scholarly journals Challenges and Opportunities for the Future of Brain-Computer Interface in Neurorehabilitation

2021 ◽  
Vol 15 ◽  
Author(s):  
Colin Simon ◽  
David A. E. Bolton ◽  
Niamh C. Kennedy ◽  
Surjo R. Soekadar ◽  
Kathy L. Ruddy
Keyword(s):  
Best Practices ◽  
Neural Mechanisms ◽  
Neural Signals ◽  
Generate Functional ◽  
Functional Movement ◽  
Computer Interfaces ◽  
Multi Stage ◽  
Challenges And Opportunities ◽  
Clinical Interest ◽  
Unique Potential

Brain-computer interfaces (BCIs) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work. Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.

scholarly journals Challenges and opportunities for the future of Brain-Computer Interface in neurorehabilitation

2021 ◽  
Author(s):  
Colin Simon ◽  
David A E Bolton ◽  
Niamh Kennedy ◽  
Surjo R. Soekadar ◽  
kathy ruddy
Keyword(s):  
Best Practices ◽  
Neural Mechanisms ◽  
Neural Signals ◽  
Generate Functional ◽  
Functional Movement ◽  
Computer Interfaces ◽  
Multi Stage ◽  
Challenges And Opportunities ◽  
Clinical Interest ◽  
Unique Potential

Brain Computer Interfaces (BCI) provide a unique technological solution to circumvent the damaged motor system. For neurorehabilitation, the BCI can be used to translate neural signals associated with movement intentions into tangible feedback for the patient, when they are unable to generate functional movement themselves. Clinical interest in BCI is growing rapidly, as it would facilitate rehabilitation to commence earlier following brain damage, and provides options for patients who are unable to partake in traditional physical therapy. However, substantial challenges with existing BCI implementations have prevented its widespread adoption. Recent advances in knowledge and technology provide opportunities to facilitate a change, provided that researchers and clinicians using BCI agree on standardisation of guidelines for protocols and shared efforts to uncover mechanisms. We propose that addressing the speed and effectiveness of learning BCI control are priorities for the field, which may be improved by multimodal or multi-stage approaches harnessing more sensitive neuroimaging technologies in the early learning stages, before transitioning to more practical, mobile implementations. Clarification of the neural mechanisms that give rise to improvement in motor function is an essential next step towards justifying clinical use of BCI. In particular, quantifying the unknown contribution of non-motor mechanisms to motor recovery calls for more stringent control conditions in experimental work.Here we provide a contemporary viewpoint on the factors impeding the scalability of BCI. Further, we provide a future outlook for optimal design of the technology to best exploit its unique potential, and best practices for research and reporting of findings.

scholarly journals Effortless Retaliation: the Neural Dynamics of Interpersonal Intentions in a Chicken Game Using Brain-Computer Interface

2021 ◽  
Author(s):  
Yiwen Wang ◽  
Yuxiao Lin ◽  
Chao Fu ◽  
Zhihua Huang ◽  
Rongjun Yu ◽  
...  
Keyword(s):  
Brain Activity ◽  
Brain Computer Interface ◽  
Neural Mechanisms ◽  
Computer Interface ◽  
Neural Dynamics ◽  
Neural Signals ◽  
Chicken Game ◽  
Common Response ◽  
Tit For Tat ◽  
Novel Method

Abstract The desire for retaliation is a common response across a majority of human societies. However, the neural mechanisms underlying aggression and retaliation remain unclear. Previous studies on social intentions are confounded by low-level response related brain activity. Using an EEG-based brain-computer interface (BCI) combined with the Chicken Game, our study examined the neural dynamics of aggression and retaliation after controlling for nonessential response related neural signals. Our results show that aggression is associated with reduced alpha event-related desynchronization (ERD), indicating reduced mental effort. Moreover, retaliation and tit-for-tat strategy use are also linked with smaller alpha-ERD. Our study provides a novel method to minimize motor confounds and demonstrates that choosing aggression and retaliation is less effortful in social conflicts.

scholarly journals YASS: Yet Another Spike Sorter

2017 ◽  
Author(s):  
JinHyung Lee ◽  
David Carlson ◽  
Hooshmand Shokri ◽  
Weichi Yao ◽  
Georges Goetz ◽  
...  
Keyword(s):  
Dirichlet Process ◽  
Large Scale ◽  
Matching Pursuit ◽  
Spike Sorting ◽  
Dirichlet Process Mixture ◽  
Electrode Arrays ◽  
Neural Signals ◽  
Model Framework ◽  
Multi Stage ◽  
Computational Bottleneck

