Simulating the psychological and neural effects of affective touch with soft robotics: an experimental study.

Human affective touch is known to be beneficial for social-emotional interactions and has a therapeutic effect. For touch initiated by robotic entities, richer affective affordance is a critical enabler to unlock its potential in social-emotional interactions and especially in care and therapeutic applications. Simulating the attributes of particular types of human affective touch to inform robotic touch design can be a beneficial step.

Speech motor cortex enables BCI cursor control and click.

Decoding neural activity from ventral (speech) motor cortex is known to enable high-performance speech brain-computer interface (BCI) control. It was previously unknown whether this brain area could also enable computer control via neural cursor and click, as is typically associated with dorsal (arm and hand) motor cortex. We recruited a clinical trial participant with ALS and implanted intracortical microelectrode arrays in ventral precentral gyrus (vPCG), which the participant used to operate a speech BCI in a prior study.

A mosaic of whole-body representations in human motor cortex.

Understanding how the body is represented in motor cortex is key to understanding how the brain controls movement. The precentral gyrus (PCG) has long been thought to contain largely distinct regions for the arm, leg and face (represented by the "motor homunculus"). However, mounting evidence has begun to reveal a more intermixed, interrelated and broadly tuned motor map.

An instantaneous voice synthesis neuroprosthesis.

Brain computer interfaces (BCIs) have the potential to restore communication to people who have lost the ability to speak due to neurological disease or injury. BCIs have been used to translate the neural correlates of attempted speech into text. However, text communication fails to capture the nuances of human speech such as prosody, intonation and immediately hearing one's own voice.

An Accurate and Rapidly Calibrating Speech Neuroprosthesis.

Background: Brain-computer interfaces can enable communication for people with paralysis by transforming cortical activity associated with attempted speech into text on a computer screen. Communication with brain-computer interfaces has been restricted by extensive training requirements and limited accuracy.

Methods: A 45-year-old man with amyotrophic lateral sclerosis (ALS) with tetraparesis and severe dysarthria underwent surgical implantation of four microelectrode arrays into his left ventral precentral gyrus 5 years after the onset of the illness; these arrays recorded neural activity from 256 intracortical electrodes.

An accurate and rapidly calibrating speech neuroprosthesis.

Brain-computer interfaces can enable rapid, intuitive communication for people with paralysis by transforming the cortical activity associated with attempted speech into text on a computer screen. Despite recent advances, communication with brain-computer interfaces has been restricted by extensive training data requirements and inaccurate word output. A man in his 40's with ALS with tetraparesis and severe dysarthria (ALSFRS-R = 23) was enrolled into the BrainGate2 clinical trial.

A novel approach for modelling and classifying sit-to-stand kinematics using inertial sensors.

Sit-to-stand transitions are an important part of activities of daily living and play a key role in functional mobility in humans. The sit-to-stand movement is often affected in older adults due to frailty and in patients with motor impairments such as Parkinson's disease leading to falls. Studying kinematics of sit-to-stand transitions can provide insight in assessment, monitoring and developing rehabilitation strategies for the affected populations.

Dynamics of Long-Range Temporal Correlations in Broadband EEG During Different Motor Execution and Imagery Tasks.

Brain activity is composed of oscillatory and broadband arrhythmic components; however, there is more focus on oscillatory sensorimotor rhythms to study movement, but temporal dynamics of broadband arrhythmic electroencephalography (EEG) remain unexplored. We have previously demonstrated that broadband arrhythmic EEG contains both short- and long-range temporal correlations that change significantly during movement. In this study, we build upon our previous work to gain a deeper understanding of these changes in the long-range temporal correlation (LRTC) in broadband EEG and contrast them with the well-known LRTC in alpha oscillation amplitude typically found in the literature.

Acceptability of Social Robots and Adaptation of Hybrid-Face Robot for Dementia Care in India: A Qualitative Study.

Objectives: This study aims to understand the acceptability of social robots and the adaptation of the Hybrid-Face Robot for dementia care in India.

Methods: We conducted a focus group discussion and in-depth interviews with persons with dementia (PwD), their caregivers, professionals in the field of dementia, and technical experts in robotics to collect qualitative data.

Results: This study explored the following themes: Acceptability of Robots in Dementia Care in India, Adaptation of Hybrid-Face Robot and Future of Robots in Dementia Care.

Robotic Telemedicine for Mental Health: A Multimodal Approach to Improve Human-Robot Engagement.

COVID-19 has severely impacted mental health in vulnerable demographics, in particular older adults, who face unprecedented isolation. Consequences, while globally severe, are acutely pronounced in low- and middle-income countries (LMICs) confronting pronounced gaps in resources and clinician accessibility. Social robots are well-recognized for their potential to support mental health, yet user compliance (i.

Modeling the Ongoing Dynamics of Short and Long-Range Temporal Correlations in Broadband EEG During Movement.

Electroencephalogram (EEG) undergoes complex temporal and spectral changes during voluntary movement intention. Characterization of such changes has focused mostly on narrowband spectral processes such as Event-Related Desynchronization (ERD) in the sensorimotor rhythms because EEG is mostly considered as emerging from oscillations of the neuronal populations. However, the changes in the temporal dynamics, especially in the broadband arrhythmic EEG have not been investigated for movement intention detection.

Exploration of neural correlates of movement intention based on characterisation of temporal dependencies in electroencephalography.

Brain computer interfaces (BCIs) provide a direct communication channel by using brain signals, enabling patients with motor impairments to interact with external devices. Motion intention detection is useful for intuitive movement-based BCI as movement is the fundamental mode of interaction with the environment. The aim of this paper is to investigate the temporal dynamics of brain processes using electroencephalography (EEG) to explore novel neural correlates of motion intention.

MaLT - Combined Motor and Language Therapy Tool for Brain Injury Patients Using Kinect.

Background: The functional connectivity and structural proximity of elements of the language and motor systems result in frequent co-morbidity post brain injury. Although rehabilitation services are becoming increasingly multidisciplinary and "integrated", treatment for language and motor functions often occurs in isolation. Thus, behavioural therapies which promote neural reorganisation do not reflect the high intersystem connectivity of the neurologically intact brain.