193 research outputs found

    Biosignal‐based human–machine interfaces for assistance and rehabilitation : a survey

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    As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal‐based HMIs for assistance and rehabilitation to outline state‐of‐the‐art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full‐text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever‐growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complex-ity, so their usefulness should be carefully evaluated for the specific application

    Physiological Responses During Hybrid BNCI Control of an Upper-Limb Exoskeleton

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    When combined with assistive robotic devices, such as wearable robotics, brain/neural-computer interfaces (BNCI) have the potential to restore the capabilities of handicapped people to carry out activities of daily living. To improve applicability of such systems, workload and stress should be reduced to a minimal level. Here, we investigated the user’s physiological reactions during the exhaustive use of the interfaces of a hybrid control interface. Eleven BNCI-naive healthy volunteers participated in the experiments. All participants sat in a comfortable chair in front of a desk and wore a whole-arm exoskeleton as well as wearable devices for monitoring physiological, electroencephalographic (EEG) and electrooculographic (EoG) signals. The experimental protocol consisted of three phases: (i) Set-up, calibration and BNCI training; (ii) Familiarization phase ; and (iii) Experimental phase during which each subject had to perform EEG and EoG tasks. After completing each task, the NASA-TLX questionnaire and self-assessment manikin (SAM) were completed by the user. We found significant differences (p-value < 0.05) in heart rate variability (HRV) and skin conductance level (SCL) between participants during the use of the two different biosignal modalities (EEG, EoG) of the BNCI. This indicates that EEG control is associated with a higher level of stress (associated with a decrease in HRV) and mental work load (associated with a higher level of SCL) when compared to EoG control. In addition, HRV and SCL modulations correlated with the subject’s workload perception and emotional responses assessed through NASA-TLX questionnaires and SAM

    Past, Present, and Future of EEG-Based BCI Applications

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    An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed

    A Cloud Robotics Solution to Improve Social Assistive Robots for Active and Healthy Aging

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    Technological innovation in robotics and ICT represents an effective solution to tackle the challenge of providing social sustainable care services for the ageing population. The recent introduction of cloud technologies is opening new opportunities for the provisioning of advanced robotic services based on the cooperation of a number of connected robots, smart environments and devices improved by the huge cloud computational and storage capability. In this context, this paper aims to investigate and assess the potentialities of a cloud robotic system for the provisioning of assistive services for the promotion of active and healthy ageing. The system comprised two different smart environments, located in Italy and Sweden, where a service robot is connected to a cloud platform for the provisioning of localization based services to the users. The cloud robotic services were tested in the two realistic environments to assess the general feasibility of the solution and demonstrate the ability to provide assistive location based services in a multiple environment framework. The results confirmed the validity of the solution but also suggested a deeper investigation on the dependability of the communication technologies adopted in such kind of systems

    Neuroengineering Tools/Applications for Bidirectional Interfaces, Brain–Computer Interfaces, and Neuroprosthetic Implants – A Review of Recent Progress

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    The main focus of this review is to provide a holistic amalgamated overview of the most recent human in vivo techniques for implementing brain–computer interfaces (BCIs), bidirectional interfaces, and neuroprosthetics. Neuroengineering is providing new methods for tackling current difficulties; however neuroprosthetics have been studied for decades. Recent progresses are permitting the design of better systems with higher accuracies, repeatability, and system robustness. Bidirectional interfaces integrate recording and the relaying of information from and to the brain for the development of BCIs. The concepts of non-invasive and invasive recording of brain activity are introduced. This includes classical and innovative techniques like electroencephalography and near-infrared spectroscopy. Then the problem of gliosis and solutions for (semi-) permanent implant biocompatibility such as innovative implant coatings, materials, and shapes are discussed. Implant power and the transmission of their data through implanted pulse generators and wireless telemetry are taken into account. How sensation can be relayed back to the brain to increase integration of the neuroengineered systems with the body by methods such as micro-stimulation and transcranial magnetic stimulation are then addressed. The neuroprosthetic section discusses some of the various types and how they operate. Visual prosthetics are discussed and the three types, dependant on implant location, are examined. Auditory prosthetics, being cochlear or cortical, are then addressed. Replacement hand and limb prosthetics are then considered. These are followed by sections concentrating on the control of wheelchairs, computers and robotics directly from brain activity as recorded by non-invasive and invasive techniques

    Advanced spike sorting approaches in implantable VLSI wireless brain computer interfaces: a survey

