Response-related potentials during semantic priming: the effect of a speeded button response task on ERPs

This study examines the influence of a button response task on the event-related potential (ERP) in a semantic priming experiment. Of particular interest is the N400 component. In many semantic priming studies, subjects are asked to respond to a stimulus as fast and accurately as possible by pressing a button. Response time (RT) is recorded in parallel with an electroencephalogram (EEG) for ERP analysis. In this case, the response occurs in the time window used for ERP analysis and response-related components may overlap with stimulus-locked ones such as the N400. This has led to a recommendation against such a design, although the issue has not been explored in depth. Since studies keep being published that disregard this issue, a more detailed examination of influence of response-related potentials on the ERP is needed. Two experiments were performed in which subjects pressed one of two buttons with their dominant hand in response to word-pairs with varying association strength (AS), indicating a personal judgement of association between the two words. In the first experiment, subjects were instructed to respond as fast and accurately as possible. In the second experiment, subjects delayed their button response to enforce a one second interval between the onset of the target word and the button response. Results show that in the first experiment a P3 component and motor-related potentials (MRPs) overlap with the N400 component, which can cause a misinterpretation of the latter. In order to study the N400 component, the button response should be delayed to avoid contamination of the ERP with response-related components

Characterizing Patient-Reported Fatigue Using Electronic Diaries in Neurodegenerative and Immune-Mediated Inflammatory Diseases: Observational Study

\ua9 Adrien Bennetot, Rana Zia Ur Rehman, Robbin Romijnders, Zhi Li, Victoria Macrae, Kristen Davies, Wan-Fai Ng, Walter Maetzler, Jennifer Kudelka, Hanna Hildesheim, Kirsten Emmert, Emma Paulides, C Janneke van der Woude, Ralf Reilmann, Svenja Aufenberg, Meenakshi Chatterjee, Nikolay V Manyakov, Cl\ue9mence Pinaud, Stefan Avey. Background: Fatigue is a prevalent and debilitating symptom in many chronic conditions, including immune-mediated inflammatory diseases (IMIDs) and neurodegenerative diseases (NDDs). Fatigue often fluctuates significantly within and between days, yet traditional patient-reported outcomes (PROs) typically rely on recall periods of a week or more, potentially missing these short-term variations. The development of digital tools, such as electronic diaries (eDiaries), offers a unique opportunity to collect granular, real-time data. However, the feasibility, adherence, and comparability of eDiary-based assessments to established PROs require further investigation. Objective: This study aimed to evaluate the feasibility and acceptability of using a high-frequency eDiary to capture intraday variability in fatigue and to compare eDiary data with scores obtained from the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), a validated weekly recall PRO. Methods: Data were collected from 159 participants enrolled in the IDEA-FAST (Identifying Digital Endpoints to Assess Fatigue, Sleep and Activities in Daily Living in Neurodegenerative Disorders and Immune-Mediated Inflammatory Diseases) feasibility study; a 4-week prospective observational study conducted at 4 European centers. Participants included individuals with NDDs (n=39), IMIDs (n=78), and healthy volunteers (n=42). Participants used an eDiary to report their physical and mental fatigue levels up to 4 times daily on a 7-point Likert scale (0=low and 6=high). Adherence was calculated as the proportion of completed eDiary entries relative to the total expected entries. Correlations between averaged eDiary scores and weekly FACIT-F scores were analyzed. Results: Adherence to the eDiary protocol was 5505/8880 (61.99%) overall, varying by cohort, with the highest adherence (1117/1200, 93.07%) observed in the primary Sj\uf6gren syndrome cohort and the lowest adherence in the Parkinson disease (410/960, 42.7%) and Huntington disease (320/720, 44.4%) cohorts. The average adherence was 430/1680 (43.45%) in the NDD cohorts and 3367/4560 (73.84%) in the IMID cohorts. Fatigue levels showed clear diurnal variation, with significantly higher fatigue reported in the evening compared to the morning (P<.001). A moderate correlation (Spearman=0.46, P<.001) was observed between eDiary fatigue scores and FACIT-F scores, with stronger cohort-specific associations for certain FACIT-F items. These results indicate that eDiaries provide complementary insights to weekly PROs by capturing intraday fluctuations in fatigue. Conclusions: This study demonstrates the feasibility, acceptability, and validity of using high-frequency eDiaries to assess fatigue in chronic conditions. By effectively detecting intra- and interday fatigue variations, eDiaries complement traditional PROs such as FACIT-F, offering a more nuanced understanding of fatigue patterns. Future research should explore optimized eDiary protocols to balance participant burden with data granularity

