Effect of simulated hearing loss on automatic speech recognition for an android robot-patient.

The importance of simulating patient behavior for medical assessment training has grown in recent decades due to the increasing variety of simulation tools, including standardized/simulated patients, humanoid and android robot-patients. Yet, there is still a need for improvement of current android robot-patients to accurately simulate patient behavior, among which taking into account their hearing loss is of particular importance. This paper is the first to consider hearing loss simulation in an android robot-patient and its results provide valuable insights for future developments. For this purpose, an open-source dataset of audio data and audiograms from human listeners was used to simulate the effect of hearing loss on an automatic speech recognition (ASR) system. The performance of the system was evaluated in terms of both word error rate (WER) and word information preserved (WIP). Comparing different ASR models commonly used in robotics, it appears that the model size alone is insufficient to predict ASR performance in presence of simulated hearing loss. However, though absolute values of WER and WIP do not predict the intelligibility for human listeners, they do highly correlate with it and thus could be used, for example, to compare the performance of hearing aid algorithms.

Comparison of Ensemble Learning Methods for Classification in Cancer Registries.

Significant developments are currently underway in the field of cancer research, particularly in Germany, regarding cancer registration and the use of medical information systems. The use of such systems contributes significantly to quality assurance and increased efficiency in data evaluation. The growing importance of artificial intelligence (AI) in cancer research is evident as these systems integrate AI for various purposes, i.e. to assist users in data analysis. This paper uses ensemble learning to classify the graphical user interface state of the medical information system CARESS. The results show that all ensemble learning models utilized achieved good performance. In particular, the gradient boosting algorithm performed the best with an accuracy of 97%. The results represent a starting point for further development of ensemble learning in medical data analysis, with the potential for integration into various applications such as recommender systems.

Pre-Processing of Categorical Features Within Medical Analysis Systems.

The complexity of the cancer problem domain presents challenges not only to the medical analysis systems tasked with its analysis, but also to the users of such systems. While it is desirable to assist users in operating these medical analysis systems, prior groundwork is required before this can be achieved, such as recognising patterns in the way users create certain analyses within these systems. In this paper, we use machine learning algorithms to analyse user behaviour patterns and attempt to predict the next user interaction within the CARESS medical analysis system. Since an appropriate pre-processing scheme is essential for the performance of these algorithms, we propose the usage of a Natural Language Processing (NLP)- inspired approach to preserve some semantic cohesion of the mostly categorical features of these user interactions. Furthermore, we propose to use a sliding window that contains information about the latest user interactions in combination with Latent Dirichlet Allocation (LDA) to extract a latent topic from these last interactions and use it as additional input to the machine learning models. We compare this pre-processing scheme with other approaches that utilise one-hot encoding and feature hashing. The results of our experiments show that the sliding window LDA scheme is a promising solution, that performs better for our use case than the other evaluated pre-processing schemes. Overall, our results provide an important piece for further research and development in the area of assisting users in operating analysis systems in complex problem domains.

Towards comparable quality-assured Azure Kinect body tracking results in a study setting-Influence of light.

Quality assurance in research helps to ensure reliability and comparable results within a study. This includes reliable measurement equipment and data-processing. The Azure Kinect DK is a popular sensor used in studies with human subjects that tracks numerous joint positions with the Azure Kinect Body Tracking SDK. Prior experiments in literature indicate that light might influence the results of the body tracking. As similar light conditions are not always given in study protocols, the impact needs to be analyzed to ensure comparable results. We ran two experiments, one with four different light conditions and one with repeated measures of similar light conditions, and compared the results by calculating the random error of depth measurement, the mean distance error of the detected joint positions, and the distance between left and right ankle. The results showed that recordings with similar light conditions produce comparable results, with a maximum difference in the median value of mean distance error of 0.06 mm, while different light conditions result in inconsistent outcomes with a difference in the median value of mean distance error of up to 0.35 mm. Therefore, light might have an influence on the Azure Kinect and its body tracking. Especially additional infrared light appears to have a negative impact on the results. Therefore, we recommend recording various videos in a study under similar light conditions whenever possible, and avoiding additional sources of infrared light.

Comparison of professional and everyday wearable technology at different body positions in terms of recording gait perturbations.

