We revisit a previous study on inter-session variability (McGonigle et al. [2000]: Neuroimage 11:708-734), showing that contrary to one popular interpretation of the original article, inter-session variability is not necessarily high. We also highlight how evaluating variability based on thresholded single-session images alone can be misleading. Finally, we show that the use of different first-level preprocessing, time-series statistics, and registration analysis methodologies can give significantly different inter-session analysis results.
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http://dx.doi.org/10.1002/hbm.20080 | DOI Listing |
Prog Biomed Eng (Bristol)
October 2024
Neural Engineering Lab, Department of Biosciences and Bioengineering, Indian Institute of Technology Guwahati, Assam 781039, India.
This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)-based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL.
View Article and Find Full Text PDFPLoS One
October 2024
Department of Physiotherapy, Singapore General Hospital, Singapore, Singapore.
Heliyon
September 2024
Department of Biomedical Engineering, Korea University College of Medicine, Seoul, 02841, Republic of Korea.
J Biomech
November 2024
KU Leuven, Department of Rehabilitation Sciences, Leuven, Belgium; Clinical Motion Analysis Laboratory, University Hospitals Leuven, Leuven, Belgium.
Three-dimensional gait analysis is the 'gold standard' for measurement and description of gait. Gait variability can arise from intrinsic and extrinsic factors and may vary between walking conditions. This study aimed to define the inter-trial and inter-session repeatability in gait analysis data of children with cerebral palsy (CP) who were walking in four conditions, namely barefoot or with ankle-foot orthosis (AFO), and overground or treadmill.
View Article and Find Full Text PDFIEEE Trans Biomed Circuits Syst
September 2024
Driven by the progress in efficient embedded processing, there is an accelerating trend toward running machine learning models directly on wearable Brain-Machine Interfaces (BMIs) to improve portability and privacy and maximize battery life. However, achieving low latency and high classification performance remains challenging due to the inherent variability of electroencephalographic (EEG) signals across sessions and the limited onboard resources. This work proposes a comprehensive BMI workflow based on a CNN-based Continual Learning (CL) framework, allowing the system to adapt to inter-session changes.
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