Design and Development of Toroidal Core Winding Machine with Digital Interface

The small size and inherent superior electrical characteristics of a toroid has made it the first choice for many Original Equipment Manufacturers (OEMs). However, the lack of knowledge regarding the toroidal coil winding equipment is still hampering the growth of toroid as the first choice for transformers, inductors and other electrical applications. Additionally, due to Covid-19 pandemic and lockdown situation, small scale companies are lacking skilled manpower for the high precision task of toroidal core winding and taping. Although the machine is readily available in the market, the cost is still very high. Toroidal core winding machine is an equipment used for the purpose of winding toroidal cores which is used in various electrical machines such as current transformers, power transformers, isolation transformers, inductors and chokes, auto transformers, etc. This project aims to develop a low-cost toroidal winding machine with a user-friendly digital interface for selection of winding parameters as per the user input. The winding machine developed in this project is efficient and reliable with high-speed performance and negligible error. © 2022 IEEE.

Federated Learning for Healthcare Domain - Pipeline, Applications and Challenges

Federated learning is the process of developing machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage. This survey examines previous research and studies on federated learning in the healthcare sector across a range of use cases and applications. Our survey shows what challenges, methods, and applications a practitioner should be aware of in the topic of federated learning. This paper aims to lay out existing research and list the possibilities of federated learning for healthcare industries.© 2022 Copyright held by the owner/author(s).

Development of Personalized Sleep Induction System based on Mental States

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7% and stop auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants. © 2023 IEEE.

A case report: Upper limb recovery from stroke related to SARS-CoV-2 infection during an intervention with a brain-computer interface.

COVID-19 may increase the risk of acute ischemic stroke that can cause a loss of upper limb function, even in patients with low risk factors. However, only individual cases have been reported assessing different degrees of hospitalization outcomes. Therefore, outpatient recovery profiles during rehabilitation interventions are needed to better understand neuroplasticity mechanisms required for upper limb motor recovery. Here, we report the progression of physiological and clinical outcomes during upper limb rehabilitation of a 41-year-old patient, without any stroke risk factors, which presented a stroke on the same day as being diagnosed with COVID-19. The patient, who presented hemiparesis with incomplete motor recovery after conventional treatment, participated in a clinical trial consisting of an experimental brain-computer interface (BCI) therapy focused on upper limb rehabilitation during the chronic stage of stroke. Clinical and physiological features were measured throughout the intervention, including the Fugl-Meyer Assessment for the Upper Extremity (FMA-UE), Action Research Arm Test (ARAT), the Modified Ashworth Scale (MAS), corticospinal excitability using transcranial magnetic stimulation, cortical activity with electroencephalography, and upper limb strength. After the intervention, the patient gained 8 points and 24 points of FMA-UE and ARAT, respectively, along with a reduction of one point of MAS. In addition, grip and pinch strength doubled. Corticospinal excitability of the affected hemisphere increased while it decreased in the unaffected hemisphere. Moreover, cortical activity became more pronounced in the affected hemisphere during movement intention of the paralyzed hand. Recovery was higher compared to that reported in other BCI interventions in stroke and was due to a reengagement of the primary motor cortex of the affected hemisphere during hand motor control. This suggests that patients with stroke related to COVID-19 may benefit from a BCI intervention and highlights the possibility of a significant recovery in these patients, even in the chronic stage of stroke.

Brain-Computer Interface Approach for improving the Pedagogical Practices for Virtual Learning: A Conceptual Framework

The coronavirus epidemic (COVID19) has com-pelled the global halting of various services, including educational service, resulting in a massive crisis-response movement of education institutions to online learning platforms. Therefore, teachers had to shift from the traditional face-to-face modality and quickly adapt to virtual learning to continue their education. This conceptual paper discusses a theoretical framework for mon-itoring and improving the level of interaction between students and teachers during virtual learning environments. Through this interaction, teachers can gather some essential cognitive learning behaviors of their students by collecting some biomedical signals. In this conceptual framework, we propose a theoretical end-to-end approach to support teachers in understanding the cognitive learning behaviors of their students during online learning and where face-to-face contact is not possible. This shall be enabled by monitoring the brain patterns of students during their learning, using Brain-computer interface techniques to enhance their cognitive skills and maximize their learning. This approach is also expected to underpin new pedagogical methodologies to support remote learning. © 2022 IEEE.

