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      Conformal in-ear bioelectronics for visual and auditory brain-computer interfaces

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          Abstract

          Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks and even irreversible damage to soft tissues. Here, we propose in-ear visual and auditory BCIs based on in-ear bioelectronics, named as SpiralE, which can adaptively expand and spiral along the auditory meatus under electrothermal actuation to ensure conformal contact. Participants achieve offline accuracies of 95% in 9-target steady state visual evoked potential (SSVEP) BCI classification and type target phrases successfully in a calibration-free 40-target online SSVEP speller experiment. Interestingly, in-ear SSVEPs exhibit significant 2 nd harmonic tendencies, indicating that in-ear sensing may be complementary for studying harmonic spatial distributions in SSVEP studies. Moreover, natural speech auditory classification accuracy can reach 84% in cocktail party experiments. The SpiralE provides innovative concepts for designing 3D flexible bioelectronics and assists the development of biomedical engineering and neural monitoring.

          Abstract

          In-ear visual and auditory brain-computer interfaces typically have issues with poor interfacial adhesion or user irritation. Here, Wang et al. presents an in-ear hollow bioelectronic device that adaptively conforms to the ear canal, under electrothermal actuation, for electroencephalogram sensing.

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          Most cited references57

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          Restoring cortical control of functional movement in a human with quadriplegia.

          Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5-C6) to the seventh cervical to first thoracic (C7-T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis.
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            Restoration of reaching and grasping in a person with tetraplegia through brain-controlled muscle stimulation: a proof-of-concept demonstration

            SUMMARY Background People with chronic tetraplegia due to high cervical spinal cord injury (SCI) can regain limb movements through coordinated electrical stimulation of peripheral muscles and nerves, known as Functional Electrical Stimulation (FES). Users typically command FES systems through other preserved, but limited and unrelated, volitional movements (e.g. facial muscle activity, head movements). We demonstrate an individual with traumatic high cervical SCI performing coordinated reaching and grasping movements using his own paralyzed arm and hand, reanimated through FES, and commanded using his own cortical signals through an intracortical brain-computer-interface (iBCI). Methods The study participant (53 years old, C4, ASIA A) received two intracortical microelectrode arrays in the hand area of motor cortex, and 36 percutaneous electrodes for electrically stimulating hand, elbow, and shoulder muscles. The participant used a motorized mobile arm support for gravitational assistance and to provide humeral ab/adduction under cortical control. We assessed the participant’s ability to cortically command his paralyzed arm to perform simple single-joint arm/hand movements and functionally meaningful multi-joint movements. We compared iBCI control of his paralyzed arm to that of a virtual 3D arm. This study is registered with ClinicalTrials.gov, NCT00912041. Findings The participant successfully cortically commanded single-joint and coordinated multi-joint arm movements for point-to-point target acquisitions (80% – 100% accuracy) using first a virtual arm, and second his own arm animated by FES. Using his paralyzed arm, the participant volitionally performed self-paced reaches to drink a mug of coffee (successfully completing 11 of 12 attempts within a single session) and feed himself. Interpretation This is the first demonstration of a combined FES+iBCI neuroprosthesis for both reaching and grasping for people with SCI resulting in chronic tetraplegia, and represents a major advance, with a clear translational path, for clinically viable neuroprostheses for restoring reaching and grasping post-paralysis.
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              The Multivariate Temporal Response Function (mTRF) Toolbox: A MATLAB Toolbox for Relating Neural Signals to Continuous Stimuli

              Understanding how brains process sensory signals in natural environments is one of the key goals of twenty-first century neuroscience. While brain imaging and invasive electrophysiology will play key roles in this endeavor, there is also an important role to be played by noninvasive, macroscopic techniques with high temporal resolution such as electro- and magnetoencephalography. But challenges exist in determining how best to analyze such complex, time-varying neural responses to complex, time-varying and multivariate natural sensory stimuli. There has been a long history of applying system identification techniques to relate the firing activity of neurons to complex sensory stimuli and such techniques are now seeing increased application to EEG and MEG data. One particular example involves fitting a filter—often referred to as a temporal response function—that describes a mapping between some feature(s) of a sensory stimulus and the neural response. Here, we first briefly review the history of these system identification approaches and describe a specific technique for deriving temporal response functions known as regularized linear regression. We then introduce a new open-source toolbox for performing this analysis. We describe how it can be used to derive (multivariate) temporal response functions describing a mapping between stimulus and response in both directions. We also explain the importance of regularizing the analysis and how this regularization can be optimized for a particular dataset. We then outline specifically how the toolbox implements these analyses and provide several examples of the types of results that the toolbox can produce. Finally, we consider some of the limitations of the toolbox and opportunities for future development and application.
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                Author and article information

                Contributors
                mayinji@tsinghua.edu.cn
                gxr-dea@mail.tsinghua.edu.cn
                fengxue@tsinghua.edu.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                14 July 2023
                14 July 2023
                2023
                : 14
                : 4213
                Affiliations
                [1 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Laboratory of Flexible Electronics Technology, , Tsinghua University, ; Beijing, 100084 China
                [2 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, AML, Department of Engineering Mechanics, , Tsinghua University, ; Beijing, 100084 China
                [3 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Department of Biomedical Engineering, , Tsinghua University, ; Beijing, 100084 China
                [4 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, , Zhejiang University, ; Hangzhou, 310027 China
                [5 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Department of Otolaryngology-Head and Neck Surgery, Beijing Tongren Hospital, , Capital Medical University, ; Beijing, 100730 China
                [6 ]GRID grid.12527.33, ISNI 0000 0001 0662 3178, Institute of Flexible Electronics Technology of THU, ; Zhejiang, Jiaxing 314000 China
                [7 ]GRID grid.9227.e, ISNI 0000000119573309, Institute of Semiconductors, , Chinese Academy of Sciences, ; Beijing, 100083 China
                Author information
                http://orcid.org/0000-0002-2516-164X
                http://orcid.org/0000-0002-8114-553X
                http://orcid.org/0000-0003-0222-9717
                http://orcid.org/0000-0003-0498-7132
                http://orcid.org/0000-0003-0499-2740
                http://orcid.org/0000-0001-9242-8474
                Article
                39814
                10.1038/s41467-023-39814-6
                10349124
                37452047
                6b95aeef-b2c2-4055-b0ed-a87ffbdd63d5
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 23 December 2022
                : 28 June 2023
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 11921002
                Award ID: U20A6001
                Award Recipient :
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                © Springer Nature Limited 2023

                Uncategorized
                biomedical engineering,brain-machine interface
                Uncategorized
                biomedical engineering, brain-machine interface

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