Developing a limb repositioning robotic interface for persons with severe physical disabilities

Enhancing the Social Impact of Contemporary Music with Neurotechnology

Brain–computer interfaces (BCIs), also known as brain–machine interfaces (BMIs), translate brain activity into new outputs that replace, restore, enhance, supplement or improve natural brain outputs. BCI research and development has grown rapidly for the past two decades. It is beginning to provide useful communication and control capacities to people with severe neuromuscular disabilities; and it is expanding into new areas such as neurorehabilitation that may greatly increase its clinical impact. At the same time, significant challenges remain, particularly in regard to translating laboratory advances into clinical use.

Children with severe physical disabilities are denied their fundamental right to move, restricting their development, independence, and participation in life. Brain-computer interfaces (BCIs) could enable children with complex physical needs to access power mobility (PM) devices, which could help them move safely and independently. BCIs have been studied for PM control for adults but remain unexamined in children. In this study, we explored the feasibility of BCI-enabled PM control for children with severe physical disabilities, assessing BCI performance, standard PM skills and tolerability of BCI. Patient-oriented pilot trial. Eight children with quadriplegic cerebral palsy attended two sessions where they used a simple, commercial-grade BCI system to activate a PM trainer device. Performance was assessed through controlled activation trials (holding the PM device still or activating it upon verbal and visual cueing), and basic PM skills (driving time, number of activations, stopping) were assessed through distance trials. Setup and calibration times, headset tolerability, workload, and patient/caregiver experience were also evaluated. All participants completed the study with favorable tolerability and no serious adverse events or technological challenges. Average control accuracy was 78.3 ± 12.1%, participants were more reliably able to activate (95.7 ± 11.3%) the device than hold still (62.1 ± 23.7%). Positive trends were observed between performance and prior BCI experience and age. Participants were able to drive the PM device continuously an average of 1.5 meters for 3.0 s. They were able to stop at a target 53.1 ± 23.3% of the time, with significant variability. Participants tolerated the headset well, experienced mild-to-moderate workload and setup/calibration times were found to be practical. Participants were proud of their performance and both participants and families were eager to participate in future power mobility sessions. BCI-enabled PM access appears feasible in disabled children based on evaluations of performance, tolerability, workload, and setup/calibration. Performance was comparable to existing pediatric BCI literature and surpasses established cut-off thresholds (70%) of "effective" BCI use. Participants exhibited PM skills that would categorize them as "emerging operational learners." Continued exploration of BCI-enabled PM for children with severe physical disabilities is justified.

Brain computer interface (BCI) technology is a communication and control approach. Up to now many studies have attempted to develop an EEG-based BCI system to improve the quality of life of people with severe disabilities, such as amyotrophic lateral sclerosis (ALS), paralysis, brain stroke and so on. The proposed BCIBSHS could help to provide a new way for supporting life of paralyzed people and elderly people. The goal of this paper is to explore how to set up a cost-effective and safe-to-use online BCIBSHS to recognize multi-commands and control smart devices based on SSVEP. The portable EEG acquisition device (Emotiv EPOC) was used to collect EEG signals. The raw signals were denoised by discrete wavelet transform (DWT) method, and then the canonical correlation analysis (CCA) method was used for feature extraction and classification. Another part is the control of smart home devices. The classification results of SSVEP can be translated into commands to control several devices for the smart home. Here, the Power over Ethernet (PoE) technology was utilized to provide electrical energy and communication for those devices. During online experiments, four different control commands have been achieved to control four smart home devices (lamp, web camera, guardianship telephone and intelligent blinds). Experimental results showed that the online BCIBSHS obtained 86.88 ± 5.30% average classification accuracy rate. The BCI and PoE technology, combined with smart home system, overcoming the shortcomings of traditional systems and achieving home applications management rely on EEG signal. In this paper, we proposed an online steady-state visual evoked potential (SSVEP) based BCI system on controlling several smart home devices.

