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This study provides a comprehensive overview of the materials and methods used to evaluate the effectiveness of the use of biofeedback in the treatment of aggressive episodes in children and adolescents. Aggressive episodes are common in various disorders and are associated with deficits in emotional processing and impulse control, primarily due to dysfunctions in the amygdala and prefrontal cortex (PFC). These brain regions also regulate physiological arousal, influencing heart rate and other autonomic functions even before aggression manifests. These early signals can be shown to the person (biofeedback) reinforcing therapeutic skills to enhance emotional regulation and reduce aggression. A total of 70 participants will be recruited for a randomized controlled trial (RCT). All participants will receive therapy, although only the intervention group will incorporate biofeedback. The experimental study will be split into three blocks: (1) Home Monitoring: Physiological signals will be recorded using a smartwatch, and aggressive episodes will be captured with a camera; (2) Laboratory Assessment: Participants will attend three sessions, where therapists will induce aggressive reactions, using the video clips recorded at home. Simultaneously, real-time physiological signals will be measured. These sessions will also include relaxation periods before and after the provoked outburst; (3) Therapeutic Intervention: Similar to the laboratory assessment block, therapists will induce aggressive responses in three sessions; however, in this block, participants will receive therapy. Additionally, participants who belong to the intervention group, will include biofeedack in the therapy. Biofeedback is focused on heart rate (HR), heart rate variability (HRV), and skin conductance level (SCL). The CACIA, the Stroop, and other pre- and post-experimental tests. will be used to assess the differences between the control and intervention groups. Emotions play a fundamental role in decision-making, social interactions, and mental health. Emotional dysregulation often leads to aggression, irritability, and anxiety. Showing physiological responses to patients, such as heart rate variability and skin conductance, may improve emotional awareness and regulation. This study aims to verify the effectiveness of including biofeedback in such therapy.

[This corrects the article DOI: 10.1371/journal.pone.0327361.].

Attentional biomarkers in attention deficit hyperactivity disorder are difficult to detect using only behavioural testing. We explored whether attention measured by a low-cost EEG system might be helpful to detect a possible disorder at its earliest stages. The GokEvolution application was designed to train attention and to provide a measure to identify attentional problems in children early on. Attention changes registered with NeuroSky MindWave in combination with the CARAS-R psychological test were used to characterise the attentional profiles of 52 non-ADHD and 23 ADHD children aged 7 to 12 years old. The analyses revealed that the GokEvolution was valuable in measuring attention through its use of EEG–BCI technology. The ADHD group showed lower levels of attention and more variability in brain attentional responses when compared to the control group. The application was able to map the low attention profiles of the ADHD group when compared to the control group and could distinguish between participants who completed the task and those who did not. Therefore, this system could potentially be used in clinical settings as a screening tool for early detection of attentional traits in order to prevent their development.

To determine the psychosocial impact of assistive technology(AT) based on robotics and artificial intelligence in the life of people with disabilities. The best match between any person with disabilities and its AT only can be gotten through a complete assessment and monitoring of his/her needs, abilities, priorities, difficulties and limitations. Without this analysis, it's possible that the device won't meet the individual's expectations. Therefore, it is important that any project focused on the development of innovating AT for people with disabilities includes the perspective of outcome measures as an important phase of the research. In this sense, the integration of the assessment, implementation process and outcome measures is crucial to guarantee the transferability for the project findings and to get the perspective from the final user. Pilot study, with prospective, longitudinal and analytical cohort. The study lasts from July 2020 until April 2023. The sample is formed by people with disabilities, ages from 2-21, that will participate from the first stage of the process (initial assessment of their abilities and needs) to the final application of outcome measures instruments (with a complete implication during the test of technology). Only with the active participation of the person is possible to carry out a user-centered approach. This fact will allow us to define and generate technological solutions that really adjust to the expectations, needs and priorities of the people with disabilities, avoiding the AT from being abandoned, with the consequent health and social spending. Clinical Trials ID: NCT04723784; https://clinicaltrials.gov/.

(1) Background: Heartbeat detection from electrocardiogram (ECG) and photoplethysmograph (PPG) signals is widely used in wearable devices for health monitoring, fitness tracking, and stress assessment. While numerous methods have been proposed, their practical suitability depends not only on accuracy but also on computational and memory constraints inherent to resource-limited systems. (2) Methods: A scoping review of 52 studies published between 2017 and 2024 was conducted, covering time-domain, frequency-domain, matrix-based, and machine learning approaches. The methods were evaluated according to estimation accuracy, computational complexity, memory footprint, and suitability for on-device implementation. (3) Results: Time-domain peak detection methods consistently provide high accuracy (minimum of 79.25%, maximum of 99.96%, and median ≥99.69%) for ECG and reliable heart rate estimation for PPG with linear computational complexity, low memory requirements and low energy consumption. Frequency-domain approaches are suitable for average heart rate estimation from PPG but do not preserve inter-beat intervals (error range of [1.07, 6.4] beats per minute (BPM)). Matrix-based and machine learning methods often entail higher computational cost without proportional performance gains in wearable contexts (error range of [1.07, 6.4] BPM for PPG signals; accuracy in range of [95.4, 99.96]% for ECG). (4) Conclusions: Lightweight signal-processing techniques offer the most favorable trade-off between accuracy and efficiency for wearable implementations, whereas computationally intensive approaches are better suited for edge- or cloud-based processing.