Falls are a significant health problem in older people, so preventing them is essential. Since falls are often a consequence of improper reaction to gait disturbances, such as slips and trips, their detection is gaining attention in research. However there are no studies to date that investigated perturbation detection, using everyday wearable devices like hearing aids or smartphones at different body positions. Sixty-six study participants were perturbed on a split-belt treadmill while recording data with hearing aids, smartphones, and professional inertial measurement units (IMUs) at various positions (left/right ear, jacket pocket, shoulder bag, pants pocket, left/right foot, left/right wrist, lumbar, sternum). The data were visually inspected and median maximum cross-correlations were calculated for whole trials and different perturbation conditions. The results show that the hearing aids and IMUs perform equally in measuring acceleration data (correlation coefficient of 0.93 for the left hearing aid and 0.99 for the right hearing aid), which emphasizes the potential of utilizing sensors in hearing aids for head acceleration measurements. Additionally, the data implicate that measurement with a single hearing aid is sufficient and a second hearing aid provides no added value. Furthermore, the acceleration patterns were similar for the ear position, the jacket pocket position, and the lumbar (correlation coefficient of about 0.8) or sternal position (correlation coefficient of about 0.9). The correlations were found to be more or less independent of the type of perturbation. Data obtained from everyday wearable devices appears to represent the movements of the human body during perturbations similar to that of professional devices. The results suggest that IMUs in hearing aids and smartphones, placed at the trunk, could be well suited for an automatic detection of gait perturbations.

Nonclinical and clinical characterization of MAU868, a novel human-derived monoclonal neutralizing antibody targeting BK polyomavirus VP1.

Reactivation of BK polyomavirus (BKPyV) can cause significant kidney and bladder disease in immunocompromised patients. There are currently no effective, BKPyV-specific therapies. MAU868 is a novel, human immunoglobulin (Ig) G1 monoclonal antibody that binds the major capsid protein, VP1, of BKPyV with picomolar affinity, neutralizes infection by the 4 major BKPyV genotypes (EC50 ranging from 0.009-0.093 µg/mL; EC90 ranging from 0.102-4.160 µg/mL), and has comparable activity against variants with highly prevalent VP1 polymorphisms. No resistance-associated variants were identified in long-term selection studies, indicating a high in vitro barrier-to-resistance. The high-resolution crystal structure of MAU868 in complex with VP1 pentamer identified 3 key contact residues in VP1 (Y169, R170, and K172). A first-in-human study was conducted to assess the safety, tolerability, and pharmacokinetics of MAU868 following intravenous and subcutaneous administration to healthy adults in a randomized, placebo-controlled, double-blinded, single ascending dose design. MAU868 was safe and well-tolerated. All adverse events were grade 1 and resolved. The pharmacokinetics of MAU868 was typical of a human IgG, with dose-proportional systemic exposure and an elimination half-life ranging between 23 and 30 days. These results demonstrate the potential of MAU868 as a first-in-class therapeutic agent for the treatment or prevention of BKPyV disease.

The role of information systems in emergency department decision-making-a literature review.

OBJECTIVES: Healthcare providers employ heuristic and analytical decision-making to navigate the high-stakes environment of the emergency department (ED). Despite the increasing integration of information systems (ISs), research on their efficacy is conflicting. Drawing on related fields, we investigate how timing and mode of delivery influence IS effectiveness. Our objective is to reconcile previous contradictory findings, shedding light on optimal IS design in the ED. MATERIALS AND METHODS: We conducted a systematic review following PRISMA across PubMed, Scopus, and Web of Science. We coded the ISs' timing as heuristic or analytical, their mode of delivery as active for automatic alerts and passive when requiring user-initiated information retrieval, and their effect on process, economic, and clinical outcomes. RESULTS: Our analysis included 83 studies. During early heuristic decision-making, most active interventions were ineffective, while passive interventions generally improved outcomes. In the analytical phase, the effects were reversed. Passive interventions that facilitate information extraction consistently improved outcomes. DISCUSSION: Our findings suggest that the effectiveness of active interventions negatively correlates with the amount of information received during delivery. During early heuristic decision-making, when information overload is high, physicians are unresponsive to alerts and proactively consult passive resources. In the later analytical phases, physicians show increased receptivity to alerts due to decreased diagnostic uncertainty and information quantity. Interventions that limit information lead to positive outcomes, supporting our interpretation. CONCLUSION: We synthesize our findings into an integrated model that reveals the underlying reasons for conflicting findings from previous reviews and can guide practitioners in designing ISs in the ED.