To assess—from a qualitative perspective—the perceptions and attitudes of Spanish rehabilitation professionals (e.g. rehabilitation doctors, speech therapists, physical therapists) about Brain–Computer Interface (BCI) technology. A qualitative, exploratory and descriptive study was carried out by means of interviews and analysis of textual content with mixed generation of categories and segmentation into frequency of topics. We present the results of three in-depth interviews that were conducted with Spanish speaking individuals who had previously completed a survey as part of a larger, 3-country/language, survey on BCI perceptions. 11 out of 15 of these Spanish respondents (survey) either strongly or somewhat accept the use of BCI in rehabilitation therapy. However, the results of our three in-depth interviews show how, due to a strong inertia of attitudes and perceptions about BCI technology, most professionals feel reluctant to use BCI technology in their daily practice (interview).

Objective. Brain–computer interface (BCI) technologies have been intensely studied to provide alternative communication tools entirely independent of neuromuscular activities. Current BCI technologies use electroencephalogram (EEG) acquisition methods that require unpleasant gel injections, impractical preparations and clean-up procedures. The next generation of BCI technologies requires practical, user-friendly, nonintrusive EEG platforms in order to facilitate the application of laboratory work in real-world settings. Approach. A capacitive electrode that does not require an electrolytic gel or direct electrode–scalp contact is a potential alternative to the conventional wet electrode in future BCI systems. We have proposed a new capacitive EEG electrode that contains a conductive polymer-sensing surface, which enhances electrode performance. This paper presents results from five subjects who exhibited visual or auditory steady-state responses according to BCI using these new capacitive electrodes. The steady-state visual evoked potential (SSVEP) spelling system and the auditory steady-state response (ASSR) binary decision system were employed. Main results. Offline tests demonstrated BCI performance high enough to be used in a BCI system (accuracy: 95.2%, ITR: 19.91 bpm for SSVEP BCI (6 s), accuracy: 82.6%, ITR: 1.48 bpm for ASSR BCI (14 s)) with the analysis time being slightly longer than that when wet electrodes were employed with the same BCI system (accuracy: 91.2%, ITR: 25.79 bpm for SSVEP BCI (4 s), accuracy: 81.3%, ITR: 1.57 bpm for ASSR BCI (12 s)). Subjects performed online BCI under the SSVEP paradigm in copy spelling mode and under the ASSR paradigm in selective attention mode with a mean information transfer rate (ITR) of 17.78 ± 2.08 and 0.7 ± 0.24 bpm, respectively. Significance. The results of these experiments demonstrate the feasibility of using our capacitive EEG electrode in BCI systems. This capacitive electrode may become a flexible and non-intrusive tool fit for various applications in the next generation of BCI technologies.

Globally, a wide range of mental disorders continues to significantly impact people’s lives, with traditional treatment methods often falling short of providing effective and systematic solutions. Brain-computer interface (BCI) technology, one of the most rapidly advancing fields, offers promising applications across various sectors of human society, including production, healthcare, and media. By combining different BCI technologies, such as EEG monitoring and Deep Brain Stimulation, medical institutions can now offer patients a broader spectrum of effective or potential treatment options. This paper provides a comprehensive review of the current application status of various representative BCI technologies in mental health treatment. It also explores the prospect of constructing an integrated treatment system for mental disorders based on BCI technology, aiming to enhance this paper understanding of BCI applications in mental health care. The study examines how different BCI technologies can be utilized at various stages of disease progression, from early diagnosis to long-term management. Additionally, it discusses the potential of BCI in improving patient outcomes, quality of life, and social integration, while addressing ethical considerations and future research directions in this rapidly evolving field.

The design and development of Brain Computer Interface (BCI) technologies for clinical applications is a steadily growing area of research. Applications of BCI technologies in rehabilitation contexts is often impeded by the cumbersome setup and computational complexity in BCI data analytics, which consequently leads to challenges in integrating these technologies in clinical contexts. This paper describes a framework for a novel BCI system designed for clinical settings in speech-language rehabilitation. It presents an overview of the technology involved, the applied context and the system design approach. Moreover, an exploratory study was conducted to understand the functional requirements of BCI systems in speech-language rehabilitation contexts of use.

Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.

