Basal ganglia-cortical connectivity underlies self-regulation of brain oscillations in humans

Brain-Computer Interface操作の得手不得手に関わる脳回路を発見 --操作を「考える」か「感じる」か、個人差に合わせた技術開発へ期待--. 京都大学プレスリリース. 2022-08-10.Brain-computer interfaces provide an artificial link by which the brain can directly interact with the environment. To achieve fine brain-computer interface control, participants must modulate the patterns of the cortical oscillations generated from the motor and somatosensory cortices. However, it remains unclear how humans regulate cortical oscillations, the controllability of which substantially varies across individuals. Here, we performed simultaneous electroencephalography (to assess brain-computer interface control) and functional magnetic resonance imaging (to measure brain activity) in healthy participants. Self-regulation of cortical oscillations induced activity in the basal ganglia-cortical network and the neurofeedback control network. Successful self-regulation correlated with striatal activity in the basal ganglia-cortical network, through which patterns of cortical oscillations were likely modulated. Moreover, basal ganglia-cortical network and neurofeedback control network connectivity correlated with strong and weak self-regulation, respectively. The findings indicate that the basal ganglia-cortical network is important for self-regulation, the understanding of which should help advance brain-computer interface technology

Robot Control Using Electromyography (EMG) Signals of the Wrist

The aim of this paper is to design a human–interface system, using EMG signals elicited by various wrist movements, to control a robot. EMG signals are normalized and based on joint torque. A three-layer neural network is used to estimate posture of the wrist and forearm from EMG signals. After training the neural network and obtaining appropriate weights, the subject was able to control the robot in real time using wrist and forearm movements

Class observations from the University of Hawai‘i at Mānoa highlight the need for active learning strategies to support diverse students in large classes

[EN] Compelling evidence indicates that “active learning” (learning by doing) is an effective pedagogy regardless of discipline or class size, and can be particularly effective with diverse students. This study investigated active learning practices in 64 classes at the University of Hawai‘i at Mānoa, a US university with a highly diverse student body, using a “Passivity Indicator” (PI: ratio of class time spent in passive activities to total class time). For all classes, the mean PI was 43%. Statistical analysis reveals no significant differences in the PI of classes taught in STEM vs. non-STEM disciplines, or between upper vs. lower division courses. However, the PI in larger classes was found to be significantly greater than in small classes (64% vs. 39%, respectively; p=0.02). Moreover, classroom activities aligned with an active learning standard in Language and Literacy Development (e.g., students answering questions) occurred twice as often in small (24%) vs. large classes (12%, with p=0.02). Altogether, these findings indicate an opportunity for more active learning in large classes. We present a range of research-based pedagogical strategies that can be readily implemented in large classrooms, and encourage instructors to use their implementation as research opportunities to gather data on student success.This project is supported by the US NSF‟s Research Infrastructure Improvement (RII) Track-1: „Ike Wai: Securing Hawai„i‟s Water Future‟ Award #NSF/OIA-1557349; and by Improving Undergraduate STEM Education (IUSE) Award #NSF/GEO-1565950. The research protocol was approved as exempt the UH Institutional Review Board (#2017- 003518).Engels, J.; Bruno, B.; Dasalla, N.; Böttjer-Wilson, D. (2019). Class observations from the University of Hawai‘i at Mānoa highlight the need for active learning strategies to support diverse students in large classes. En HEAD'19. 5th International Conference on Higher Education Advances. Editorial Universitat Politècnica de València. 539-547. https://doi.org/10.4995/HEAD19.2019.9547OCS53954

ICARUS: An Android-Based Unmanned Aerial Vehicle (UAV) Search and Rescue Eye in the Sky

The purpose of this paper is to develop an unmanned aerial vehicle (UAV) using a quadcopter with the capability of video surveillance, map coordinates, a deployable parachute with a medicine kit or a food pack as a payload, a collision warning system, remotely controlled, integrated with an android application to assist in search and rescue operations. Applied research for the development of the functional prototype, quantitative and descriptive statistics to summarize data by describing the relationship between variables in a sample or population. The quadcopter underwent an evaluation using a survey instrument to test its acceptability using predefined variables to select respondents within Caloocan City and Quezon City, Philippines. Demographic profiles and known issues and concerns were answered by 30 respondents. The results were summarized and distributed in Tables 1 and 2. In terms of demographic profiles, the number of SAR operators within the specified areas is distributed equally, most are male, single, and within the age bracket of 31 and above. In issues and concerns, the most common type of search and rescue was ground search and rescue. Human error is the primary cause of most injuries in operating units. The prototype was useful and everyone agreed, in terms of acceptability, drone technology will improve search and rescue operations. The innovative way of utilizing Android and drone technology is a new step towards the improvement of SAR operations in the Philippines. The LiPo battery must be replaced with a higher capacity and the drone operator should undergo a training course and secure a permit from the Civil Aviation Authority of the Philippines (CAAP).Comment: 15 pages, 14 figures, Special Issue: IRCCETE 202

CUID: a new study of perceived image quality and its subjective assessment

Research on image quality assessment (IQA) remains limited mainly due to our incomplete knowledge about human visual perception. Existing IQA algorithms have been designed or trained with insufficient subjective data with a small degree of stimulus variability. This has led to challenges for those algorithms to handle complexity and diversity of real-world digital content. Perceptual evidence from human subjects serves as a grounding for the development of advanced IQA algorithms. It is thus critical to acquire reliable subjective data with controlled perception experiments that faithfully reflect human behavioural responses to distortions in visual signals. In this paper, we present a new study of image quality perception where subjective ratings were reliably collected in a controlled laboratory environment and for a large degree of stimulus content variability. We investigate how quality perception is affected by a combination of different categories of images and different types and levels of distortions. The database will be made publicly available to facilitate calibration and validation of IQA algorithms

Neurolinguistics Research Advancing Development of a Direct-Speech Brain-Computer Interface

A direct-speech brain-computer interface (DS-BCI) acquires neural signals corresponding to imagined speech, then processes and decodes these signals to produce a linguistic output in the form of phonemes, words, or sentences. Recent research has shown the potential of neurolinguistics to enhance decoding approaches to imagined speech with the inclusion of semantics and phonology in experimental procedures. As neurolinguistics research findings are beginning to be incorporated within the scope of DS-BCI research, it is our view that a thorough understanding of imagined speech, and its relationship with overt speech, must be considered an integral feature of research in this field. With a focus on imagined speech, we provide a review of the most important neurolinguistics research informing the field of DS-BCI and suggest how this research may be utilized to improve current experimental protocols and decoding techniques. Our review of the literature supports a cross-disciplinary approach to DS-BCI research, in which neurolinguistics concepts and methods are utilized to aid development of a naturalistic mode of communication. : Cognitive Neuroscience; Computer Science; Hardware Interface Subject Areas: Cognitive Neuroscience, Computer Science, Hardware Interfac

Design of a two-layered scatternet formation (TLSF) protocol for bluetooth ad hoc wireless personal area networks

This paper aims to explore the problem scatternet formation in ad hoc wireless personal area networks built on the Bluetooth technology. The issue selection of which nodes become master or slaves and how slaves are assigned masters during the topology formation can have a significant impact on the first connectivity