. The proposed method is based on the eigenanalysis of the discrete Laplace-Beltrami operator defined on a triangular mesh. We present several ways to discretize the continuous Laplace-Beltrami operator and compare the properties of the resulting basis functions computed using these discretization methods. We apply SPHARA to somatosensory evoked potential data from eleven volunteers and demonstrate the ability of the method for spatial data decomposition, dimensionality reduction and noise suppression. When employing SPHARA for dimensionality reduction, a significantly more compact representation can be achieved using the FEM approach, compared to the other discretization methods. Using FEM, to recover 95% and 99% of the total energy of the EEG data, on average only 35% and 58% of the coefficients are necessary. The capability of SPHARA for noise suppression is shown using artificial data. We conclude that SPHARA can be used for spatial harmonic analysis of multi-sensor data at arbitrary positions and can be utilized in a variety of other applications.

Show MeSH
Related in: MedlinePlus