Online artifact rejection, feature extraction, and pattern recognition are essential to advance the Brain Computer Interface (BCI) technology so as to be practical for real-world applications. The goals of BCI system should be a small size, rugged, lightweight, and have low power consumption to meet the requirements of wearability, portability, and durability. This study proposes and implements a moving-windowed Independent Component Analysis (ICA) on a battery-powered, miniature, embedded BCI. This study also tests the embedded BCI on simulated and real EEG signals. Experimental results indicated that the efficacy of the online ICA decomposition is comparable with that of the offline version of the same algorithm, suggesting the feasibility of ICA for online analysis of EEG in a BCI. To demonstrate the feasibility of the wearable embedded BCI, this study also implements an online spectral analysis to the resultant component activations to continuously estimate subject's task performance in near real time.

Humans interact with computers through various devices. Such interactions may not require any physical movement, thus aiding people with severe motor disabilities in communicating with external devices. The brain–computer interface (BCI) has turned into a field involving new elements for assistive and rehabilitative technologies. This systematic literature review (SLR) aims to help BCI investigator and investors to decide which devices to select or which studies to support based on the current market examination. This examination of noninvasive EEG devices is based on published BCI studies in different research areas. In this SLR, the research area of noninvasive BCIs using electroencephalography (EEG) was analyzed by examining the types of equipment used for assistive, adaptive, and rehabilitative BCIs. For this SLR, candidate studies were selected from the IEEE digital library, PubMed, Scopus, and ScienceDirect. The inclusion criteria (IC) were limited to studies focusing on applications and devices of the BCI technology. The data used herein were selected using IC and exclusion criteria to ensure quality assessment. The selected articles were divided into four main research areas: education, engineering, entertainment, and medicine. Overall, 238 papers were selected based on IC. Moreover, 28 companies were identified that developed wired and wireless equipment as means of BCI assistive technology. The findings of this review indicate that the implications of using BCIs for assistive, adaptive, and rehabilitative technologies are encouraging for people with severe motor disabilities and healthy people. With an increasing number of healthy people using BCIs, other research areas, such as the motivation of players when participating in games or the security of soldiers when observing certain areas, can be studied and collaborated using the BCI technology. However, such BCI systems must be simple (wearable), convenient (sensor fabrics and self-adjusting abilities), and inexpensive.

People with severe physical disabilities are not able of using standard robotic wheelchairs, which generally demand some motor skills, and therefore total usage of associate muscles. Robotic wheelchairs commanded by Brain-Computer Interfaces (BCIs) based on Electroencephalography (EEG) have demonstrated to be an alternative for these end-users. In general, existing robotic wheelchairs commanded by BCIs require special platforms adapted to the EEG-BCI, and end-users need to attend a long training process for safely drive these devices. But many times these potential users do not have access to training sessions; due to mobility problems or technology access restrictions. This study proposes an EEG-based BCI with customizable configuration to be used in cloud architectures for remote control of robotic wheelchairs. This research explores two types of Steady State Visual Evoked Potential (SSVEP)-based BCI by applying Canonical Correlation Analysis (CCA) and compressive sensing (CS) as a novelty, adopting in one free calibration, and other including a calibration stage. The free-calibrated SSVEP recognition approach (CS-ncCCA) using compression ratio (CR) at 60% obtained accuracy (ACC) of 85% and information transfer rate (ITR) of 102 bits per minute (bpm), whereas the calibrated BCI (CS-wcCCA) applying also CR at 62% achieved ACC of 85% and ITR of 195 bpm. As a hilghlight, the proposed BCI allows significant reduction of the transmitted file size, and improves the communication latency that may be useful in remote and Cloud robotics applications, such as for users with severe motor disabilities, to train driving safely robotic wheelchairs via IoT.

