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Jonathan R Wolpaw - One of the best experts on this subject based on the ideXlab platform.

  • eeg based brain Computer Interfaces
    Current Opinion in Biomedical Engineering, 2017
    Co-Authors: Dennis J Mcfarland, Jonathan R Wolpaw
    Abstract:

    Abstract Brain–Computer Interfaces (BCIs) are real-time Computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more confortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.

  • brain Computer Interfaces
    Handbook of Clinical Neurology, 2013
    Co-Authors: Shangkai Gao, Han Yuan, Jonathan R Wolpaw
    Abstract:

    Brain–Computer Interfaces are a new technology that could help to restore useful function to people severely disabled by a wide variety of devastating neuromuscular disorders and to enhance functions in healthy individuals. The first demonstrations of brain–Computer interface (BCI) technology occurred in the 1960s when Grey Walter used the scalp-recorded electroencephalogram (EEG) to control a slide projector in 1964 [1] and when Eberhard Fetz taught monkeys to control a meter needle (and thereby earn food rewards) by changing the firing rate of a single cortical neuron [2, 3]. In the 1970s, Jacques Vidal developed a system that used the scalp-recorded visual evoked potential (VEP) over the visual cortex to determine the eye-gaze direction (i.e., the visual fixation point) in humans, and thus to determine the direction in which a person wanted to move a Computer cursor [4, 5]. At that time, Vidal coined the term “brain–Computer interface.” Since then and into the early 1990s, BCI research studies continued to appear only every few years. In 1980, Elbert et al. showed that people could learn to control slow cortical potentials (SCPs) in scalp-recorded EEG activity and could use that control to adjust the vertical position of a rocket image moving across a TV screen [6]. In 1988, Farwell and Donchin [7] reported that people could use scalp-recorded P300 event-related potentials (ERPs) to spell words on a Computer screen. Wolpaw and his colleagues trained people to control the amplitude of mu and beta rhythms (i.e., sensorimotor rhythms) in the EEG recorded over the sensorimotor cortex and showed that the subjects could use this control to move a Computer cursor rapidly and accurately in one or two dimensions [8, 9].

  • brain Computer Interfaces principles and practice
    2012
    Co-Authors: Jonathan R Wolpaw, Elizabeth Winter Wolpaw
    Abstract:

    Contributors PART I: INTRODUCTION 1. Brain-Computer Interfaces: Something New under the Sun Jonathan R. Wolpaw and Elizabeth Winter Wolpaw PART II: BRAIN SIGNALS FOR BCIs 2. Neuronal Activity in Motor Cortex and Related Areas Lee E. Miller and Nicholas Hatsopoulos 3. Electric and Magnetic Fields Produced by the Brain Paul L. Nunez 4. Signals Reflecting Brain Metabolic Activity Nick F. Ramsey PART III: BCI DESIGN, IMPLEMENTATION, AND OPERATION 5. Acquiring Brain Signals from Within the Brain Kevin Otto, Kip A. Ludwig, Daryl R. Kipke 6. Acquiring Brain Signals from Outside the Brain Ramesh Srinivasan 7. BCI Signal Processing: Feature Extraction Dean J. Krusienski, Dennis J. McFarland, and Jose C. Principe 8. BCI Signal Processing: Feature Translation Dennis J. McFarland and Dean J. Krusienski 9. BCI Hardware and Software J. Adam Wilson, Christoph Guger, and Gerwin Schalk 10. BCI Operating Protocols Steven G. Mason, Brendan Z. Allison, and Jonathan R. Wolpaw 11. BCI Applications Jane E. Huggins and Debra Zeitlin PART IV: EXISTING BCIs 12. BCIs that Use P300 Event-Related Potentials Eric W. Sellers, Yael Arbel, and Emanuel Donchin 13. BCIs that Use Sensorimotor Rhythms Gert Pfurtscheller and Dennis J. McFarland 14. BCIs that Use Steady-State Visual Evoked Potentials or Slow Cortical Potentials Brendan Z. Allison, Josef Faller, and Christa Neuper 15. BCIs that Use Electrocorticographic (ECoG) Activity Gerwin Schalk 16. BCIs that Use Signals Recorded in Motor Cortex John P. Donoghue 17. BCIs that Use Signals Recorded in Parietal or Premotor Cortex Hansjorg Scherberger 18. BCIs that Use Brain Metabolic Signals Ranganatha Sitaram, Sangkyung Lee, and Niels Birbaumer PART V: USING BCIs 19. BCI Users and Their Needs Leigh R. Hochberg and Kim D. Anderson 20. Clinical Evaluation of BCIs Theresa M. Vaughan, Eric W. Sellers, and Jonathan R. Wolpaw 21. Dissemination: Getting BCIs to the People Who Need Them Frances J.R. Richmond and Gerald E. Loeb 22. BCI Therapeutic Applications for Improving Brain Function Janis J. Daly and Ranganatha Sitaram 23. BCI Applications for the General Population Benjamin Blankertz, Michael Tangermann, and Klaus-Robert Mu?ller 24. Ethical Issues in BCI Research Mary-Jane Schneider, Joseph J. Fins, and Jonathan R. Wolpaw PART VI: CONCLUSION 25. The Future of BCIs: Meeting the Expectations Jonathan R. Wolpaw and Elizabeth Winter Wolpaw Index

