This review presents an analytical and comparative survey of upper limb prosthesis acceptance and abandonment as documented over the past 25 years, detailing areas of consumer dissatisfaction and ...ongoing technological advancements. English-language articles were identified in a search of Ovid, PubMed, and ISI Web of Science (1980 until February 2006) for key words upper limb and prosthesis. Articles focused on upper limb prostheses and addressing: (i) Factors associated with abandonment; (ii) Rejection rates; (iii) Functional analyses and patterns of wear; and (iv) Consumer satisfaction, were extracted with the exclusion of those detailing tools for outcome measurement, case studies, and medical procedures. Approximately 200 articles were included in the review process with 40 providing rates of prosthesis rejection. Quantitative measures of population characteristics, study methodology, and prostheses in use were extracted from each article. Mean rejection rates of 45% and 35% were observed in the literature for body-powered and electric prostheses respectively in pediatric populations. Significantly lower rates of rejection for both body-powered (26%) and electric (23%) devices were observed in adult populations while the average incidence of non-wear was similar for pediatric (16%) and adult (20%) populations. Documented rates of rejection exhibit a wide range of variance, possibly due to the heterogeneous samples involved and methodological differences between studies. Future research should comprise of controlled, multifactor studies adopting standardized outcome measures in order to promote comprehensive understanding of the factors affecting prosthesis use and abandonment. An enhanced understanding of these factors is needed to optimize prescription practices, guide design efforts, and satiate demand for evidence-based measures of intervention.
Changes in psychological state have been proposed as a cause of variation in brain-computer interface performance, but little formal analysis has been conducted to support this hypothesis. In this ...study, we investigated the effects of three mental states-fatigue, frustration, and attention-on BCI performance. Twelve able-bodied participants were trained to use a two-class EEG-BCI based on the performance of user-specific mental tasks. Following training, participants completed three testing sessions, during which they used the BCI to play a simple maze navigation game while periodically reporting their perceived levels of fatigue, frustration, and attention. Statistical analysis indicated that there is a significant relationship between frustration and BCI performance while the relationship between fatigue and BCI performance approached significance. BCI performance was 7% lower than average when self-reported fatigue was low and 7% higher than average when self-reported frustration was moderate. A multivariate analysis of mental state revealed the presence of contiguous regions in mental state space where BCI performance was more accurate than average, suggesting the importance of moderate fatigue for achieving effortless focus on BCI control, frustration as a potential motivating factor, and attention as a compensatory mechanism to increasing frustration. Finally, a visual analysis showed the sensitivity of underlying class distributions to changes in mental state. Collectively, these results indicate that mental state is closely related to BCI performance, encouraging future development of psychologically adaptive BCIs.
Previous brain–computer interface (BCI) research has largely focused on single neuroimaging modalities such as near-infrared spectroscopy (NIRS) or transcranial Doppler ultrasonography (TCD). ...However, multimodal brain–computer interfaces, which combine signals from different brain modalities, have been suggested as a potential means of improving the accuracy of BCI systems. In this paper, we compare the classification accuracies attainable using NIRS signals alone, TCD signals alone, and a combination of NIRS and TCD signals. Nine able-bodied subjects (mean age=25.7) were recruited and simultaneous measurements were made with NIRS and TCD instruments while participants were prompted to perform a verbal fluency task or to remain at rest, within the context of a block-stimulus paradigm. Using Linear Discriminant Analysis, the verbal fluency task was classified at mean accuracies of 76.1±9.9%, 79.4±10.3%, and 86.5±6.0% using NIRS, TCD, and NIRS–TCD systems respectively. In five of nine participants, classification accuracies with the NIRS–TCD system were significantly higher (p<0.05) than with NIRS or TCD systems alone. Our results suggest that multimodal neuroimaging may be a promising method of improving the accuracy of future brain–computer interfaces.
•A multimodal brain–computer interface (BCI) relying on NIRS and TCD is proposed.•The multimodal BCI significantly improves accuracy in some individuals.•Improvement is greatest for individuals who perform poorly with unimodal systems.•NIRS and TCD both contribute information to the multimodal classifier.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Researchers have designed training methods that can be used to improve mental health and to test the efficacy of education programs. However, few studies have demonstrated broad transfer from such ...training to performance on untrained cognitive activities. Here we report the effects of two interactive computerized training programs developed for preschool children: one for music and one for visual art. After only 20 days of training, only children in the music group exhibited enhanced performance on a measure of verbal intelligence, with 90% of the sample showing this improvement. These improvements in verbal intelligence were positively correlated with changes in functional brain plasticity during an executive-function task. Our findings demonstrate that transfer of a high-level cognitive skill is possible in early childhood.
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BFBNIB, NMLJ, NUK, OILJ, PNG, SAZU, UKNU, UL, UM, UPUK
A corrigendum on Riemannian geometry-based metrics to measure and reinforce user performance changes during brain-computer interface user training by Ivanov, N., and Chau, T. (2023). Materials and ...methods, subsection “2.1 Performance metric design”, paragraph 7. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way.