Review on Signal Generation for Neurofeedback

Neurofeedback (NF) is a scientific method that alters the brain states to improve mental disorders. Neurofeedback can perform through Brain-Computer Interface (BCI) which involves hardware, and software to communicate with the outside environment using the brain's thoughts. Coronavirus disease (COVID-19) has shown a substantial influence on mental health symptoms because individuals are working from home (WFH). However, A brain condition known as Mental Fatigue (MF) is induced by continuous cognitive work and lowers mental attentiveness as well as negatively affects performance. There are different approaches to address different mental states and Neurofeedback strategies to change mental states. Thus, Neurofeedback can be an Intervention technique to reduce mental fatigue and improve cognitive task performance. Furthermore, it is proven by researchers that Machine Learning Technique can successfully detect Mental Fatigue through electroencephalography (EEG). Currently, there is no BCI that integrated Mental Fatigue detection and applies Neurofeedback strategies to reduce Mental Fatigue. This review identified a neurofeedback process that includes signal acquisition, signal pre-processing, feature extraction, classification and generation of feedback signals. This review aims to develop a general architecture of mental fatigue intervention through BCI. © 2022 IEEE.

A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence.

Over the last couple of years, in the context of the COVID-19 pandemic, many healthcare issues have been exacerbated, highlighting the paramount need to provide both reliable and affordable health services to remote locations by using the latest technologies such as video conferencing, data management, the secure transfer of patient information, and efficient data analysis tools such as machine learning algorithms. In the constant struggle to offer healthcare to everyone, many modern technologies find applicability in eHealth, mHealth, telehealth or telemedicine. Through this paper, we attempt to render an overview of what different technologies are used in certain healthcare applications, ranging from remote patient monitoring in the field of cardio-oncology to analyzing EEG signals through machine learning for the prediction of seizures, focusing on the role of artificial intelligence in eHealth.

Teleintervention for users of augmentative and alternative communication (AAC) devices: A systematic review

Introduction: The COVID-19 pandemic rapidly and drastically required the shift of healthcare services from face-to- face delivery to telepractice modalities. This was a key strategy to maintain and complement healthcare services disrupted by the pandemic, revealing the need for a higher emphasis on telepractice in speech-language- hearing services. We synthesized existing evidence on the effectiveness of speech-language teleinterventions delivered via videoconferencing to users of augmentative and alternative communication (AAC) devices. Patients and methods: A systematic literature search was conducted in ten electronic databases, from inception until August 2021. Included were speech-language teleinterventions delivered by researchers and/or clinicians via videoconferencing to users of AAC systems, without restrictions on chronological age and clinical diagnosis. Quality of the included studies was appraised using the Downs and Brown's checklist, and risk of bias was assessed using the Risk Of Bias In Non-randomized Studies-of Interventions (ROBINS-I). Results: Six teleinterventions involving 25 participants met inclusion criteria. Five studies used a single-subject design and one was a cohort study. Interventions included active consultation (n = 2), functional communication training (n = 2), brain computer interface (n = 1), and both tele-and on-site intervention (n = 1). All teleinterventions reported an increase in participants' independent use of AAC systems during the training sessions compared to baseline, and an overall high satisfaction and treatment acceptability. Conclusion: Speech-language teleinterventions for users of AAC systems show a great potential of a successful method of service delivery. Future teleintervention studies with larger sample sizes and more robust methodology are strongly encouraged to allow generalization of results across different populations.

Detecting attention level as a warning sign for online classes by using EEG signals

When Online learning got popular during the COVID-19 pandemic, tracking students' in-class attention became a troublesome business. Our experiment is designed to find the possibility and reliability of using EEG signals to detect students' attention level and ultimately determine whether detecting EEG signals can help online classes. It turns out human's attention level could be determined, and such property could be used to develop certain device to help online teaching. © SPIE 2021.