Hammerstein-Wiener Motion Artifact Correction for Functional Near-Infrared Spectroscopy: A Novel Inertial Measurement Unit-Based Technique.

Participant movement is a major source of artifacts in functional near-infrared spectroscopy (fNIRS) experiments. Mitigating the impact of motion artifacts (MAs) is crucial to estimate brain activity robustly. Here, we suggest and evaluate a novel application of the nonlinear Hammerstein-Wiener model to estimate and mitigate MAs in fNIRS signals from direct-movement recordings through IMU sensors mounted on the participant's head (head-IMU) and the fNIRS probe (probe-IMU). To this end, we analyzed the hemodynamic responses of single-channel oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) signals from 17 participants who performed a hand tapping task with different levels of concurrent head movement. Additionally, the tapping task was performed without head movements to estimate the ground-truth brain activation. We compared the performance of our novel approach with the probe-IMU and head-IMU to eight established methods (PCA, tPCA, spline, spline Savitzky-Golay, wavelet, CBSI, RLOESS, and WCBSI) on four quality metrics: SNR, △AUC, RMSE, and R. Our proposed nonlinear Hammerstein-Wiener method achieved the best SNR increase (p < 0.001) among all methods. Visual inspection revealed that our approach mitigated MA contaminations that other techniques could not remove effectively. MA correction quality was comparable with head- and probe-IMUs.

Developing Advanced AI Ecosystems to Enhance Diagnosis and Care for Patients with Depression.

Major Depressive Disorder (MDD) has a significant impact on the daily lives of those affected. This concept paper presents a project that aims at addressing MDD challenges through innovative therapy systems. The project consists of two use cases: a multimodal neurofeedback (NFB) therapy and an AI-based virtual therapy assistant (VTA). The multimodal NFB integrates EEG and fNIRS to comprehensively assess brain function. The goal is to develop an open-source NFB toolbox for EEG-fNIRS integration, augmented by the VTA for optimized efficacy. The VTA will be able to collect behavioral data, provide personalized feedback and support MDD patients in their daily lives. This project aims to improve depression treatment by bringing together digital therapy, AI and mobile apps to potentially improve outcomes and accessibility for people living with depression.

Usability and feasibility of a tablet-based e-coach for older adults in rehabilitation units to improve nutrition and physical activity: a prospective pilot study.

BACKGROUND: For older adults (≥ 70 years), it is often challenging to maintain new nutrition and physical activity behaviours learned in rehabilitation. To minimize the risk of negative health consequences when returning home, an e-coach can be helpful. Aligning the program with an established concept such as the Transtheoretical Model of Behaviour Change (TTM) and guidance from healthcare professionals can optimize behaviour change. OBJECTIVE: This prospective single-arm pilot study aimed to assess the usability and feasibility of a nutrition and mobility e-coach for older adults during and after rehabilitation for a period of 9 weeks. In addition, we examined the change in the TTM phase as an indicator of the participant's readiness to change or the changes made. METHODS: Older adults (≥ 70 years) with nutrition deficits and/ or mobility limitations were recruited in a rehabilitation centre. Participants' phases of behaviour change in the TTM were identified by comparing current nutrition and physical activity habits via self-report with age-specific nutrition and physical activity recommendations. They received a tablet with the e-coach containing educational and interactive elements on the topics of nutrition and physical activity in older age. Participants used the e-coach and received support from healthcare professionals. The TTM phases were assessed at five times; the e-coach content was adjusted accordingly. Usability was assessed using the System Usability Scale (SUS, Score range: 0-100). Timestamps were used to evaluate how frequently participants used the e-coach: high (≥ 67% of the days), medium (66 - 33% of the days), and low (< 33% of the days). RESULTS: Approximately 140 patients were approached and n = 30 recruited. Complete data sets of n = 21 persons were analysed (38% female, mean age 79.0 ± 6.0 years). The SUS was 78.6 points, 11 participants (42%) were classified as high users, 6 (39%) as medium users and 4 (19%) as low users. After nine weeks, 15 participants (71%) achieved the physical activity recommendations (baseline: 33%, n = 7). Nutrition recommendations were achieved by 14 participants (66%) after nine weeks (baseline: 24%, n = 5). CONCLUSION: The e-coach seems to be usable and feasible for older adults. We identified some optimization potentials for our application that can be transferred to the development of comparable e-health interventions for vulnerable older adults.

