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| http://dx.doi.org/10.3389/fpsyg.2015.01456 | DOI Listing |
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Repairbads: An automatic and adaptive method to repair bad channels and segments for OPM-MEG.
Neuroimage
January 2025
Key Laboratory of Ultra-Weak Magnetic Field Measurement Technology, Ministry of Education, School of Instrumentation and Optoelectronic Engineering, Beihang University, 100191, Beijing, China; Hangzhou Institute of Extremely-Weak Magnetic Field Major National Science and Technology Infrastructure, Hangzhou, 310051, China; State Key Laboratory of Traditional Chinese Medicine Syndrome/Health Construction Center, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, China; Hefei National Laboratory, Hefei, 230088, China. Electronic address:
The optically pumped magnetometer (OPM) based magnetoencephalography (MEG) system offers advantages such as flexible layout and wearability. However, the position instability or jitter of OPM sensors can result in bad channels and segments, which significantly impede subsequent preprocessing and analysis. Most common methods directly reject or interpolate to repair these bad channels and segments.
View Article and Find Full Text PDFAssessing serial recall as a measure of artificial grammar learning.
Front Psychol
December 2024
Department of Psychology, Emory University, Atlanta, GA, United States.
Introduction: Implicit statistical learning is, by definition, learning that occurs without conscious awareness. However, measures that putatively assess implicit statistical learning often require explicit reflection, for example, deciding if a sequence is 'grammatical' or 'ungrammatical'. By contrast, 'processing-based' tasks can measure learning without requiring conscious reflection, by measuring processes that are facilitated by implicit statistical learning.
View Article and Find Full Text PDFLOTUS Software to Process Wearable EmbracePlus Data.
Sensors (Basel)
November 2024
Science of Learning in Education Centre, National Institute of Education, Nanyang Technological University, Singapore 637616, Singapore.
The Empatica EmbracePlus is a recent innovation in medical-grade wristband wearable sensors that enable unobtrusive continuous measurement of pulse rate, electrodermal activity, skin temperature, and various accelerometry-based actigraphy measures using a minimalistic smartwatch design. The advantage of this lightweight wearable is the potential for holistic longitudinal recording and monitoring of physiological processes that index a suite of autonomic functions, as well as to provide ecologically valid insights into human behaviour, health, physical activity, and psychophysiological processes. Given the longitudinal nature of wearable recordings, EmbracePlus data collection is managed by storing raw timeseries in short 'chunks' in avro file format organised by universal standard time.
View Article and Find Full Text PDFDeveloping and Evaluating Large Language Model-Generated Emergency Medicine Handoff Notes.
JAMA Netw Open
December 2024
Department of Emergency Medicine, NewYork-Presbyterian/Weill Cornell Medicine, New York.
Importance: An emergency medicine (EM) handoff note generated by a large language model (LLM) has the potential to reduce physician documentation burden without compromising the safety of EM-to-inpatient (IP) handoffs.
Objective: To develop LLM-generated EM-to-IP handoff notes and evaluate their accuracy and safety compared with physician-written notes.
Design, Setting, And Participants: This cohort study used EM patient medical records with acute hospital admissions that occurred in 2023 at NewYork-Presbyterian/Weill Cornell Medical Center.
Exploring the hierarchical structure of human plans via program generation.
Cognition
February 2025
Department of Psychology, Princeton University, USA; Department of Computer Science, Princeton University, USA.
Human behavior is often assumed to be hierarchically structured, made up of abstract actions that can be decomposed into concrete actions. However, behavior is typically measured as a sequence of actions, which makes it difficult to infer its hierarchical structure. In this paper, we explore how people form hierarchically structured plans, using an experimental paradigm with observable hierarchical representations: participants create programs that produce sequences of actions in a language with explicit hierarchical structure.
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