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PloS One 2024Current evidence supports the benefits of cochlear implants (CIs) in children with hearing loss, including those with auditory neuropathy spectrum disorder (ANSD)....
OBJECTIVES
Current evidence supports the benefits of cochlear implants (CIs) in children with hearing loss, including those with auditory neuropathy spectrum disorder (ANSD). However, there is limited evidence regarding factors that hold predictive value for intervention outcomes.
DESIGN
This retrospective case-control study consisted of 66 children with CIs, including 22 with ANSD and 44 with sensorineural hearing loss (SNHL) matched on sex, age, age at CI activation, and the length of follow-up with CIs (1:2 ratio). The case and control groups were compared in the results of five open-set speech perception tests, and a Forward Linear Regression Model was used to identify factors that can predict the post-CI outcomes.
RESULTS
There was no significant difference in average scores between the two groups across five outcome measures, ranging from 88.40% to 95.65%. The correlation matrix revealed that younger ages at hearing aid fitting and CI activation positively influenced improvements in speech perception test scores. Furthermore, among the variables incorporated in the regression model, the duration of follow-up with CIs, age at CI activation, and the utilization of two CIs demonstrated prognostic significance for improved post-CI speech perception outcomes.
CONCLUSIONS
Children with ANSD can achieve similar open-set speech perception outcomes as children with SNHL. A longer CI follow-up, a lower age at CI activation, and the use of two CIs are predictive for optimal CI outcome.
Topics: Humans; Male; Female; Cochlear Implants; Case-Control Studies; Child, Preschool; Child; Retrospective Studies; Hearing Loss, Central; Hearing Loss, Sensorineural; Speech Perception; Treatment Outcome; Cochlear Implantation; Infant; Prognosis
PubMed: 38809896
DOI: 10.1371/journal.pone.0304316 -
World Neurosurgery May 2024Brain-Computer Interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of... (Review)
Review
Brain-Computer Interfaces (BCIs), a remarkable technological advancement in neurology and neurosurgery, mark a significant leap since the inception of electroencephalography (EEG) in 1924. These interfaces effectively convert central nervous system signals into commands for external devices, offering revolutionary benefits to patients with severe communication and motor impairments due to a myriad of neurological conditions like stroke, spinal cord injuries, and neurodegenerative disorders. BCIs enable these individuals to communicate and interact with their environment, using their brain signals to operate interfaces for communication and environmental control. This technology is especially crucial for those completely locked in, providing a communication lifeline where other methods fall short. The advantages of BCIs are profound, offering autonomy and an improved quality of life for patients with severe disabilities. They allow for direct interaction with various devices and prostheses, bypassing damaged or non-functional neural pathways. However, challenges persist, including the complexity of accurately interpreting brain signals, the need for individual calibration, and ensuring reliable, long-term use. Additionally, ethical considerations arise regarding autonomy, consent, and the potential for dependence on technology. Despite these challenges, BCIs represent a transformative development in neurotechnology, promising enhanced patient outcomes and a deeper understanding of brain-machine interfaces.
PubMed: 38789029
DOI: 10.1016/j.wneu.2024.05.104 -
Journal of Neuroscience Methods May 2024Neuroprostheses are used to electrically stimulate the brain, modulate neural activity and restore sensory and motor function following injury or disease, such as...
BACKGROUND
Neuroprostheses are used to electrically stimulate the brain, modulate neural activity and restore sensory and motor function following injury or disease, such as blindness, paralysis, and other movement and psychiatric disorders. Recordings are often made simultaneously with stimulation, allowing the monitoring of neural signals and closed-loop control of devices. However, stimulation-evoked artifacts may obscure neural activity, particularly when stimulation and recording sites are nearby. Several methods have been developed to remove stimulation artifacts, but it remains challenging to validate and compare these methods because the 'ground-truth' of the neuronal signals may be contaminated by artifacts.
NEW METHOD
Here, we delivered stimulation to the visual cortex via a high-channel-count prosthesis while recording neuronal activity and stimulation artifacts. We quantified the waveforms and temporal properties of stimulation artifacts from the cortical visual prosthesis (CVP) and used them to build a dataset, in which we simulated the neuronal activity and the stimulation artifacts. We illustrate how to use the simulated data to evaluate the performance of six software-based artifact removal methods (Template subtraction, Linear interpolation, Polynomial fitting, Exponential fitting, SALPA and ERAASR) in a CVP application scenario.
RESULTS
We here focused on stimulation artifacts caused by electrical stimulation through a high-channel-count cortical prosthesis device. We find that the Polynomial fitting and Exponential fitting methods outperform the other methods in recovering spikes and multi-unit activity. Linear interpolation and Template subtraction recovered the local-field potentials.
CONCLUSION
Polynomial fitting and Exponential fitting provided a good trade-off between the quality of the recovery of spikes and multi-unit activity (MUA) and the computational complexity for a cortical prosthesis.