Jinchang Ren - One of the best experts on this subject based on the ideXlab platform.

  • eeg based brain Computer Interfaces using motor imagery techniques and challenges
    Sensors, 2019
    Co-Authors: Natasha Padfield, Jaime Zabalza, Huimin Zhao, Valentin Masero, Jinchang Ren
    Abstract:

    Electroencephalography (EEG)-based brain-Computer Interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

Yijun Wang - One of the best experts on this subject based on the ideXlab platform.

  • an open dataset for wearable ssvep based brain Computer Interfaces
    Sensors, 2021
    Co-Authors: Fangkun Zhu, Xiaorong Gao, Lu Jiang, Guoya Dong, Yijun Wang
    Abstract:

    Brain-Computer Interfaces (BCIs) provide humans a new communication channel by encoding and decoding brain activities. Steady-state visual evoked potential (SSVEP)-based BCI stands out among many BCI paradigms because of its non-invasiveness, little user training, and high information transfer rate (ITR). However, the use of conductive gel and bulky hardware in the traditional Electroencephalogram (EEG) method hinder the application of SSVEP-based BCIs. Besides, continuous visual stimulation in long time use will lead to visual fatigue and pose a new challenge to the practical application. This study provides an open dataset, which is collected based on a wearable SSVEP-based BCI system, and comprehensively compares the SSVEP data obtained by wet and dry electrodes. The dataset consists of 8-channel EEG data from 102 healthy subjects performing a 12-target SSVEP-based BCI task. For each subject, 10 consecutive blocks were recorded using wet and dry electrodes, respectively. The dataset can be used to investigate the performance of wet and dry electrodes in SSVEP-based BCIs. Besides, the dataset provides sufficient data for developing new target identification algorithms to improve the performance of wearable SSVEP-based BCIs.

  • a dynamic window recognition algorithm for ssvep based brain Computer Interfaces using a spatio temporal equalizer
    International Journal of Neural Systems, 2018
    Co-Authors: Chen Yang, Yijun Wang, Rami Saab
    Abstract:

    The past decade has witnessed rapid development in the field of brain–Computer Interfaces (BCIs). While the performance is no longer the biggest bottleneck in the BCI application, the tedious train...

  • visual and auditory brain Computer Interfaces
    IEEE Transactions on Biomedical Engineering, 2014
    Co-Authors: Shangkai Gao, Yijun Wang, Xiaorong Gao, Bo Hong
    Abstract:

    Over the past several decades, electroencephalogram (EEG)-based brain-Computer Interfaces (BCIs) have attracted attention from researchers in the field of neuroscience, neural engineering, and clinical rehabilitation. While the performance of BCI systems has improved, they do not yet support widespread usage. Recently, visual and auditory BCI systems have become popular because of their high communication speeds, little user training, and low user variation. However, building robust and practical BCI systems from physiological and technical knowledge of neural modulation of visual and auditory brain responses remains a challenging problem. In this paper, we review the current state and future challenges of visual and auditory BCI systems. First, we describe a new taxonomy based on the multiple access methods used in telecommunication systems. Then, we discuss the challenges of translating current technology into real-life practices and outline potential avenues to address them. Specifically, this review aims to provide useful guidelines for exploring new paradigms and methodologies to improve the current visual and auditory BCI technology.