Association of real life postural transitions kinematics with fatigue in neurodegenerative and immune diseases

\ua9 The Author(s) 2025.Fatigue is prevalent in immune-mediated inflammatory and neurodegenerative diseases, yet its assessment relies largely on patient-reported outcomes, which capture perception but not fluctuations over time. Wearable sensors, like inertial measurement units (IMUs), offer a way to monitor daily activities and evaluate functional capacity. This study investigates the relationship between sit-to-stand and stand-to-sit transitions and self-reported physical and mental fatigue in participants with Parkinson’s, Huntington’s, rheumatoid arthritis, systemic lupus erythematosus, primary Sj\uf6gren’s syndrome and inflammatory bowel disease. Over 4 weeks, participants wore an IMU and reported fatigue levels four times daily. Using mixed-effects models, associations were identified between fatigue and specific kinematic features, such as 5th and 95th percentiles of sit-to-stand performance, suggesting that fatigue alters the control and effort of movement. These kinematic features show promise as indicators for fatigue in these patient populations

Language Model Applications to Spelling with Brain-Computer Interfaces

Within the Ambient Assisted Living (AAL) community, Brain-Computer Interfaces (BCIs) have raised great hopes as they provide alternative communication means for persons with disabilities bypassing the need for speech and other motor activities. Although significant advancements have been realized in the last decade, applications of language models (e.g., word prediction, completion) have only recently started to appear in BCI systems. The main goal of this article is to review the language model applications that supplement non-invasive BCI-based communication systems by discussing their potential and limitations, and to discern future trends. First, a brief overview of the most prominent BCI spelling systems is given, followed by an in-depth discussion of the language models applied to them. These language models are classified according to their functionality in the context of BCI-based spelling: the static/dynamic nature of the user interface, the use of error correction and predictive spelling, and the potential to improve their classification performance by using language models. To conclude, the review offers an overview of the advantages and challenges when implementing language models in BCI-based communication systems when implemented in conjunction with other AAL technologies.status: publishe

Selecting remote measurement technologies to optimize assessment of function in early Alzheimer's disease: a case study.

Despite the importance of function in early Alzheimer's disease (AD), current measures are outdated and insensitive. Moreover, COVID-19 has heighted the need for remote assessment in older people, who are at higher risk of being infection and are particularly advised to use social distancing measures, yet the importance of diagnosis and treatment of dementia remains unchanged. The emergence of remote measurement technologies (RMTs) allows for more precise and objective measures of function. However, RMT selection is a critical challenge. Therefore, this case study outlines the processes through which we identified relevant functional domains, engaged with stakeholder groups to understand participants' perspectives and worked with technical experts to select relevant RMTs to examine function. After an extensive literature review to select functional domains relevant to AD biomarkers, quality of life, rate of disease progression and loss of independence, functional domains were ranked and grouped by the empirical evidence for each. For all functional domains, we amalgamated feedback from a patient advisory board. The results were prioritized into: highly relevant, relevant, neutral, and less relevant. This prioritized list of functional domains was then passed onto a group of experts in the use of RMTs in clinical and epidemiological studies to complete the selection process, which consisted of: (i) identifying relevant functional domains and RMTs; (ii) synthesizing proposals into final RMT selection, and (iii) verifying the quality of these decisions. Highly relevant functional domains were, "difficulties at work," "spatial navigation and memory," and "planning skills and memory required for task completion." All functional domains were successfully allocated commercially available RMTs that make remote measurement of function feasible. This case study provides a set of prioritized functional domains sensitive to the early stages of AD and a set of RMTs capable of targeting them. RMTs have huge potential to transform the way we assess function in AD-monitoring for change and stability continuously within the home environment, rather than during infrequent clinic visits. Our decomposition of RMT and functional domain selection into identify, synthesize and verify activities, provides a pragmatic structure with potential to be adapted for use in future RMT selection processes

Human-Centered Design Strategies for Device Selection in mHealth Programs: Development of a Novel Framework and Case Study.