Existing brain-computer interface (BCI) software platforms are typically designed for research purposes and offer limited usability for non-technical end-users. This paper presents the Mindset ...software application, which allows end-users to train and use a BCI through a graphical user interface. The four modules of Mindset (acquisition, visualization, training, and output) collectively provide functionality to connect to various EEG hardware, visualize incoming data streams, perform user training for mental imagery and visual P300 paradigms, and facilitate real-time control of a diverse range of applications. Online experiments were conducted to characterize system performance during motor imagery and visual P300 tasks with different EEG headsets and computing hardware. With both motor imagery and visual P300 paradigms, and two different headsets and two different computer configurations, output latency was no greater than 30 ms, latency jitter below 10 ms and system clock jitter less than 17 ms. Further, the prototypical event-related potential morphology was confirmed in the visual P300 paradigm, while the expected contralateral desynchronization was observed in the motor imagery paradigm. These results demonstrate that Mindset can satisfy the real-time requirements of a BCI system and reliably capture relevant neurophysiological signals with readily available computing hardware. Mindset facilitates the translation of BCI research into clinical and practical use by improving the accessibility and usability of BCI technology.
Despite growing interest and research into brain-computer interfaces (BCI), their usage remains limited outside of research laboratories. One reason for this is BCI inefficiency, the phenomenon where ...a significant number of potential users are unable to produce machine-discernible brain signal patterns to control the devices. To reduce the prevalence of BCI inefficiency, some have advocated for novel user-training protocols that enable users to more effectively modulate their neural activity. Important considerations for the design of these protocols are the assessment measures that are used for evaluating user performance and for providing feedback that guides skill acquisition. Herein, we present three trial-wise adaptations (running, sliding window and weighted average) of Riemannian geometry-based user-performance metrics (
reflecting the degree of class separability and
reflecting the level of within-class consistency) to enable feedback to the user following each individual trial. We evaluated these metrics, along with conventional classifier feedback, using simulated and previously recorded sensorimotor rhythm-BCI data to assess their correlation with and discrimination of broader trends in user performance. Analysis revealed that the sliding window and weighted average variants of our proposed trial-wise Riemannian geometry-based metrics more accurately reflected performance changes during BCI sessions compared to conventional classifier output. The results indicate the metrics are a viable method for evaluating and tracking user performance changes during BCI-user training and, therefore, further investigation into how these metrics may be presented to users during training is warranted.
Aim
To assess the sensitivity and specificity of automated movement recognition in predicting motor impairment in high‐risk infants.
Method
We searched MEDLINE, Embase, PsycINFO, CINAHL, Web of ...Science, and Scopus databases and identified additional studies from the references of relevant studies. We included studies that evaluated automated movement recognition in high‐risk infants to predict motor impairment, including cerebral palsy (CP) and non‐CP motor impairments. Two authors independently assessed studies for inclusion, extracted data, and assessed methodological quality using the Quality Assessment of Diagnostic Accuracy Studies‐2. Meta‐analyses were performed using hierarchical summary receiver operating characteristic models.
Results
Of 6536 articles, 13 articles assessing 59 movement variables in 1248 infants under 5 months corrected age were included. Of these, 143 infants had CP. The overall sensitivity and specificity for motor impairment were 0.73 (95% confidence interval CI 0.68–0.77) and 0.70 (95% CI 0.65–0.75) respectively. Comparatively, clinical General Movements Assessment (GMA) was found to have sensitivity and specificity of 98% (95% CI 74–100) and 91% (95% CI 83–93) respectively. Sensor‐based technologies had higher specificity (0.88, 95% CI 0.80–0.93).
Interpretation
Automated movement recognition technology remains inferior to clinical GMA. The strength of this study is its meta‐analysis to summarize performance, although generalizability of these results is limited by study heterogeneity.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
The present study proposes a hybrid brain-computer interface (BCI) with 64 selectable items based on the fusion of P300 and steady-state visually evoked potential (SSVEP) brain signals. With this ...approach, row/column (RC) P300 and two-step SSVEP paradigms were integrated to create two hybrid paradigms, which we denote as the double RC (DRC) and 4-D spellers. In each hybrid paradigm, the target is simultaneously detected based on both P300 and SSVEP potentials as measured by the electroencephalogram. We further proposed a maximum-probability estimation (MPE) fusion approach to combine the P300 and SSVEP on a score level and compared this approach to other approaches based on linear discriminant analysis, a naïve Bayes classifier, and support vector machines. The experimental results obtained from thirteen participants indicated that the 4-D hybrid paradigm outperformed the DRC paradigm and that the MPE fusion achieved higher accuracy compared with the other approaches. Importantly, 12 of the 13 participants, using the 4-D paradigm achieved an accuracy of over 90% and the average accuracy was 95.18%. These promising results suggest that the proposed hybrid BCI system could be used in the design of a high-performance BCI-based keyboard.
Objective Despite their many emerging applications, practical use of brain–computer interfaces (BCIs) is often impeded by BCI-inefficiency, that is, the failure of the technology to decode neural ...modulations with sufficient accuracy. Recent evidence suggests that ineffective user training, namely feeding back to the user performance metrics that do not relate to the future performance of the BCI, may be obstructing users from learning how to produce machine-discernible sensorimotor rhythm modulations. Here, we use models of human skill acquisition to design a user-training interface to address these challenges.Approach We presented feedback via Riemannian geometry-based user performance metrics, which were validated via BCI simulation as bearing relation to future classifier performance. We subsequently evaluated the effect of the proposed feedback on users’ interpretation of their performance.Results Regression models showed that the metrics accounted for 53%–62% of intersubject variation in future classification accuracy with common BCI classifiers, thereby substantiating the use of the metric to guide user training. Participants were significantly better (p < 0.05) at detecting user performance changes with Riemannian metric-based feedback than with classifier feedback.Conclusion Our findings suggest that the proposed metrics can be effective for both assessing and communicating user performance, and therefore, warrant further investigation.
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