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    Brain Computer/Machine Interfaces (BCI/BMIs) have substantial potential for enhancing the lives of disabled individuals by restoring functionalities of missing body parts or allowing paralyzed individuals to regain speech and other motor capabilities. Due to severe health hazards arising from skull incisions required for wired BCI/BMIs, scientists are focusing on developing VLSI wireless BCI implants using biomaterials. However, significant challenges, like power efficiency and implant size, persist in creating reliable and efficient wireless BCI implants. With advanced spike sorting techniques, VLSI wireless BCI implants can function within the power and size constraints while maintaining neural spike classification accuracy. This study explores advanced spike sorting techniques to overcome these hurdles and enable VLSI wireless BCI/BMI implants to transmit data efficiently and achieve high accuracy.Comment: Submitted to 37th International Conference on VLSI Design 202

    MI-BMInet: An Efficient Convolutional Neural Network for Motor Imagery Brain--Machine Interfaces with EEG Channel Selection

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    A brain--machine interface (BMI) based on motor imagery (MI) enables the control of devices using brain signals while the subject imagines performing a movement. It plays an important role in prosthesis control and motor rehabilitation and is a crucial element towards the future Internet of Minds (IoM). To improve user comfort, preserve data privacy, and reduce the system's latency, a new trend in wearable BMIs is to embed algorithms on low-power microcontroller units (MCUs) to process the electroencephalographic (EEG) data in real-time close to the sensors into the wearable device. However, most of the classification models present in the literature are too resource-demanding, making them unfit for low-power MCUs. This paper proposes an efficient convolutional neural network (CNN) for EEG-based MI classification that achieves comparable accuracy while being orders of magnitude less resource-demanding and significantly more energy-efficient than state-of-the-art (SoA) models for a long-lifetime battery operation. We propose an automatic channel selection method based on spatial filters and quantize both weights and activations to 8-bit precision to further reduce the model complexity with negligible accuracy loss. Finally, we efficiently implement and evaluate the proposed models on a parallel ultra-low power (PULP) MCU. The most energy-efficient solution consumes only 50.10 uJ with an inference runtime of 5.53 ms and an accuracy of 82.51% while using 6.4x fewer EEG channels, becoming the new SoA for embedded MI-BMI and defining a new Pareto frontier in the three-way trade-off among accuracy, resource cost, and power usage

    Brain-computer interfaces in safety and security fields: Risks and applications

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    With the recent increasing interest of researchers for Brain-Computer Interface (BCI), emerges a challenge for safety and security fields. Thus, the general objective of this research is to explore, from an engineering perspective, the trends and main research needs on the risks and applications of BCIs in safety and security fields. In addition, the specific objective is to explore the BCIs as an emerging risk. The method used consists of the sequential application of two phases. The first phase is carried out a scoping literature review. And with the second phase, the BCIs are analyzed as an emerging risk. With the first phase, thematic categories are analyzed. The categories are fatigue detection, safety control, and risk identification within the safety field. And within the security field are the categories cyberattacks and authentication. As a result, a trend is identified that considers the BCI as a source of risk and as a technology for risk prevention. Also, another trend based on the definitions and concepts of safety and security applied to BCIs is identified. Thus, “BCI safety” and “BCI security” are defined. The second phase proposes a general emerging risk framing of the BCI technology based on the qualitative results of type, level, and management strategies for emerging risk. These results define a framework for studying the safety and security of BCIs. In addition, there are two challenges. Firstly, to design techniques to assess the BCI risks. Secondly, probably more critical, to define the tolerability criteria of individual and social risk

    Mind Games: How Robots Can Help Regulate Brain-Computer Interfaces

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    Assistive telehealth systems for neurorehabilitation

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    Telehealth is an evolving field within the broader domain of Biomedical Engineering, specifically situated within the context of the Internet of Medical Things (IoMT). In today's society, the importance of Telehealth systems is increasingly recognized, as they enable remote patient treatment by physicians. One significant application in neurorehabilitation is Transcranial Direct Current Stimulation (tDCS), which has demonstrated its effectiveness in modulating mental function and learning over several years. Furthermore, tDCS is widely accepted as a safe approach in the field. This presentation focuses on the development of a non-invasive wearable tDCS device with integrated Internet connectivity. This IoMT device enables remote configuration of treatment parameters, such as session duration, current level, and placebo status. Clinicians can remotely access the device and define these parameters within the approved safety ranges for tDCS treatments. In addition to the wearable tDCS device, a prototype web portal is being developed to collect performance data during neurorehabilitation exercises conducted by individuals at home. This portal also facilitates remote interaction between patients and clinicians. To provide a platform-independent solution for accessing up-to-date healthcare information, a Progressive Web Application (PWA) is being developed. The PWA enables real-time communication between patients and doctors through text chat and video conferencing. The primary objective is to create a cross-platform web application with PWA features that can function effectively as a native application in various operating systems
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