Our study could provide a rabbit model to explore the mechanism of photoreceptor degeneration and therapies for the side effects after photocoagulation.As a physiological process and high-level cognitive behavior, emotion is an important subarea in neuroscience research. Emotion recognition across subjects based on brain signals has attracted much attention. Due to individual differences across subjects and the low signal-to-noise ratio of EEG signals, the performance of conventional emotion recognition methods is relatively poor. In this paper, we propose a self-organized graph neural network (SOGNN) for cross-subject EEG emotion recognition. Unlike the previous studies based on pre-constructed and fixed graph structure, the graph structure of SOGNN are dynamically constructed by self-organized module for each signal. To evaluate the cross-subject EEG emotion recognition performance of our model, leave-one-subject-out experiments are conducted on two public emotion recognition datasets, SEED and SEED-IV. The SOGNN is able to achieve state-of-the-art emotion recognition performance. Moreover, we investigated the performance variances of the models with different graph construction techniques or features in different frequency bands. Furthermore, we visualized the graph structure learned by the proposed model and found that part of the structure coincided with previous neuroscience research. The experiments demonstrated the effectiveness of the proposed model for cross-subject EEG emotion recognition.Cochlear implants (CIs) have been remarkably successful at restoring speech perception for severely to profoundly deaf individuals. Despite their success, several limitations remain, particularly in CI users' ability to understand speech in noisy environments, locate sound sources, and enjoy music. A new multimodal approach has been proposed that uses haptic stimulation to provide sound information that is poorly transmitted by the implant. This augmenting of the electrical CI signal with haptic stimulation (electro-haptic stimulation; EHS) has been shown to improve speech-in-noise performance and sound localization in CI users. There is also evidence that it could enhance music perception. We review the evidence of EHS enhancement of CI listening and discuss key areas where further research is required. These include understanding the neural basis of EHS enhancement, understanding the effectiveness of EHS across different clinical populations, and the optimization of signal-processing strategies. We also discuss the significant potential for a new generation of haptic neuroprosthetic devices to aid those who cannot access hearing-assistive technology, either because of biomedical or healthcare-access issues. While significant further research and development is required, we conclude that EHS represents a promising new approach that could, in the near future, offer a non-invasive, inexpensive means of substantially improving clinical outcomes for hearing-impaired individuals.
  Partial driving automation is not always reliable and requires that drivers maintain readiness to take over control and manually operate the vehicle. Little is known about differences in drivers' arousal and cognitive demands under partial automation and how it may make it difficult for drivers to transition from automated to manual modes. This research examined whether there are differences in drivers' arousal and cognitive demands during manual versus partial automation driving.
 
  We compared arousal (using heart rate) and cognitive demands (using the root mean square of successive differences in normal heartbeats; RMSSD, and Detection Response Task; DRT) while 39 younger (
  = 28.82 years) and 32 late-middle-aged (
  = 52.72 years) participants drove four partially automated vehicles (Cadillac, Nissan Rogue, Tesla, and Volvo) on interstate highways. If compared to manual driving, drivers' arousal and cognitive demands were different under partial automation, then corresponding differences in heart rate, th limited prior experience.
 This novel study conducted on real roads with a representative sample provides important evidence of no difference in arousal and cognitive demands.  https://www.selleckchem.com/products/Rapamycin.html  Younger and late-middle-aged motorists who are new to partial automation are able to maintain arousal and cognitive demands comparable to manual driving while using the partially automated technology. Drivers who are more experienced with partially automated technology may respond differently than those with limited prior experience.The electroencephalography (EEG) is a well-established non-invasive method in neuroscientific research and clinical diagnostics. It provides a high temporal but low spatial resolution of brain activity. To gain insight about the spatial dynamics of the EEG, one has to solve the inverse problem, i.e., finding the neural sources that give rise to the recorded EEG activity. The inverse problem is ill-posed, which means that more than one configuration of neural sources can evoke one and the same distribution of EEG activity on the scalp. Artificial neural networks have been previously used successfully to find either one or two dipole sources. These approaches, however, have never solved the inverse problem in a distributed dipole model with more than two dipole sources. We present ConvDip, a novel convolutional neural network (CNN) architecture, that solves the EEG inverse problem in a distributed dipole model based on simulated EEG data. We show that (1) ConvDip learned to produce inverse solutions from a single time point of EEG data and (2) outperforms state-of-the-art methods on all focused performance measures. (3) It is more flexible when dealing with varying number of sources, produces less ghost sources and misses less real sources than the comparison methods. It produces plausible inverse solutions for real EEG recordings from human participants. (4) The trained network needs less then 40 ms for a single prediction. Our results qualify ConvDip as an efficient and easy-to-apply novel method for source localization in EEG data, with high relevance for clinical applications, e.g., in epileptology and real-time applications.