Comparison of machine learning approaches for near-fall-detection with motion sensors.

Introduction: Falls are one of the most common causes of emergency hospital visits in older people. Early recognition of an increased fall risk, which can be indicated by the occurrence of near-falls, is important to initiate interventions. Methods: In a study with 87 subjects we simulated near-fall events on a perturbation treadmill and recorded them with inertial measurement units (IMU) at seven different positions. We investigated different machine learning models for the near-fall detection including support vector machines, AdaBoost, convolutional neural networks, and bidirectional long short-term memory networks. Additionally, we analyzed the influence of the sensor position on the classification results. Results: The best results showed a DeepConvLSTM with an F1 score of 0.954 (precision 0.969, recall 0.942) at the sensor position "left wrist." Discussion: Since these results were obtained in the laboratory, the next step is to evaluate the suitability of the classifiers in the field.

A system for monitoring the functional status of older adults in daily life.

Functional decline in older adults can lead to an increased need of assistance or even moving to a nursing home. Utilising home automation, power and wearable sensors, our system continuously keeps track of the functional status of older adults through monitoring their daily life and allows health care professionals to create individualised rehabilitation programmes based on the changes in the older adult's functional capacity and performance in daily life. The system uses the taxonomy of the International Classification of Functioning, Disability and Health (ICF) by the World Health Organization (WHO). It links sensor data to five ICF items from three ICF categories and measures their change over time. We collected data from 20 (pre-)frail older adults (aged [Formula: see text] 75 years) during a 10-month observational randomised pilot intervention study. The system successfully passed the first pre-clinical validation step on the real-world data of the OTAGO study. Furthermore, an initial test with a medical professional showed that the system is intuitive and can be used to design personalised rehabilitation measures. Since this research is in an early stage further clinical studies are needed to fully validate the system.

AI-assisted ethics? considerations of AI simulation for the ethical assessment and design of assistive technologies.

Current ethical debates on the use of artificial intelligence (AI) in healthcare treat AI as a product of technology in three ways. First, by assessing risks and potential benefits of currently developed AI-enabled products with ethical checklists; second, by proposing ex ante lists of ethical values seen as relevant for the design and development of assistive technology, and third, by promoting AI technology to use moral reasoning as part of the automation process. The dominance of these three perspectives in the discourse is demonstrated by a brief summary of the literature. Subsequently, we propose a fourth approach to AI, namely, as a methodological tool to assist ethical reflection. We provide a concept of an AI-simulation informed by three separate elements: 1) stochastic human behavior models based on behavioral data for simulating realistic settings, 2) qualitative empirical data on value statements regarding internal policy, and 3) visualization components that aid in understanding the impact of changes in these variables. The potential of this approach is to inform an interdisciplinary field about anticipated ethical challenges or ethical trade-offs in concrete settings and, hence, to spark a re-evaluation of design and implementation plans. This may be particularly useful for applications that deal with extremely complex values and behavior or with limitations on the communication resources of affected persons (e.g., persons with dementia care or for care of persons with cognitive impairment). Simulation does not replace ethical reflection but does allow for detailed, context-sensitive analysis during the design process and prior to implementation. Finally, we discuss the inherently quantitative methods of analysis afforded by stochastic simulations as well as the potential for ethical discussions and how simulations with AI can improve traditional forms of thought experiments and future-oriented technology assessment.

Influence of Long-Lasting Static Stretching Intervention on Functional and Morphological Parameters in the Plantar Flexors: A Randomized Controlled Trial.

