IEEE Transactions on Neural Systems and Rehabilitation Engineering最新文献

SSVEP-based brain-computer interface (BCI) systems have received a lot of attention due to their relatively high Signal to Noise Ratio (SNR) and less training requirements. Most of the existing steady-state visual evoked potential (SSVEP) detection algorithms treat the prior probability of each alternative target being selected as equal. In this study, the prior probability distribution of alternative targets was introduced into the SSVEP recognition algorithm, and an asynchronous training-free SSVEP-BCI detection algorithm for non-equal prior probability scenarios was proposed. This algorithm is based on the Spatio-temporal equalization multi-window technique (STE-MW) and introduces the Maximum A Posteriori criterion (MAP), which makes full use of prior information to improve the performance of the asynchronous training-free BCI system. In addition, we proposed a mutual information-based performance evaluation metric called Mutual information rate (MIR) specifically for non-equal prior probability scenarios. This evaluation framework is designed to provide a more accurate estimation of the information transmission performance of BCI systems in such scenarios. A 10-target simulated vehicle control offline experiment involving 17 subjects showed that the proposed method improved the average MIR by 6.48%. Online free control experiments involving 12 subjects showed that the proposed method improved the average MIR by 14.93%, and significantly reduced the average instruction time. The proposed algorithm is more suitable for practical engineering application scenarios that are asynchronous and training-free; the extremely high accuracy is guaranteed while maintaining a low false alarm rate, which can be applied to asynchronous BCI systems that require high stability.

Dyslexia is a complex reading disorder characterized by difficulties in accurate or fluent word recognition, poor spelling, and decoding abilities. These challenges are not due to intellectual, visual, or auditory deficits. The diagnosis of dyslexia is further complicated by symptom variability, influenced by cultural and personal factors. This study leverages Virtual Reality (VR) advancements, eye movement tracking, and machine learning to create a virtual reading environment that captures eye movement data. This data extracts features such as eye movement metrics, word vectors, and saliency maps. We introduce a novel fusion model that integrates various machine learning algorithms to objectively and automatically assess dyslexia using physiological data derived from user interactions. Our findings suggest that this model significantly enhances the accuracy and efficiency of dyslexia diagnosis, marking an important advancement in educational technology and providing robust support for individuals with dyslexia. Although the sample size was limited to 10 dyslexic and 4 control participants, the results offer valuable insights and lay the groundwork for future studies with larger cohorts.

Objective: Cognitive performance state is an unobserved state that refers to the overall performance of cognitive functions. Deriving an informative observation vector as well as the adaptive model and decoder would be essential in decoding the hidden performance.

Methods: We decode the performance from behavioral observation data using the Bayesian state-space approach. Forming an observation from the paired binary response with the associated continuous reaction time may lead to an overestimation of the performance, especially when an incorrect response is accompanied by a fast reaction time. We apply the marked point process (MPP) framework such that the performance decoder takes the correct/incorrect responses and the reaction time associated with correct responses as an observation. We compare the MPP-based performance with two other decoders in which the pairs of binary and continuous signals are taken as the observation; one decoder considers an autoregressive (AR) model for the performance state, and the other one employs an autoregressive-autoregressive conditional heteroskedasticity (AR-ARCH) model which incorporates the time-varying and adaptive innovation term within the model.

Results: To evaluate decoders, we use the simulated data and the n-back experimental data in the presence of multiple music sessions.

Conclusion: The Bayesian state-space approach is a promising way to decode the performance state. With respect to individual perspective, the estimated MPP-based and ARCH-based performance states outperform the AR-based estimation. Based on the aggregated data analysis, the ARCH-based performance decoder outperforms the other decoders.

Significance: Performance decoders can be employed in educational settings and smart workplaces to monitor one's performance and contribute to developing a feedback controller in closed-loop architecture to improve cognitive performance.

Spasticity is characterized by involuntary muscle activation due to the hyperexcitability of the stretch reflex. However, the temporal pattern and spatial heterogeneity of motor unit recruitment in this activation are not fully understood. The aim of this study was to investigate changes in spatiotemporal variability in spastic muscle responses to passive stretch after stroke. We acquired high-density surface electromyography (HD-sEMG) signals from the biceps brachii in 21 stroke survivors, 10 age-matched old healthy controls, and 10 young healthy controls at velocities of 10°/s, 60°/s, 120°/s, and 180°/s. The average and distribution of fuzzy entropy extracted from HD-sEMG signals were used to characterize the spatiotemporal variability of muscle activation. Our results showed that the temporal variability of stroke survivors was significantly lower than that of old controls, while the latter was significantly lower than that of young controls. As velocity increased, only stroke survivors showed decreased activation variability and increased activation levels in specific region, and the distributions were similar across velocities. Notably, the activation variability and the size of regions with high activation levels were significantly negatively and positively correlated with the severity of spasticity, respectively. These findings provide entropy-based evidence for the contribution of greater silencing of motoneurons responsible for reflex inhibition to the synchronous recruitment of more motor units.