  • brain Computer Interfaces based on visual evoked potentials
    IEEE Engineering in Medicine and Biology Magazine, 2008
    Co-Authors: Yijun Wang, Xiaorong Gao, Bo Hong, Chuan Jia, Shangkai Gao
    Abstract:

    Recently, electroencephalogram (EEG)-based brain- Computer Interfaces (BCIs) have become a hot spot in the study of neural engineering, rehabilitation, and brain science. In this article, we review BCI systems based on visual evoked potentials (VEPs). Although the performance of this type of BCI has already been evaluated by many research groups through a variety of laboratory demonstrations, researchers are still facing many difficulties in changing the demonstrations to practically applicable systems. On the basis of the literature, we describe the challenges in developing practical BCI systems. Also, our recent work in the designs and implementations of the BCI systems based on steady-state VEPs (SSVEPs) is described in detail. The results show that by adequately considering the problems encountered in system design, signal processing, and parameter optimization, SSVEPs can provide the most useful information about brain activities using the least number of electrodes. At the same time, system cost could be greatly decreased and usability could be readily improved, thus benefiting the implementation of a practical BCI.

Leigh R Hochberg - One of the best experts on this subject based on the ideXlab platform.

  • brain Computer Interfaces in neurorecovery and neurorehabilitation
    Seminars in Neurology, 2021
    Co-Authors: Michael J Young, David Lin, Leigh R Hochberg
    Abstract:

    Recent advances in brain-Computer interface technology to restore and rehabilitate neurologic function aim to enable persons with disabling neurologic conditions to communicate, interact with the environment, and achieve other key activities of daily living and personal goals. Here we evaluate the principles, benefits, challenges, and future directions of brain-Computer Interfaces in the context of neurorehabilitation. We then explore the clinical translation of these technologies and propose an approach to facilitate implementation of brain-Computer Interfaces for persons with neurologic disease.

  • Power-saving design opportunities for wireless intracortical brain-Computer Interfaces.
    Nature biomedical engineering, 2020
    Co-Authors: Nir Even-chen, Leigh R Hochberg, Dante Gabriel Muratore, Sergey D. Stavisky, Jaimie M. Henderson, Boris Murmann, Krishna V. Shenoy
    Abstract:

    The efficacy of wireless intracortical brain-Computer Interfaces (iBCIs) is limited in part by the number of recording channels, which is constrained by the power budget of the implantable system. Designing wireless iBCIs that provide the high-quality recordings of today's wired neural Interfaces may lead to inadvertent over-design at the expense of power consumption and scalability. Here, we report analyses of neural signals collected from experimental iBCI measurements in rhesus macaques and from a clinical-trial participant with implanted 96-channel Utah multielectrode arrays to understand the trade-offs between signal quality and decoder performance. Moreover, we propose an efficient hardware design for clinically viable iBCIs, and suggest that the circuit design parameters of current recording iBCIs can be relaxed considerably without loss of performance. The proposed design may allow for an order-of-magnitude power savings and lead to clinically viable iBCIs with a higher channel count.

  • applications of brain Computer Interfaces to the control of robotic and prosthetic arms
    Handbook of Clinical Neurology, 2020
    Co-Authors: Marco Vilela, Leigh R Hochberg
    Abstract:

    Brain-Computer Interfaces (BCIs) have the potential to improve the quality of life of individuals with severe motor disabilities. BCIs capture the user's brain activity and translate it into commands for the control of an effector, such as a Computer cursor, robotic limb, or functional electrical stimulation device. Full dexterous manipulation of robotic and prosthetic arms via a BCI system has been a challenge because of the inherent need to decode high dimensional and preferably real-time control commands from the user's neural activity. Nevertheless, such functionality is fundamental if BCI-controlled robotic or prosthetic limbs are to be used for daily activities. In this chapter, we review how this challenge has been addressed by BCI researchers and how new solutions may improve the BCI user experience with robotic effectors.

  • review human intracortical recording and neural decoding for brain Computer Interfaces
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2017
    Co-Authors: David M Brandman, Sydney S Cash, Leigh R Hochberg
    Abstract:

    Brain–Computer Interfaces (BCIs) use neural information recorded from the brain for the voluntary control of external devices. The development of BCI systems has largely focused on improving functional independence for individuals with severe motor impairments, including providing tools for communication and mobility. In this review, we describe recent advances in intracortical BCI technology and provide potential directions for further research.

  • sensors and decoding for intracortical brain Computer Interfaces
    Annual Review of Biomedical Engineering, 2013
    Co-Authors: Mark L Homer, A V Nurmikko, John P Donoghue, Leigh R Hochberg
    Abstract:

    Intracortical brain Computer Interfaces (iBCIs) are being developed to enable people to drive an output device, such as a Computer cursor, directly from their neural activity. One goal of the technology is to help people with severe paralysis or limb loss. Key elements of an iBCI are the implanted sensor that records the neural signals and the software that decodes the user's intended movement from those signals. Here, we focus on recent advances in these two areas, placing special attention on contributions that are or may soon be adopted by the iBCI research community. We discuss how these innovations increase the technology's capability, accuracy, and longevity, all important steps that are expanding the range of possible future clinical applications.