ABSTRACT: Warneke, K, Keiner, M, Wohlann, T, Lohmann, LH, Schmitt, T, Hillebrecht, M, Brinkmann, A, Hein, A, Wirth, K, and Schiemann, S. Influence of long-lasting static stretching intervention on functional and morphological parameters in the plantar flexors: a randomised controlled trial. J Strength Cond Res 37(10): 1993-2001, 2023-Animal studies show that long-lasting stretching training can lead to significant hypertrophy and increases in maximal strength. Accordingly, previous human studies found significant improvements in maximal voluntary contraction (MVC), flexibility, and muscle thickness (MTh) using constant angle long-lasting stretching. It was hypothesized that long-lasting stretching with high intensity will lead to sufficient mechanical tension to induce muscle hypertrophy and maximal strength gains. This study examined muscle cross-sectional area (MCSA) using magnetic resonance imaging (MRI). Therefore, 45 well-trained subjects (f: 17, m: 28, age: 27.7 ± 3.0 years, height: 180.8 ± 4.9 cm, mass: 80.4 ± 7.2 kg) were assigned to an intervention group (IG) that stretched the plantar flexors 6 × 10 minutes per day for 6 weeks or a control group (CG). Data analysis was performed using 2-way ANOVA. There was a significant Time × Group interaction in MVC ( p < 0.001-0.019, ƞ 2 = 0.158-0.223), flexibility ( p < 0.001, ƞ 2 = 0.338-0.446), MTh ( p = 0.002-0.013, ƞ 2 = 0.125-0.172), and MCSA ( p = 0.003-0.014, ƞ 2 = 0.143-0.197). Post hoc analysis showed significant increases in MVC ( d = 0.64-0.76), flexibility ( d = 0.85-1.12), MTh ( d = 0.53-0.6), and MCSA ( d = 0.16-0.3) in IG compared with CG, thus confirming previous results in well-trained subjects. Furthermore, this study improved the quality for the morphological examination by investigating both heads of the gastrocnemius with MRI and sonography. Because stretching can be used passively, an application in rehabilitation settings seems plausible, especially if no commonly used alternatives such as strength training are applicable.

[Motivational Reasons and Perceptions about Future Participation of Older People in the Research and Development Process of Health Technologies: a Mixed Methods Study]. / Motivationsgründe und Vorstellungen über eine zukünftige Beteiligung älterer Menschen im Forschungs- und Entwicklungsprozess von Gesundheitstechnologien ­ eine Mixed Methods-Studie.

BACKGROUND: Although digital approaches for disease prevention in older people have a high potential and are being used more often, there are still inequalities in access and use. One reason could be that in technology development future users are insufficiently taken into consideration, or involved very late in the process using inappropriate methods. The aim of this work was to analyze the motivation of older people participating, and their perceptions of future participation in the research and development process of health technologies aimed at health care for older people. METHODOLOGY: Quantitative and qualitative data from one needs assessment and two evaluation studies were analyzed. The quantitative data were analyzed descriptively and the qualitative data were analyzed content-analytically with inductive-deductive category formation. RESULTS: The median age of the 103 participants (50 female) was 75 years (64-90), most of whom were interested in using technology and had prior experience of study participation. Nine categories for participation motivation were derived. A common motivation for participation was to promote and support their own health. Respondents were able to envision participation both at the beginning of the research process and at its end. In terms of technique development, different ideas were expressed, but there was a general interest in technological development. Methods that would enable exchange with others were favored most. CONCLUSIONS: Differences in motivation to participate and ideas about participation were identified. The results provide important information from the perspective of older people and complement the existing state of research.

Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation-Based Signal Improvement.

Functional near-infrared spectroscopy (fNIRS) is an optical non-invasive neuroimaging technique that allows participants to move relatively freely. However, head movements frequently cause optode movements relative to the head, leading to motion artifacts (MA) in the measured signal. Here, we propose an improved algorithmic approach for MA correction that combines wavelet and correlation-based signal improvement (WCBSI). We compare its MA correction accuracy to multiple established correction approaches (spline interpolation, spline-Savitzky-Golay filter, principal component analysis, targeted principal component analysis, robust locally weighted regression smoothing filter, wavelet filter, and correlation-based signal improvement) on real data. Therefore, we measured brain activity in 20 participants performing a hand-tapping task and simultaneously moving their head to produce MAs at different levels of severity. In order to obtain a "ground truth" brain activation, we added a condition in which only the tapping task was performed. We compared the MA correction performance among the algorithms on four predefined metrics (R, RMSE, MAPE, and ΔAUC) and ranked the performances. The suggested WCBSI algorithm was the only one exceeding average performance (p < 0.001), and it had the highest probability to be the best ranked algorithm (78.8% probability). Together, our results indicate that among all algorithms tested, our suggested WCBSI approach performed consistently favorably across all measures.

Chair squat performance as a potential predictor of nurses' physical capabilities in ergonomic patient transfers.