Automated Generated Binaural Beats Using EEG: A User-Friendly Auditory Brain-Computer Interface

A person who is physically, mentally, and socially fit is known as a healthy person. However, only 45% of people pass the criteria of being healthy. Approximately 25% of people in India are suffering from mental disorders and are mentally unfit. The current scenario and the lockdowns have increased the rates from 17.5% in March 2020 to 25% in March 2021. Stress has always been an integral part of human life. But with the rising age of digitisation, stress coping abilities are deteriorating, and mental health issues are increasing worldwide, especially among young adults. Mental health has become a significant concern, which needs to be dealt with as soon as possible. According to a survey, many deaths during the covid resulted from anxiety, panic, and mental weakness. Technology is proliferating, but problems like Mental health and cure are still technologically handicapped. Music is known for its healing beauty. It has profound psychophysical effects and can act as a great stress reliever. Integrating music and technology in the right manner can enhance psychological and physical health, which in turn increases brain plasticity. This paper proposes a device that uses non-invasive neurotechnology in acquiring any mental state, from heightened alertness to deep rest. The device uses auditory brain stimulation and neurofeedback technology in achieving so.Binaural Beat possesses brainwave entrainment properties, making it an excellent stimulus in designing this Auditory Brain-Computer Interface. The salient feature of this device is the usage of real-time EEG neurofeedback in producing a personalised binaural beat track for the user. The binaural beat track is structured to help the user smoothly achieve a particular mental state, providing a pleasant experience. Not only that, the experience becomes more harmonious with the integration of the Fibonacci and the Golden Ratio. The device escalates the benefits of binaural beats as the real-time EEG feedback reduces the occurrence of dizziness caused by the beats. Hence, creating an effective system to improve the cognitive functioning of the user. © 2021 IEEE.

Emerging technologies for management of patients with amyotrophic lateral sclerosis: from telehealth to assistive robotics and neural interfaces.

Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease, is characterized by the degeneration of both upper and lower motor neurons, which leads to muscle weakness and subsequently paralysis. It begins subtly with focal weakness but spreads relentlessly to involve most muscles, thus proving to be effectively incurable. Typically, death due to respiratory paralysis occurs in 3-5 years. To date, it has been shown that the management of ALS patients is best achieved with a multidisciplinary approach, and with the help of emerging technologies ranging from multidisciplinary teleconsults (for monitoring the dysphagia, respiratory function, and nutritional status) to brain-computer interfaces and eye tracking for alternative augmentative communication, until robotics, it may increase effectiveness. The COVID-19 pandemic created a spasmodic need to accelerate the development and implementation of such technologies in clinical practice, to improve the daily lives of both ALS patients and caregivers. However, despite the remarkable strides that have been made in the field, there are still issues to be addressed. This review will be discussed on the eureka moment of emerging technologies for ALS, used as a blueprint not only for neurodegenerative diseases, examining the current technologies already in place or being evaluated, highlighting the pros and cons for future clinical applications.

Simultaneous Classification of Both Mental Workload and Stress Level Suitable for an Online Passive Brain-Computer Interface.

Research studies on EEG-based mental workload detection for a passive BCI generally focus on classifying cognitive states associated with the performance of tasks at different levels of difficulty, with no other aspects of the user's mental state considered. However, in real-life situations, different aspects of the user's state such as their cognitive (e.g., level of mental workload) and affective (e.g., level of stress/anxiety) states will often change simultaneously, and performance of a BCI system designed considering just one state may be unreliable. Moreover, multiple mental states may be relevant to the purposes of the BCI-for example both mental workload and stress level might be related to an aircraft pilot's risk of error-and the simultaneous prediction of states may be critical in maximizing the practical effectiveness of real-life online BCI systems. In this study we investigated the feasibility of performing simultaneous classification of mental workload and stress level in an online passive BCI. We investigated both subject-specific and cross-subject classification approaches, the latter with and without the application of a transfer learning technique to align the distributions of data from the training and test subjects. Using cross-subject classification with transfer learning in a simulated online analysis, we obtained accuracies of 77.5 ± 6.9% and 84.1 ± 5.9%, across 18 participants for mental workload and stress level detection, respectively.

Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.

The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.

Design Considerations for Long Term Non-invasive Brain Computer Interface Training With Tetraplegic CYBATHLON Pilot.

Several studies in the recent past have demonstrated how Brain Computer Interface (BCI) technology can uncover the neural mechanisms underlying various tasks and translate them into control commands. While a multitude of studies have demonstrated the theoretic potential of BCI, a point of concern is that the studies are still confined to lab settings and mostly limited to healthy, able-bodied subjects. The CYBATHLON 2020 BCI race represents an opportunity to further develop BCI design strategies for use in real-time applications with a tetraplegic end user. In this study, as part of the preparation to participate in CYBATHLON 2020 BCI race, we investigate the design aspects of BCI in relation to the choice of its components, in particular, the type of calibration paradigm and its relevance for long-term use. The end goal was to develop a user-friendly and engaging interface suited for long-term use, especially for a spinal-cord injured (SCI) patient. We compared the efficacy of conventional open-loop calibration paradigms with real-time closed-loop paradigms, using pre-trained BCI decoders. Various indicators of performance were analyzed for this study, including the resulting classification performance, game completion time, brain activation maps, and also subjective feedback from the pilot. Our results show that the closed-loop calibration paradigms with real-time feedback is more engaging for the pilot. They also show an indication of achieving better online median classification performance as compared to conventional calibration paradigms (p = 0.0008). We also observe that stronger and more localized brain activation patterns are elicited in the closed-loop paradigm in which the experiment interface closely resembled the end application. Thus, based on this longitudinal evaluation of single-subject data, we demonstrate that BCI-based calibration paradigms with active user-engagement, such as with real-time feedback, could help in achieving better user acceptability and performance.

Effect of a Brain-Computer Interface Based on Pedaling Motor Imagery on Cortical Excitability and Connectivity.

Recently, studies on cycling-based brain-computer interfaces (BCIs) have been standing out due to their potential for lower-limb recovery. In this scenario, the behaviors of the sensory motor rhythms and the brain connectivity present themselves as sources of information that can contribute to interpreting the cortical effect of these technologies. This study aims to analyze how sensory motor rhythms and cortical connectivity behave when volunteers command reactive motor imagery (MI) BCI that provides passive pedaling feedback. We studied 8 healthy subjects who performed pedaling MI to command an electroencephalography (EEG)-based BCI with a motorized pedal to receive passive movements as feedback. The EEG data were analyzed under the following four conditions: resting, MI calibration, MI online, and receiving passive pedaling (on-line phase). Most subjects produced, over the foot area, significant event-related desynchronization (ERD) patterns around Cz when performing MI and receiving passive pedaling. The sharpest decrease was found for the low beta band. The connectivity results revealed an exchange of information between the supplementary motor area (SMA) and parietal regions during MI and passive pedaling. Our findings point to the primary motor cortex activation for most participants and the connectivity between SMA and parietal regions during pedaling MI and passive pedaling.

A Perspective of International Collaboration Through Web-Based Telecommunication-Inspired by COVID-19 Crisis.

The tsunami effect of the COVID-19 pandemic is affecting many aspects of scientific activities. Multidisciplinary experimental studies with international collaborators are hindered by the closing of the national borders, logistic issues due to lockdown, quarantine restrictions, and social distancing requirements. The full impact of this crisis on science is not clear yet, but the above-mentioned issues have most certainly restrained academic research activities. Sharing innovative solutions between researchers is in high demand in this situation. The aim of this paper is to share our successful practice of using web-based communication and remote control software for real-time long-distance control of brain stimulation. This solution may guide and encourage researchers to cope with restrictions and has the potential to help expanding international collaborations by lowering travel time and costs.