Electroencephalogram (EEG) signals are often used as an input modality for Brain Computer Interfaces (BCIs). While EEG signals can be beneficial for numerous types of interaction scenarios in the real world, high levels of noise limits their usage to strictly noise-controlled environments such as a research laboratory. Even in a controlled environment, EEG is susceptible to noise, particularly from user motion, making it highly challenging to use EEG, and consequently BCI, as a ubiquitous user interaction modality. In this work, we address the EEG noise/artifact correction problem. Our goal is to detect physiological artifacts in EEG signal and automatically replace the detected artifacts with imputed values to enable robust EEG sensing overall requiring significantly reduced manual effort than is usual. We present a novel EEG state-based imputation model built upon a recurrent neural network, which we call SRI-EEG, and evaluate the proposed method on three publicly available EEG datasets. From quantitative and qualitative comparisons with six conventional and neural network based approaches, we demonstrate that our method achieves comparable performance to the state-of-the-art methods on the EEG artifact correction task.
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Improved field-mapping and artifact correction in multispectral imaging: Field-Mapping and Artifact Correction in MSI
- NSF-PAR ID:
- 10034612
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Magnetic Resonance in Medicine
- Volume:
- 78
- Issue:
- 5
- ISSN:
- 0740-3194
- Page Range / eLocation ID:
- 2022 to 2034
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Pfefer, T. Joshua ; Hwang, Jeeseong ; Vargas, Gracie (Ed.)
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Purpose To enable rapid imaging with a scan time–efficient 3D cones trajectory with a deep‐learning off‐resonance artifact correction technique.
Methods A residual convolutional neural network to correct off‐resonance artifacts (Off‐ResNet) was trained with a prospective study of pediatric MRA exams. Each exam acquired a short readout scan (1.18 ms ± 0.38) and a long readout scan (3.35 ms ± 0.74) at 3 T. Short readout scans, with longer scan times but negligible off‐resonance blurring, were used as reference images and augmented with additional off‐resonance for supervised training examples. Long readout scans, with greater off‐resonance artifacts but shorter scan time, were corrected by autofocus and Off‐ResNet and compared with short readout scans by normalized RMS error, structural similarity index, and peak SNR. Scans were also compared by scoring on 8 anatomical features by two radiologists, using analysis of variance with post hoc Tukey's test and two one‐sided t‐tests. Reader agreement was determined with intraclass correlation.
Results The total scan time for long readout scans was on average 59.3% shorter than short readout scans. Images from Off‐ResNet had superior normalized RMS error, structural similarity index, and peak SNR compared with uncorrected images across ±1 kHz off‐resonance (
P < .01). The proposed method had superior normalized RMS error over −677 Hz to +1 kHz and superior structural similarity index and peak SNR over ±1 kHz compared with autofocus (P < .01). Radiologic scoring demonstrated that long readout scans corrected with Off‐ResNet were noninferior to short readout scans (P < .05).Conclusion The proposed method can correct off‐resonance artifacts from rapid long‐readout 3D cones scans to a noninferior image quality compared with diagnostically standard short readout scans.