PubMed: 38782123
DOI: 10.1016/j.jneumeth.2024.110169 -
Journal of Medical Systems May 2024Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused...
Designing implants for large and complex cranial defects is a challenging task, even for professional designers. Current efforts on automating the design process focused mainly on convolutional neural networks (CNN), which have produced state-of-the-art results on reconstructing synthetic defects. However, existing CNN-based methods have been difficult to translate to clinical practice in cranioplasty, as their performance on large and complex cranial defects remains unsatisfactory. In this paper, we present a statistical shape model (SSM) built directly on the segmentation masks of the skulls represented as binary voxel occupancy grids and evaluate it on several cranial implant design datasets. Results show that, while CNN-based approaches outperform the SSM on synthetic defects, they are inferior to SSM when it comes to large, complex and real-world defects. Experienced neurosurgeons evaluate the implants generated by the SSM to be feasible for clinical use after minor manual corrections. Datasets and the SSM model are publicly available at https://github.com/Jianningli/ssm .
Topics: Humans; Skull; Neural Networks, Computer; Models, Statistical; Image Processing, Computer-Assisted; Plastic Surgery Procedures; Prostheses and Implants
PubMed: 38780820
DOI: 10.1007/s10916-024-02066-y -
Journal of Prosthodontic Research May 2024This study aimed to provide the latest updates on the therapeutic effectiveness of keratinized mucosa (KM) augmentation using autogenous soft tissue grafts for dental...
PURPOSE
This study aimed to provide the latest updates on the therapeutic effectiveness of keratinized mucosa (KM) augmentation using autogenous soft tissue grafts for dental implants retaining prostheses.
STUDY SELECTION
A systematic search of electronic databases was conducted on autogenous soft tissue grafts to create and/or augment KM for functioning dental implants. Two investigators independently extracted data from the selected 11 clinical studies, including 290 participants, from the initially retrieved 573 publications.
RESULTS
A lack of KM surrounding dental implants was associated with greater mucosal inflammation. A free gingival graft (FGG) was used to increase the KM width, and a connective tissue graft (CTG) was used to manage peri-implant mucosal recession (MR). The weighted mean gain in KM was 2.6 mm from the selected FGG studies, with a significant reduction in mucosal inflammation and no changes in crestal bone levels for up to 4 years. The weighted mean reduction in MR was 2 mm in selected CTG studies.
CONCLUSIONS
A lack of KM negatively affects soft tissue health around dental implants. FGG was effective in increasing KM and reducing mucosal inflammation, whereas CTG was effective in decreasing MR.
PubMed: 38777752
DOI: 10.2186/jpr.JPR_D_24_00002 -
PloS One 2024Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop...
Upper limb robotic (myoelectric) prostheses are technologically advanced, but challenging to use. In response, substantial research is being done to develop person-specific prosthesis controllers that can predict a user's intended movements. Most studies that test and compare new controllers rely on simple assessment measures such as task scores (e.g., number of objects moved across a barrier) or duration-based measures (e.g., overall task completion time). These assessment measures, however, fail to capture valuable details about: the quality of device arm movements; whether these movements match users' intentions; the timing of specific wrist and hand control functions; and users' opinions regarding overall device reliability and controller training requirements. In this work, we present a comprehensive and novel suite of myoelectric prosthesis control evaluation metrics that better facilitates analysis of device movement details-spanning measures of task performance, control characteristics, and user experience. As a case example of their use and research viability, we applied these metrics in real-time control experimentation. Here, eight participants without upper limb impairment compared device control offered by a deep learning-based controller (recurrent convolutional neural network-based classification with transfer learning, or RCNN-TL) to that of a commonly used controller (linear discriminant analysis, or LDA). The participants wore a simulated prosthesis and performed complex functional tasks across multiple limb positions. Analysis resulting from our suite of metrics identified 16 instances of a user-facing problem known as the "limb position effect". We determined that RCNN-TL performed the same as or significantly better than LDA in four such problem instances. We also confirmed that transfer learning can minimize user training burden. Overall, this study contributes a multifaceted new suite of control evaluation metrics, along with a guide to their application, for use in research and testing of myoelectric controllers today, and potentially for use in broader rehabilitation technologies of the future.
Topics: Humans; Artificial Limbs; Male; Female; Adult; Electromyography; Prosthesis Design; Upper Extremity; Robotics; Movement; Neural Networks, Computer; Young Adult; Deep Learning
PubMed: 38739557
DOI: 10.1371/journal.pone.0291279 -
Frontiers in Neuroscience 2024Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in...
Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the "move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.
PubMed: 38737097
DOI: 10.3389/fnins.2024.1329411 -
Hearing Research Jun 2024Limited auditory input, whether caused by hearing loss or by electrical stimulation through a cochlear implant (CI), can be compensated by the remaining senses....