BACKGROUND: Despite the increasing use of remote measurement technologies (RMT) such as wearables or biosensors in health care programs, challenges associated with selecting and implementing these technologies persist. Many health care programs that use RMT rely on commercially available, "off-the-shelf" devices to collect patient data. However, validation of these devices is sparse, the technology landscape is constantly changing, relative benefits between device options are often unclear, and research on patient and health care provider preferences is often lacking. OBJECTIVE: To address these common challenges, we propose a novel device selection framework extrapolated from human-centered design principles, which are commonly used in de novo digital health product design. We then present a case study in which we used the framework to identify, test, select, and implement off-the-shelf devices for the Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) consortium, a research program using RMT to study central nervous system disease progression. METHODS: The RADAR-CNS device selection framework describes a human-centered approach to device selection for mobile health programs. The framework guides study designers through stakeholder engagement, technology landscaping, rapid proof of concept testing, and creative problem solving to develop device selection criteria and a robust implementation strategy. It also describes a method for considering compromises when tensions between stakeholder needs occur. RESULTS: The framework successfully guided device selection for the RADAR-CNS study on relapse in multiple sclerosis. In the initial stage, we engaged a multidisciplinary team of patients, health care professionals, researchers, and technologists to identify our primary device-related goals. We desired regular home-based measurements of gait, balance, fatigue, heart rate, and sleep over the course of the study. However, devices and measurement methods had to be user friendly, secure, and able to produce high quality data. In the second stage, we iteratively refined our strategy and selected devices based on technological and regulatory constraints, user feedback, and research goals. At several points, we used this method to devise compromises that addressed conflicting stakeholder needs. We then implemented a feedback mechanism into the study to gather lessons about devices to improve future versions of the RADAR-CNS program. CONCLUSIONS: The RADAR device selection framework provides a structured yet flexible approach to device selection for health care programs and can be used to systematically approach complex decisions that require teams to consider patient experiences alongside scientific priorities and logistical, technical, or regulatory constraints

Remote monitoring technologies in Alzheimer’s disease: design of the RADAR-AD study

Background Functional decline in Alzheimer’s disease (AD) is typically measured using single-time point subjective rating scales, which rely on direct observation or (caregiver) recall. Remote monitoring technologies (RMTs), such as smartphone applications, wearables, and home-based sensors, can change these periodic subjective assessments to more frequent, or even continuous, objective monitoring. The aim of the RADAR-AD study is to assess the accuracy and validity of RMTs in measuring functional decline in a real-world environment across preclinical-to-moderate stages of AD compared to standard clinical rating scales. Methods This study includes three tiers. For the main study, we will include participants (n = 220) with preclinical AD, prodromal AD, mild-to-moderate AD, and healthy controls, classified by MMSE and CDR score, from clinical sites equally distributed over 13 European countries. Participants will undergo extensive neuropsychological testing and physical examination. The RMT assessments, performed over an 8-week period, include walk tests, financial management tasks, an augmented reality game, two activity trackers, and two smartphone applications installed on the participants’ phone. In the first sub-study, fixed sensors will be installed in the homes of a representative sub-sample of 40 participants. In the second sub-study, 10 participants will stay in a smart home for 1 week. The primary outcome of this study is the difference in functional domain profiles assessed using RMTs between the four study groups. The four participant groups will be compared for each RMT outcome measure separately. Each RMT outcome will be compared to a standard clinical test which measures the same functional or cognitive domain. Finally, multivariate prediction models will be developed. Data collection and privacy are important aspects of the project, which will be managed using the RADAR-base data platform running on specifically designed biomedical research computing infrastructure. Results First results are expected to be disseminated in 2022. Conclusion Our study is well placed to evaluate the clinical utility of RMT assessments. Leveraging modern-day technology may deliver new and improved methods for accurately monitoring functional decline in all stages of AD. It is greatly anticipated that these methods could lead to objective and real-life functional endpoints with increased sensitivity to pharmacological agent signal detection