Applications involving data acquisition from sensors need samples at a preset frequency rate, the filtering out of noise and/or analysis of certain frequency components. We propose a novel software architecture based on open-software hardware platforms which allows programmers to create data streams from input channels and easily implement filters and frequency analysis objects. The performances of the different classes given in the size of memory allocated and execution time (number of clock cycles) were analyzed in the low-cost platform Arduino Genuino. In addition, 11 people took part in an experiment in which they had to implement several exercises and complete a usability test. Sampling rates under 250 Hz (typical for many biomedical applications) makes it feasible to implement filters, sliding windows and Fourier analysis, operating in real time. Participants rated software usability at 70.2 out of 100 and the ease of use when implementing several signal processing applications was rated at just over 4.4 out of 5. Participants showed their intention of using this software because it was percieved as useful and very easy to use. The performances of the library showed that it may be appropriate for implementing small biomedical real-time applications or for human movement monitoring, even in a simple open-source hardware device like Arduino Genuino. The general perception about this library is that it is easy to use and intuitive.

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.

(1) Goals: The purpose of this study was to analyze the feasibility of using the information obtained from a one-channel electro-encephalography (EEG) signal to control a mouse pointer. We used a low-cost headset, with one dry sensor placed at the FP1 position, to steer a mouse pointer and make selections through a combination of the user’s attention level with the detection of voluntary blinks. There are two types of cursor movements: spinning and linear displacement. A sequence of blinks allows for switching between these movement types, while the attention level modulates the cursor’s speed. The influence of the attention level on performance was studied. Additionally, Fitts’ model and the evolution of the emotional states of participants, among other trajectory indicators, were analyzed. (2) Methods: Twenty participants distributed into two groups (Attention and No-Attention) performed three runs, on different days, in which 40 targets had to be reached and selected. Target positions and distances from the cursor’s initial position were chosen, providing eight different indices of difficulty (IDs). A self-assessment manikin (SAM) test and a final survey provided information about the system’s usability and the emotions of participants during the experiment. (3) Results: The performance was similar to some brain–computer interface (BCI) solutions found in the literature, with an averaged information transfer rate (ITR) of 7 bits/min. Concerning the cursor navigation, some trajectory indicators showed our proposed approach to be as good as common pointing devices, such as joysticks, trackballs, and so on. Only one of the 20 participants reported difficulty in managing the cursor and, according to the tests, most of them assessed the experience positively. Movement times and hit rates were significantly better for participants belonging to the attention group. (4) Conclusions: The proposed approach is a feasible low-cost solution to manage a mouse pointer.

This work studies the feasibility of using mental attention to access a computer. Brain activity was measured with an electrode placed at the Fp1 position and the reference on the left ear; seven normally developed people and three subjects with cerebral palsy (CP) took part in the experimentation. They were asked to keep their attention high and low for as long as possible during several trials. We recorded attention levels and power bands conveyed by the sensor, but only the first was used for feedback purposes. All of the information was statistically analyzed to find the most significant parameters and a classifier based on linear discriminant analysis (LDA) was also set up. In addition, 60% of the participants were potential users of this technology with an accuracy of over 70%. Including power bands in the classifier did not improve the accuracy in discriminating between the two attentional states. For most people, the best results were obtained by using only the attention indicator in classification. Tiredness was higher in the group with disabilities (2.7 in a scale of 3) than in the other (1.5 in the same scale); and modulating the attention to access a communication board requires that it does not contain many pictograms (between 4 and 7) on screen and has a scanning period of a relatively high t s c a n ≈ 10 s. The information transfer rate (ITR) is similar to the one obtained by other brain computer interfaces (BCI), like those based on sensorimotor rhythms (SMR) or slow cortical potentials (SCP), and makes it suitable as an eye-gaze independent BCI.

Background: Mobility plays a fundamental role in causal reasoning (causal inference or cause–effect learning), which is essential for brain development at early ages. Children naturally develop causal reasoning through interaction with their environment. Therefore, children with severe motor disabilities (GMFCS levels IV–V), who face limited opportunities for interaction, often show delays in causal reasoning. Objective: This study investigates how a wheelchair-mounted, semi-autonomous mobility platform operated via a simple switch may enhance causal learning in children with severe disabilities, compared with traditional therapies. However, due to the scarcity of participants who meet the inclusion criteria and the need for long-term evaluation, recruitment poses a significant challenge. This study aims to provide an initial assessment of the platform and collect preliminary data to estimate the required sample size and number of sessions for future studies. Methods: We conducted a pilot randomized controlled trial (RCT) to assess platform usability and its effect on reaction time and keystroke accuracy. Four children, aged 8.5 ± 2.38, participated in seven 30 min sessions. They were randomly assigned in equal numbers, with two participants in the intervention group (using the platform) and two in the control group (receiving standard therapy). Usability was evaluated through a questionnaire completed by two therapists. Key outcome measures included the System Usability Scale (SUS), reaction time (RT), and keystroke accuracy (NIS). Results: Despite the small sample size and recruitment challenges, the data allowed for preliminary estimates of the sample size and number of sessions required for future studies. Therapists reported positive usability scores. Children using the platform showed promising trends in RT and NIS, suggesting improved engagement with cause–effect tasks. Conclusions: The findings support the feasibility and usability of the mobility platform by therapists, although some improvements should be implemented in the future. No conclusive evidence was found regarding the platform’s effectiveness on causal learning, despite a positive trend over time. This pilot study also provides valuable insights for designing larger, statistically powered trials, particularly focused on NIS.