Brain-computer interface (BCI) technologies, such as deep brain stimulation and transcranial magnetic stimulation, have clear medical benefits and are largely familiar to the medical community. Meanwhile, companies are developing BCIs for personal use, to promote cognitive performance and assist with everyday tasks. On a recent episode of the ANA Investigates podcast, Dr Karen Rommelfanger, professor at Emory University and founder of the Institute of Neuroethics, discusses the neuroethics of medical and commercial applications of BCI. BCI encompasses both sensing technologies (which monitor brain activity) and stimulating devices (which alter brain pathways). Dr Rommelfanger begins the podcast by distinguishing between the two. She emphasizes that many commercially available devices tend to be wearable sensing technologies as an effort to be more easily accessible, affordable, and less invasive than clinical technology.1 At the present moment, she says we should not fall prey to popular media and sci-fi tropes about so-called “mind reading” or “mind control.” However, Dr Rommelfanger says that there are legitimate concerns around privacy with existing consumer-ready devices, both around how companies use the data they collect and whether that data are adequately secured as there are gaps in regulations, particularly around potential inferences that might be made about individuals' current or future brain function. In the podcast, Dr Rommelfanger discusses how devices especially in the clinical research realm that can sense, but also stimulate are the most advanced of BCIs; for example, brain mapping to enable communication and speech in those who have lost the ability to do so and affecting aspects of personality and emotional range with implantable electrodes for intractable depression. She makes the point later in the podcast that neuroethics can guide the development in these clinical and consumer devices as well, such as considering the inclusiveness of different ethnicities and hair types when developing the standard electroencephalogram (EEG) leads.2 She also addresses the neuroethics related to brain organoids, tiny 3-dimensional aggregates of brain cells currently used in research and development of novel treatments, some of which have been observed to produce spontaneous electrical activity that resembles the activity of brains of premature human embryos. She outlines how questions about the moral status of brain organoids may influence how organoids are handled and how tissue donors are consented for research. Dr Rommelfanger concludes the podcast by discussing the clinicians' role in helping patients understand the utility and limits of commercially available BCI technologies and advocating for greater clinician involvement in BCI development.3 She suggests that clinicians familiarize themselves with information from the Institute of Neuroethics Think and Do Tank (see Institute of Neuroethics Guidance), International Neuroethics Society, and the Dana Foundation to educate patients and fellow clinicians attempting to navigate this field in applied and practical neuroethics and related policy.4, 5 On a societal level, she points to a role for physicians in promoting access and medical reimbursement of BCI technologies. She also emphasizes the need to promote equity in the development of BCI, both to improve user experiences across diverse groups and to prevent misuse of technologies. R.D.S. conducted the primary podcast interview and wrote the first draft of the manuscript. R.D.S., A.J.C., K.S.R., and A.L.G. participated in the podcast interview and edited the manuscript. R.D.S., A.J.C., A.L.G. have nothing to disclose. K.S.R. offers consulting for policy entities, foundations, and companies related to neurotechnology and ethics strategy.

The integration of Brain-Computer Interface (BCI) technology into financial decision-making processes is a transformative approach that improves the precision and efficacy of real-time financial strategies. BCI technology is capable of facilitating enhanced and expedited assessments by directly interpreting brain impulses and providing substantial insights into cognitive patterns. This optimisation is achieved by monitoring mental and emotional processes in real time, which leads to a more comprehensive comprehension of financial preferences, decision fatigue, and risk tolerance. This article investigates the potential applications, advantages, and ethical implications of brain-computer interface (BCI) technology in financial decision-making, with a particular emphasis on its revolutionary influence on major financial institutions and ordinary investors.

Brain Computer Interface (BCI) technology is currently used for therapeutic ends but can also be used for enhancing human mental capacities. This essay focuses on non-therapeutic enhancements to human mental capacities using BCI. Being a digital tool, BCI increasingly embeds artificial intelligence. The primary focus is on Digital Direct Mental Enhancements (DDME) situations where the brain is physically connected to a computer and in particular the cognitive functions are targeted. If there is to be an impact on mental capacities, a BCI is always a breach of physical, and potentially also of mental integrity. If we entertain the idea that to have mental integrity is to attain the highest possible level of cognitive capacity, when compared with other humans, then mental enhancement per se could be a way to ensure such integrity rather than only as something that poses a threat to it. The upper limit (of our mental capacities) is at the core of any serious enhancement discussion. A better theory of “mental integrity” is needed to deeply tackle the challenges and potential opportunities posed by novel BCI and their capacity to offer humans not only restorative solutions but also true enhancements of mental capacities.