Limited auditory input, whether caused by hearing loss or by electrical stimulation through a cochlear implant (CI), can be compensated by the remaining senses. Specifically for CI users, previous studies reported not only improved visual skills, but also altered cortical processing of unisensory visual and auditory stimuli. However, in multisensory scenarios, it is still unclear how auditory deprivation (before implantation) and electrical hearing experience (after implantation) affect cortical audiovisual speech processing. Here, we present a prospective longitudinal electroencephalography (EEG) study which systematically examined the deprivation- and CI-induced alterations of cortical processing of audiovisual words by comparing event-related potentials (ERPs) in postlingually deafened CI users before and after implantation (five weeks and six months of CI use). A group of matched normal-hearing (NH) listeners served as controls. The participants performed a word-identification task with congruent and incongruent audiovisual words, focusing their attention on either the visual (lip movement) or the auditory speech signal. This allowed us to study the (top-down) attention effect on the (bottom-up) sensory cortical processing of audiovisual speech. When compared to the NH listeners, the CI candidates (before implantation) and the CI users (after implantation) exhibited enhanced lipreading abilities and an altered cortical response at the N1 latency range (90-150 ms) that was characterized by a decreased theta oscillation power (4-8 Hz) and a smaller amplitude in the auditory cortex. After implantation, however, the auditory-cortex response gradually increased and developed a stronger intra-modal connectivity. Nevertheless, task efficiency and activation in the visual cortex was significantly modulated in both groups by focusing attention on the visual as compared to the auditory speech signal, with the NH listeners additionally showing an attention-dependent decrease in beta oscillation power (13-30 Hz). In sum, these results suggest remarkable deprivation effects on audiovisual speech processing in the auditory cortex, which partially reverse after implantation. Although even experienced CI users still show distinct audiovisual speech processing compared to NH listeners, pronounced effects of (top-down) direction of attention on (bottom-up) audiovisual processing can be observed in both groups. However, NH listeners but not CI users appear to show enhanced allocation of cognitive resources in visually as compared to auditory attended audiovisual speech conditions, which supports our behavioural observations of poorer lipreading abilities and reduced visual influence on audition in NH listeners as compared to CI users.
Topics: Humans; Cochlear Implants; Male; Speech Perception; Female; Middle Aged; Cochlear Implantation; Adult; Prospective Studies; Electroencephalography; Longitudinal Studies; Acoustic Stimulation; Persons With Hearing Impairments; Deafness; Case-Control Studies; Aged; Attention; Photic Stimulation; Visual Perception; Lipreading; Time Factors; Hearing; Evoked Potentials, Auditory; Auditory Cortex; Evoked Potentials
PubMed: 38733710
DOI: 10.1016/j.heares.2024.109023 -
Sensors (Basel, Switzerland) Apr 2024This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with...
This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows for action recognition and mapping through deep learning algorithms. Firstly, signal acquisition and processing were carried out, which involved acquiring data from various movements (hand gestures, single-degree-of-freedom joint movements, and continuous joint actions) and sensor placement. Then, interference signals were filtered out through filters, and the signals were preprocessed using normalization and moving averages to obtain sEMG signals with obvious features. Additionally, this paper constructs a hybrid network model, combining Convolutional Neural Networks and Artificial Neural Networks, and employs a multi-feature fusion algorithm to enhance the accuracy of gesture recognition. Furthermore, a nonlinear fitting between sEMG signals and joint angles was established based on a backpropagation neural network, incorporating momentum term and adaptive learning rate adjustments. Finally, based on the gesture recognition and joint angle prediction model, prosthetic arm control experiments were conducted, achieving highly accurate arm movement prediction and execution. This paper not only validates the potential application of sEMG signals in the precise control of robotic arms but also lays a solid foundation for the development of more intuitive and responsive prostheses and assistive devices.
Topics: Humans; Electromyography; Arm; Movement; Neural Networks, Computer; Algorithms; Signal Processing, Computer-Assisted; Gestures; Male; Adult
PubMed: 38732933
DOI: 10.3390/s24092827 -
JASA Express Letters May 2024Bimodal stimulation, a cochlear implant (CI) in one ear and a hearing aid (HA) in the other, provides highly asymmetrical inputs. To understand how asymmetry affects...
Bimodal stimulation, a cochlear implant (CI) in one ear and a hearing aid (HA) in the other, provides highly asymmetrical inputs. To understand how asymmetry affects perception and memory, forward and backward digit spans were measured in nine bimodal listeners. Spans were unchanged from monotic to diotic presentation; there was an average two-digit decrease for dichotic presentation with some extreme cases of decreases to zero spans. Interaurally asymmetrical decreases were not predicted based on the device or better-functioning ear. Therefore, bimodal listeners can demonstrate a strong ear dominance, diminishing memory recall dichotically even when perception was intact monaurally.
Topics: Humans; Cochlear Implants; Middle Aged; Aged; Male; Female; Dichotic Listening Tests; Adult; Auditory Perception; Hearing Aids
PubMed: 38727569
DOI: 10.1121/10.0025977