Muscle activation and movements performed during occupational work can lead to musculoskeletal disorders, one of the nursing profession's most significant health hazards. However, physical activity like exercise training tailored to the exposure and physical ability offers health prevention and rehabilitation. Professional nursing associations have advised squat training to promote occupational health because it strengthens lower limb and back muscles. Given that squatting is a fundamental part of many daily activities and various actions in caregiving processes, we hypothesized that chair squat performance is a potential predictor of nurses' physical capabilities to perform occupational tasks. We conducted kinetic and electromyographic assessments of 289 chair squat repetitions and compared them to ergonomic patient transfer tasks. In this task, nurses transferred a supine patient to a lateral position in a care bed using similar movement characteristics of the squat task. This cross-sectional pilot study provides initial insights into nurses' kinetic and muscle activation patterns of health-enhancing and compensational strategies. Highly asymmetric movements corresponded to distinct extremes in lower limb and spine muscle activity data-e.g., increased activity of the rectus femoris indicates increased hip flexion, including postural sway and, therefore, high torsional forces affecting the sacroiliac joints. The potential of the chair squat performance as a predictor of nurses' physical capabilities in ergonomic patient transfers was quantified by a 2 × 2 contingency table resulting in an accuracy rate of 73%.

How the Processing Mode Influences Azure Kinect Body Tracking Results.

The Azure Kinect DK is an RGB-D-camera popular in research and studies with humans. For good scientific practice, it is relevant that Azure Kinect yields consistent and reproducible results. We noticed the yielded results were inconsistent. Therefore, we examined 100 body tracking runs per processing mode provided by the Azure Kinect Body Tracking SDK on two different computers using a prerecorded video. We compared those runs with respect to spatiotemporal progression (spatial distribution of joint positions per processing mode and run), derived parameters (bone length), and differences between the computers. We found a previously undocumented converging behavior of joint positions at the start of the body tracking. Euclidean distances of joint positions varied clinically relevantly with up to 87 mm between runs for CUDA and TensorRT; CPU and DirectML had no differences on the same computer. Additionally, we found noticeable differences between two computers. Therefore, we recommend choosing the processing mode carefully, reporting the processing mode, and performing all analyses on the same computer to ensure reproducible results when using Azure Kinect and its body tracking in research. Consequently, results from previous studies with Azure Kinect should be reevaluated, and until then, their findings should be interpreted with caution.

Attempting to counteract vigilance decrement in older adults with brain stimulation.

Introduction: Against the background of demographic change and the need for enhancement techniques for an aging society, we set out to repeat a study that utilized 40-Hz transcranial alternating current stimulation (tACS) to counteract the slowdown of reaction times in a vigilance experiment but with participants aged 65 years and older. On an oscillatory level, vigilance decrement is linked to rising occipital alpha power, which has been shown to be downregulated using gamma-tACS. Method: We applied tACS on the visual cortex and compared reaction times, error rates, and alpha power of a group stimulated with 40 Hz to a sham and a 5-Hz-stimulated control group. All groups executed two 30-min-long blocks of a visual task and were stimulated according to group in the second block. We hypothesized that the expected increase in reaction times and alpha power would be reduced in the 40-Hz group compared to the control groups in the second block (INTERVENTION). Results: Statistical analysis with linear mixed models showed that reaction times increased significantly over time in the first block (BASELINE) with approximately 3 ms/min for the SHAM and 2 ms/min for the 5-Hz and 40-Hz groups, with no difference between the groups. The increase was less pronounced in the INTERVENTION block (1 ms/min for SHAM and 5-Hz groups, 3 ms/min for the 40-Hz group). Differences among groups in the INTERVENTION block were not significant if the 5-Hz or the 40-Hz group was used as the base group for the linear mixed model. Statistical analysis with a generalized linear mixed model showed that alpha power was significantly higher after the experiment (1.37 µV2) compared to before (1 µV2). No influence of stimulation (40 Hz, 5 Hz, or sham) could be detected. Discussion: Although the literature has shown that tACS offers potential for older adults, our results indicate that findings from general studies cannot simply be transferred to an old-aged group. We suggest adjusting stimulation parameters to the neurophysiological features expected in this group. Next to heterogeneity and cognitive fitness, the influence of motivation and medication should be considered.