Anatole Lecuyer - One of the best experts on this subject based on the ideXlab platform.

  • a conceptual space for eeg based brain Computer Interfaces
    PLOS ONE, 2019
    Co-Authors: Nataliya Kosmyna, Anatole Lecuyer
    Abstract:

    Brain-Computer Interfaces (BCIs) have become more and more popular these last years. Researchers use this technology for several types of applications, including attention and workload measures but also for the direct control of objects by the means of BCIs. In this work we present a first, multidimensional feature space for EEG-based BCI applications to help practitioners to characterize, compare and design systems, which use EEG-based BCIs. Our feature space contains 4 axes and 9 sub-axes and consists of 41 options in total as well as their different combinations. We presented the axes of our feature space and we positioned our feature space regarding the existing BCI and HCI taxonomies and we showed how our work integrates the past works, and/or complements them.

  • Combining Brain-Computer Interfaces and Haptics: Detecting Mental Workload to Adapt Haptic Assistance
    2012
    Co-Authors: Laurent George, Maud Marchal, Loeïz Glondu, Anatole Lecuyer
    Abstract:

    In this paper we introduce the combined use of Brain-Computer Interfaces (BCI) and Haptic Interfaces. We propose to adapt haptic guides based on the mental activity measured by a BCI system. This novel approach is illustrated within a proof-of-concept system: haptic guides are toggled during a path-following task thanks to a mental workload index provided by a BCI. The aim of this system is to provide haptic assistance only when the user's brain activity reflects a high mental workload. A user study conducted with 8 participants shows that our proof-of-concept is operational and exploitable. Results show that activation of haptic guides occurs in the most difficult part of the path-following task. Moreover it allows to increase task performance by 53% by activating assistance only 59% of the time. Taken together, these results suggest that BCI could be used to determine when the user needs assistance during haptic interaction and to enable haptic guides accordingly.

  • An overview of research on "passive" brain-Computer Interfaces for implicit human-Computer interaction
    2010
    Co-Authors: Laurent George, Anatole Lecuyer
    Abstract:

    This paper surveys existing and past research on brain-Computer Interfaces (BCI) for implicit human-Computer interaction. A novel way of using BCI has indeed emerged, which proposes to use BCI in a less explicit way : the so-called "passive" BCI. Implicit BCI or passive BCI refers to BCI in which the user does not try to control his brain activity. Thus the brain activity is assimilated to an input and can be used to adapt the application to the user's mental state. In this paper, we first study "implicit interaction" in general and recall its main applications. Then, we make a survey of existing and past research on brain-Computer Interfaces for implicit human-Computer interaction. It seems indeed that BCI can be used in many applications in an implicit way, such as for adaptive automation, affective computing, or for video games. In such applications, BCI based on implicit interaction was often reported to improve performance of either the system or the user, or to introduce novel capacities based on mental states.

  • furia an inverse solution based feature extraction algorithm using fuzzy set theory for brain Computer Interfaces
    IEEE Transactions on Signal Processing, 2009
    Co-Authors: Fabien Lotte, Anatole Lecuyer, Bruno Arnaldi
    Abstract:

    This paper presents FuRIA, a trainable feature extraction algorithm for noninvasive brain-Computer Interfaces (BCI). FuRIA is based on inverse solutions and on the new concepts of fuzzy region of interest (ROI) and fuzzy frequency band. FuRIA can automatically identify the relevant ROI and frequency bands for the discrimination of mental states, even for multiclass BCI. Once identified, the activity in these ROI and frequency bands can be used as features for any classifier. The evaluations of FuRIA showed that the extracted features were interpretable and can lead to high classification accuracies.

  • brain Computer Interfaces virtual reality and videogames
    IEEE Computer, 2008
    Co-Authors: Anatole Lecuyer, Fabien Lotte, Richard B Reilly, Robert Leeb, Michitaka Hirose, Mel Slater
    Abstract:

    Major challenges must be tackled for brain-Computer Interfaces to mature into an established communications medium for VR applications, which will range from basic neuroscience studies to developing optimal peripherals and mental gamepads and more efficient brain-signal processing techniques.