AbstractSpike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data. This manuscript describes an efficient, reliable pipeline for spike sorting on dense multi-electrode arrays (MEAs), where neural signals appear across many electrodes and spike sorting currently represents a major computational bottleneck. We present several new techniques that make dense MEA spike sorting more robust and scalable. Our pipeline is based on an efficient multi-stage “triage-then-cluster-then-pursuit” approach that initially extracts only clean, high-quality waveforms from the electrophysiological time series by temporarily skipping noisy or “collided” events (representing two neurons firing synchronously). This is accomplished by developing a neural network detection method followed by efficient outlier triaging. The clean waveforms are then used to infer the set of neural spike waveform templates through nonparametric Bayesian clustering. Our clustering approach adapts a “coreset” approach for data reduction and uses efficient inference methods in a Dirichlet process mixture model framework to dramatically improve the scalability and reliability of the entire pipeline. The “triaged” waveforms are then finally recovered with matching-pursuit deconvolution techniques. The proposed methods improve on the state-of-the-art in terms of accuracy and stability on both real and biophysically-realistic simulated MEA data. Furthermore, the proposed pipeline is efficient, learning templates and clustering much faster than real-time for a ≃ 500-electrode dataset, using primarily a single CPU core.

Emerging Challenges and Opportunities for U.S. High-Speed Rail Development

2016 ◽  
pp. 81-123
Author(s):  
Francis P. Banko ◽  
Jackson H. Xue
Keyword(s):  
Best Practices ◽  
High Speed ◽  
Task Force ◽  
Lessons Learned ◽  
Rail Transportation ◽  
High Speed Rail ◽  
International Experiences ◽  
Challenges And Opportunities ◽  
Tier I

As we witness the advancement of U.S. high-speed rail initiatives, the country can look towards its European and Asian counterparts for best practices and lessons learned from their decades of high-speed rail design and operations. These experiences gained may be applicable towards projects such as the Texas Central Railway and the California High-Speed Rail Project. This chapter will address the events of 2009 that have brought domestic high-speed rail to the forefront of U.S. rail transportation. This includes the new FRA Tier I and proposed Tier III criteria, challenges associated with each FRA tier of operation, overseas interoperability efforts, snapshots of international experiences (from policy and technological perspectives), the holistic system-based approach to safety, ongoing efforts of the FRA Engineering Task Force, and additional challenges and opportunities moving forward.

i2Flex and the Community of Inquiry Framework

2016 ◽  
pp. 115-129
Author(s):  
Antonia Fyrigou
Keyword(s):  
Best Practices ◽  
Student Learning ◽  
Community Of Inquiry ◽  
Teaching Methodology ◽  
Need To Evaluate ◽  
Community Of Inquiry Framework ◽  
French Class ◽  
New Generation ◽  
Unique Potential

In this chapter the goal is to describe my implementation of the i2Flex in two consecutive academic years in an attempt to meet more effectively the educational needs of the new generation students. Starting with the description of the i2Flex methodology in an IB French class as a member of the pilot i2Flex faculty at ACS Athens, I will present the instructional (re-)design of my Moodle shell to reflect the new teaching methodology, and the need to evaluate this pilot year via an appropriate framework. Then, I will describe the same class in the second year, from how i2Flex was at this point implemented to how it was evaluated and what data was collected from the students. Finally, the goal is to share under the umbrella of best practices how meaningful and efficient the i2Flex is, taking in consideration the new role of the teacher in and out of class and its unique potential for student learning.

Decommissioning: taking control of your committed investment

2018 ◽  
Vol 58 (2) ◽  
pp. 739 ◽  
Author(s):  
Robin Polson
Keyword(s):  
Best Practices ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Board Members ◽  
Window Of Opportunity ◽  
Australian Government ◽  
Community Groups ◽  
Gas Industry ◽  
Challenges And Opportunities ◽  
Oil And Gas Companies

At the APPEA 2017 Conference in Perth, Bernadette Cullinane and Susan Gourvenec drew our attention to the looming challenge for Australia’s oil and gas industry in decommissioning its aging assets (Cullinane and Gourvenec 2017). While Cullinane and Gourvenec’s paper focussed on the experience challenge for the Australian industry, this paper will drill down to explore the funding and financial challenges and opportunities for decommissioning in the decades ahead. In approaching the decommissioning of their assets, oil and gas companies must consider a broad range of stakeholders, beyond their immediate shareholders and board members. As we have seen in the development of new projects, Australian Government, environmental organisations and community groups, all have increasingly significant impact. These stakeholders have been considered and managed with (at best) varying degrees of effectiveness in the recent past. This impact will continue to grow for decommissioning of existing assets. However, right now, with few decommissioning projects in play, the industry has a limited window of opportunity to set the agenda for how, when and under what kind of funding arrangements and financial structures decommissioning can take place. By getting ahead of the game and establishing best practices from the outset, the industry can demonstrate to Australian Government, environmental organisations and community groups a level of commitment and accountability that will allow us to move ahead on decommissioning, with reduced outside interference. The window of opportunity is closing. The time to act is now.

scholarly journals Physiological properties of brain-machine interface input signals

2017 ◽  
Vol 118 (2) ◽  
pp. 1329-1343 ◽  
Author(s):  
Marc W. Slutzky ◽  
Robert D. Flint
Keyword(s):  
High Performance ◽  
Physiological Characteristics ◽  
Neural Signals ◽  
Related Information ◽  
Computer Interfaces ◽  
Brain Signals ◽  
Physiological Properties ◽  
Input Signals ◽  
Machine Interface ◽  
And Performance

Brain-machine interfaces (BMIs), also called brain-computer interfaces (BCIs), decode neural signals and use them to control some type of external device. Despite many experimental successes and terrific demonstrations in animals and humans, a high-performance, clinically viable device has not yet been developed for widespread usage. There are many factors that impact clinical viability and BMI performance. Arguably, the first of these is the selection of brain signals used to control BMIs. In this review, we summarize the physiological characteristics and performance—including movement-related information, longevity, and stability—of multiple types of input signals that have been used in invasive BMIs to date. These include intracortical spikes as well as field potentials obtained inside the cortex, at the surface of the cortex (electrocorticography), and at the surface of the dura mater (epidural signals). We also discuss the potential for future enhancements in input signal performance, both by improving hardware and by leveraging the knowledge of the physiological characteristics of these signals to improve decoding and stability.

scholarly journals Spatiotemporal neural correlates of brain-computer interface learning

2018 ◽  
Author(s):  
Marie-Constance Corsi ◽  
Mario Chavez ◽  
Denis Schwartz ◽  
Nathalie George ◽  
Laurent Hugueville ◽  
...  
Keyword(s):  
Skill Acquisition ◽  
Real Life ◽  
Good Control ◽  
Neural Correlates ◽  
Neural Mechanisms ◽  
Human Beings ◽  
Related Activity ◽  
Computer Interfaces ◽  
Neural Processes ◽  
Communication Control

AbstractBrain-computer interfaces have been largely developed to allow communication, control, and neurofeedback in human beings. Despite their great potential, BCIs perform inconsistently across individuals. Moreover, the neural processes activated by training that enable humans to achieve good control remain poorly understood. In this study, we show that BCI skill acquisition is paralleled by a progressive reinforcement of task-related activity and by the reduction of connectivity between regions beyond those primarily targeted during the experiments. Notably, these patterns of activity and connectivity reflect growing automaticity and predict future BCI performance. Altogether, our findings provide new insights in the neural mechanisms underlying BCI learning, which have implications for the use of this technology in a broad range of real-life applications.

scholarly journals The Combination of Artificial Intelligence and Extended Reality: A Systematic Review

2021 ◽  
Vol 2 ◽  
Author(s):  
Dirk Reiners ◽  
Mohammad Reza Davahli ◽  
Waldemar Karwowski ◽  
Carolina Cruz-Neira
Keyword(s):  
Artificial Intelligence ◽  
Affective Computing ◽  
Medical Training ◽  
Screening Strategy ◽  
Future Perspectives ◽  
Driver Education ◽  
Multi Stage ◽  
Autonomous Cars ◽  
Challenges And Opportunities ◽  
And Training

Artificial intelligence (AI) and extended reality (XR) differ in their origin and primary objectives. However, their combination is emerging as a powerful tool for addressing prominent AI and XR challenges and opportunities for cross-development. To investigate the AI-XR combination, we mapped and analyzed published articles through a multi-stage screening strategy. We identified the main applications of the AI-XR combination, including autonomous cars, robotics, military, medical training, cancer diagnosis, entertainment, and gaming applications, advanced visualization methods, smart homes, affective computing, and driver education and training. In addition, we found that the primary motivation for developing the AI-XR applications include 1) training AI, 2) conferring intelligence on XR, and 3) interpreting XR- generated data. Finally, our results highlight the advancements and future perspectives of the AI-XR combination.

scholarly journals A Study on Decoding Models for the Reconstruction of Hand Trajectories from the Human Magnetoencephalography

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Hong Gi Yeom ◽  
Wonjun Hong ◽  
Da-Yoon Kang ◽  
Chun Kee Chung ◽  
June Sic Kim ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Neural Correlates ◽  
Machine Learning Algorithms ◽  
Linear Filter ◽  
Neural Signals ◽  
Computer Interfaces ◽  
Movement Dynamics ◽  
Point To Point ◽  
Continuous Movements ◽  
Activity Design

Decoding neural signals into control outputs has been a key to the development of brain-computer interfaces (BCIs). While many studies have identified neural correlates of kinematics or applied advanced machine learning algorithms to improve decoding performance, relatively less attention has been paid to optimal design of decoding models. For generating continuous movements from neural activity, design of decoding models should address how to incorporate movement dynamics into models and how to select a model given specific BCI objectives. Considering nonlinear and independent speed characteristics, we propose a hybrid Kalman filter to decode the hand direction and speed independently. We also investigate changes in performance of different decoding models (the linear and Kalman filters) when they predict reaching movements only or predict both reach and rest. Our offline study on human magnetoencephalography (MEG) during point-to-point arm movements shows that the performance of the linear filter or the Kalman filter is affected by including resting states for training and predicting movements. However, the hybrid Kalman filter consistently outperforms others regardless of movement states. The results demonstrate that better design of decoding models is achieved by incorporating movement dynamics into modeling or selecting a model according to decoding objectives.

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