Brain correlates of task-load and dementia elucidation with tensor machine learning using oddball BCI paradigm

Dementia in the elderly has recently become the most usual cause of cognitive decline. The proliferation of dementia cases in aging societies creates a remarkable economic as well as medical problems in many communities worldwide. A recently published report by The World Health Organization (WHO) estimates that about 47 million people are suffering from dementia-related neurocognitive declines worldwide. The number of dementia cases is predicted by 2050 to triple, which requires the creation of an AI-based technology application to support interventions with early screening for subsequent mental wellbeing checking as well as preservation with digital-pharma (the so-called beyond a pill) therapeutical approaches. We present an attempt and exploratory results of brain signal (EEG) classification to establish digital biomarkers for dementia stage elucidation. We discuss a comparison of various machine learning approaches for automatic event-related potentials (ERPs) classification of a high and low task-load sound stimulus recognition. These ERPs are similar to those in dementia. The proposed winning method using tensor-based machine learning in a deep fully connected neural network setting is a step forward to develop AI-based approaches for a subsequent application for subjective- and mild-cognitive impairment (SCI and MCI) diagnostics.Comment: In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8578-8582, May 201

AI, Robotics, and Clinical Research for Innovative Dementia Interventions: A Japanese-German Collaboration

After a successful international workshop in Karlsruhe, Germany in June 2023, transformative initiative is underway involving major institutions: the RIKEN Cognitive Behavioral Assistive Technology (CB-AT) Team in Japan, the Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE) Rostock/Greifswald, Rostock, the Forschungszentrum Informatik (FZI) and the Karlsruhe Institute of Technology, Institute for Information Processing Technology as well as the Institute for Entrepreneurship, Technology Management and Innovation. The unique strengths of these institutions unite in an interdisciplinary collaboration focusing on novel dementia interventions. This consortium envisions the future of dementia care and the prevention of its progress – a model that brings together the strengths of AI, robotics, digital platforms, and clinical research, not just targeting patients but considering dyadic interventions that support both patients and caregivers. The KIT and FZI from Karlsruhe bring to the table expertise in software and AI engineering, and experience in research transfer. Particularly crucial is the role of the METIS platform, which supports multi-stage treatment processes for neurodegenerative diseases in an outpatient setting, integrating modern wearables and AI personalization of treatment strategies. RIKEN CB-AT complements this with robotics and system integration capabilities, including access to robots ready for integration into care regimens. The institute is renowned for its speech intervention strategies in dementia prevention, fostering the idea of using robots to aid caregivers and patients alike. Ultimately, the robots could serve as a base station, actively engaging with caregivers, assessing their stress levels, and providing mitigation strategies while simultaneously collecting crucial data. DZNE Rostock/Greifswald rounds out the partnership with a robust clinical background and access to well-defined clinical cohorts. Their research provides valuable insights into patient needs. Furthermore, their proficiency in qualitative research and dyadic interventions adds an essential layer of complexity to the project. In this alliance, a shared ethos of participatory approach, modern digital and wearable technology adoption, and individualized intervention strategies enable a unified research vision. The potential outcomes are manifold: they include technologies for outpatient measurements of intervention, prevention and care, robots aiding caregivers and patients, digitalization of care pathways, stress mitigation, and more. All partners strive to establish bi-lateral connections between existing technology and new integrations, enabling data insights from a variety of sources, including smartwatches, smartphones, robots, novel technology, and caregiver-patient interactions. These insights can be used for the personalization of intervention and care, medication, early detection of emergency situations, and strategies to empower patients and enhance the resilience of caregivers. Once addressed, the opportunity for transformative early prevention of dementia progression are immense. The expected outcomes span joint research projects, scientific publications, societal impact, and entrepreneurial initiatives. In conclusion, this collaborative venture aspires to make strides in dementia care and intervention through the integrative use of platform-based AI, robotics, and clinical research, fostering an enhanced care ecosystem that values patients and caregivers

AI for social good: unlocking the opportunity for positive impact

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world’s most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations’ 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good

Scaling laws in natural conversations among elderly people.

Language is a result of brain function; thus, impairment in cognitive function can result in language disorders. Understanding the aging of brain functions in terms of language processing is crucial for modern aging societies. Previous studies have shown that language characteristics, such as verbal fluency, are associated with cognitive functions. However, the scaling laws in language in elderly people remain poorly understood. In the current study, we recorded large-scale data of one million words from group conversations among healthy elderly people and analyzed the relationship between spoken language and cognitive functions in terms of scaling laws, namely, Zipf's law and Heaps' law. We found that word patterns followed these scaling laws irrespective of cognitive function, and that the variations in Heaps' exponents were associated with cognitive function. Moreover, variations in Heaps' exponents were associated with the ratio of new words taken from the other participants' speech. These results indicate that the exponents of scaling laws in language are related to cognitive processes

Open Brain Simulator Estimating Internal State of Human through External Observation towards Human Biomechatronics

Abstract-This paper presents open brain simulator, which estimates the neural state of human through external measurement for the purpose of improving motor and social skills. Macroscopic anatomical nervous systems model was built which can be connected to the musculoskeletal model. Microscopic anatomical and physiological neural models were interfaced to the macroscopic model. Neural activities of somatosensory area and Purkinje cell were calculated from motion capture data. The simulator provides technical infrastructure for human biomechatronics, which is promising for the novel diagnosis of neurological disorders and their treatments through medication and movement therapy, and for motor learning support system supporting acquisition of motor skill considering neural mechanism

Effect of home-based group conversation intervention using smartphone application on cognitive health and psychological well-being of older adults with subjective cognitive concerns in Japan: a randomized controlled trial protocol

BackgroundSocial activity is a key component in the prevention of cognitive decline. However, face-to-face social intervention has limited accessibility. To address this issue, we developed the “Photo-Integrated Conversation Moderated by Application” (PICMOA), a home-based group conversation intervention using smartphones. This paper introduces the PICMOA intervention and the protocol of the ongoing randomized controlled trial (RCT), which aims to evaluate the effects of PICMOA on the cognitive functioning and psychological well-being of Japanese community dwelling older adults at the risk of cognitive function decline.MethodsThis study uses an RCT design in two parallel group trials with 1:1 allocation. The participants are community dwelling older adults aged 65 years and above, living in an urban city in Japan, with subjective cognitive concerns. In total, 81 participants were allocated to the intervention or control groups. The intervention group receives 30 min of weekly PICMOA sessions at their home for 12 weeks. The PICMOA intervention consists of (1) a photo preparation period before the session and (2) a structured group conversation session talking about the photos that participants took according to a specific theme. The control group receives 30 min of weekly health education videos on a tablet device. The primary outcome is cognitive functioning at pre- and post-phases of the 12-week intervention measured using the Telephone Interview for Cognitive Status in Japanese, semantic and phonemic fluency tests, and the Digit Span Forward and Backward tests. The secondary outcomes are psychological and social aspects including mental status, well-being, loneliness, and social support.DiscussionInterest is growing in internet-based activities for preventing social isolation. However, the effect of remote conversation interventions on cognitive functioning remains unclear. This study addresses this issue and provides a new avenue of social participation for older adults.Clinical trial registrationhttps://www.umin.ac.jp/ctr/, identifier: UMIN000047247