Dataset Information

Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling

ABSTRACT: Background: Electroencephalography (EEG)-derived event-related potentials (ERPs) provide information about a variety of brain functions, but often suffer from low inherent signal-to-noise ratio (SNR). To overcome the low SNR, techniques that pool data from multiple sensors have been applied. However, such pooling implicitly assumes that the SNR among sensors is equal, which is not necessarily valid. This study presents a novel approach for signal pooling that accounts for differential SNR among sensors. Methods: The new technique involves pooling together signals from multiple EEG channels weighted by their respective SNRs relative to the overall SNR of all channels. We compared ERP responses derived using this new technique with those derived using both individual channels as well as traditional averaged-based channel pooling. The outcomes were evaluated in both simulated data and real data from healthy adult volunteers (n = 37). Responses corresponding to a range of ERP components indexing auditory sensation (N100), attention (P300) and language processing (N400) were evaluated. Results: Simulation results demonstrate that, compared to traditional pooling technique, the new SNR-weighted channel pooling technique improved ERP response effect size in cases of unequal noise among channels (p’s < 0.001). Similarly, results from real-world experimental data showed that the new technique resulted in significantly greater ERP effect sizes compared to either traditional pooling or individual channel approach for all three ERP components (p’s < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (p’s < 0.001). Conclusion: These results suggest that the new technique improves the capture of ERP responses relative to traditional techniques. As such, SNR-weighted channel pooling can further enable widespread applications of ERP techniques, especially those that require rapid assessments in noisy out-of-laboratory environments.

SUBMITTER: Hajra S

PROVIDER: S-EPMC8588067 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:PurposeDynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is increasingly used for breast cancer diagnosis as supplementary to conventional imaging techniques. Combining of diffusion-weighted imaging (DWI) of morphology and kinetic features from DCE-MRI to improve the discrimination power of malignant from benign breast masses is rarely reported.Materials and methodsThe study comprised of 234 female patients with 85 benign and 149 malignant lesions. Four distinct groups of features, coupling with pathological tests, were estimated to comprehensively characterize the pictorial properties of each lesion, which was obtained by a semi-automated segmentation method. Classical machine learning scheme including feature subset selection and various classification schemes were employed to build prognostic model, which served as a foundation for evaluating the combined effects of the multi-sided features for predicting of the types of lesions. Various measurements including cross validation and receiver operating characteristics were used to quantify the diagnostic performances of each feature as well as their combination.ResultsSeven features were all found to be statistically different between the malignant and the benign groups and their combination has achieved the highest classification accuracy. The seven features include one pathological variable of age, one morphological variable of slope, three texture features of entropy, inverse difference and information correlation, one kinetic feature of SER and one DWI feature of apparent diffusion coefficient (ADC). Together with the selected diagnostic features, various classical classification schemes were used to test their discrimination power through cross validation scheme. The averaged measurements of sensitivity, specificity, AUC and accuracy are 0.85, 0.89, 90.9% and 0.93, respectively.ConclusionMulti-sided variables which characterize the morphological, kinetic, pathological properties and DWI measurement of ADC can dramatically improve the discriminatory power of breast lesions.

Project description:Background Dynamic contrast agent-enhanced (DCE) perfusion MRI may help differentiate between nonneoplastic and malignant lesions in the spine. Purpose To investigate the correlation between fractional plasma volume (Vp), a parameter derived from DCE perfusion MRI, and histopathologic diagnosis for spinal lesions. Materials and Methods In this retrospective study, patients who underwent DCE perfusion MRI and lesion biopsy between May 2015 and May 2018 were included. Inclusion criteria were short time interval (<30 days) between DCE perfusion MRI and biopsy, DCE perfusion MRI performed before biopsy, and DCE perfusion MRI performed at the same spine level as biopsy. Exclusion criteria were prior radiation treatment on vertebrae of interest, poor DCE perfusion MRI quality, nondiagnostic biopsy, and extensive spinal metastasis or prior kyphoplasty. One hundred thirty-four lesions were separated into a nonneoplastic group (n = 51) and a malignant group (n = 83) on the basis of histopathologic analysis. Two investigators manually defined regions of interest in the vertebrae. DCE perfusion MRI parameter Vp was calculated by using the Tofts pharmacokinetic two-compartment model. Vp was quantified, normalized to adjacent normal vertebrae, and compared between the two groups. A Mann-Whitney U test and receiver operating characteristic analysis was performed to verify the difference in Vp between the nonneoplastic and malignant groups. Reproducibility was assessed by calculating the Cohen κ coefficient. Results One hundred patients (mean age, 65 years ± 11 [standard deviation]; 52 men) were evaluated. Vp was lower in nonneoplastic lesions versus malignant lesions (1.6 ± 1.3 vs 4.2 ± 3.0, respectively; P < .001). The sensitivity of Vp was 93% (77 of 83; 95% confidence interval [CI]: 85%, 97%), specificity was 78% (40 of 51; 95% CI: 65%, 89%), and area under the receiver operating characteristic curve was 0.88 (95% CI: 0.82, 0.95). Cohen κ coefficient suggested substantial agreement in both intra- (κ = 0.72) and interreader (κ = 0.70) reproducibility. Conclusion This study indicated that dynamic contrast agent-enhanced perfusion MRI parameter, fractional plasma volume, was able to differentiate between nonneoplastic spinal lesions and malignant lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Haller in this issue.

Dataset Information

Event Related Potential Signal Capture Can Be Enhanced through Dynamic SNR-Weighted Channel Pooling

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets