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Purpose: Artifacts in magnetic resonance imaging (MRI) scans degrade image quality and thus negatively affect the outcome measures of clinical and research scanning. Considering the time-consuming and subjective nature of visual quality control (QC), multiple (semi-)automatic QC algorithms have been developed. This systematic review presents an overview of the available (semi-)automatic QC algorithms and software packages designed for raw, structural T1-weighted (T1w) MRI datasets. The objective of this review was to identify the differences among these algorithms in terms of their features of interest, performance, and benchmarks. Methods: We queried PubMed, EMBASE (Ovid), and Web of Science databases on the fifth of January 2023, and cross-checked reference lists of retrieved papers. Bias assessment was performed using PROBAST (Prediction model Risk Of Bias ASsessment Tool). Results: A total of 18 distinct algorithms were identified, demonstrating significant variations in methods, features, datasets, and benchmarks. The algorithms were categorized into rule-based, classical machine learning-based, and deep learning-based approaches. Numerous unique features were defined, which can be roughly divided into features capturing entropy, contrast, and normative measures. Conclusion: Due to dataset-specific optimization, it is challenging to draw broad conclusions about comparative performance. Additionally, large variations exist in the used datasets and benchmarks, further hindering direct algorithm comparison. The findings emphasize the need for standardization and comparative studies for advancing QC in MR imaging. Efforts should focus on identifying a dataset-independent measure as well as algorithm-independent methods for assessing the relative performance of different approaches. [ABSTRACT FROM AUTHOR]

Feature extraction and classification is a vital part in motor imagery-based brain-computer interface (BCI) system. Traditional deep learning (DL) methods usually perform better with more labeled training samples. Unfortunately, the labeled samples are usually scarce for electroencephalography (EEG) data, while unlabeled samples are available in large quantity and easy to collect. In addition, traditional DL algorithms are notoriously time-consuming for the training process. To address these issues, a novel method of hierarchical semi-supervised extreme learning machine (HSS-ELM) is proposed in this paper and applied for motor imagery (MI) task classification. Firstly, the deep architecture of hierarchical ELM (H-ELM) approach is employed for feature learning automatically, and then these new high-level features are classified using the semi-supervised ELM (SS-ELM) algorithm which can exploit the information from both labeled and unlabeled data. Extensive experiments were conducted on some benchmark datasets and EEG datasets to evaluate the effectiveness of the proposed method. Compared with several state-of-the-art methods, including SVM, ELM, SAE, H-ELM, and SS-ELM, our HSS-ELM method can achieve better classification accuracy, a mean kappa value of 0.7945 and 0.5701 across all subjects in the training and evaluation sessions of BCI Competition IV Dataset 2a, respectively. Finally, it comes to the conclusion that the proposed method has achieved superior performance for feature extraction and classification of EEG signals. Graphical abstract The schematic of the proposed HSS-ELM algorithm. [ABSTRACT FROM AUTHOR]