Panagiotis Kartsidis

Neuroplastic Effects of Combined Computerized Physical and Cognitive Training in Elderly Individuals at Risk for Dementia: An eLORETA Controlled Study on Resting States

The present study investigates whether a combined cognitive and physical training may induce changes in the cortical activity as measured via electroencephalogram (EEG) and whether this change may index a deceleration of pathological processes of brain aging. Seventy seniors meeting the clinical criteria of mild cognitive impairment (MCI) were equally divided into 5 groups: 3 experimental groups engaged in eight-week cognitive and/or physical training and 2 control groups: active and passive. A 5-minute long resting state EEG was measured before and after the intervention. Cortical EEG sources were modelled by exact low resolution brain electromagnetic tomography (eLORETA). Cognitive function was assessed before and after intervention using a battery of neuropsychological tests including the minimental state examination (MMSE). A significant training effect was identified only after the combined training scheme: a decrease in the post- compared to pre-training activity of precuneus/posterior cingulate cortex in delta, theta, and beta bands. This effect was correlated to improvements in cognitive capacity as evaluated by MMSE scores. Our results indicate that combined physical and cognitive training shows indices of a positive neuroplastic effect in MCI patients and that EEG may serve as a potential index of gains versus cognitive declines and neurodegeneration. This trial is registered with ClinicalTrials.gov Identifier NCT02313935.

Development and User Assessment of a Body-Machine Interface for a Hybrid-Controlled 6-Degree of Freedom Robotic Arm (MERCURY)

This paper presents the development, pilot testing and user assessment results for a body-machine interface (BMI) designed to control a 6-degree of freedom robotic arm, developed by our research team. The BMI was designed to be wearable, immersive and intuitive, constituting the first part of a hybrid real-time user interface. A total of 34 volunteers participated in this study, performing two sets of three tasks in which they controlled the robotic arm, a) within direct line of sight and b) through a video link. All participants completed questionnaires to evaluate their technological background, familiarization with informatics, electronics, robotics and video teleconferencing. At this point of development the system does not capture brainwaves or electric neural input, it simply captures the motion of the operator’s arm. The complete MERCURY prototype system is still under development and additionally comprises a wearable, wireless brain-computer interface (BCI) headset. The BCI headset is currently being integrated into the system and has not yet been pilot tested. The complete hybrid-interface system is primarily intended for research into human-computer interfaces, neurophysiological experiments, as well as industrial applications requiring immersive remote control of robotic machinery.

Development of MERCURY version 2.0 robotic arms for rehabilitation applications

MERCURY is a robotic platform comprised of two mechatronic robotic arm manipulators, a body machine interface (BMI) in the form of a wearable hardware sensor sleeve and a brain computer interface (BCI). It is a prototype system primarily aimed at research in human-robotic interfaces, medical rehabilitation and assistive technologies for patients with Spinal Cord Injury. This paper discusses improvements implemented in the second generation of the system, following evaluation of results obtained from pilot testing the first generation robotic setup. The system now integrates two of the second generation MERCURY robotic arms. The main improvements are digitization of control signals, the addition of anthropomorphic hands in place of pincers, two additional degrees of freedom, improved telecommunications and BCI control.

Statistical learning of multisensory regularities is enhanced in musicians: An MEG study

&NA; The present study used magnetoencephalography (MEG) to identify the neural correlates of audiovisual statistical learning, while disentangling the differential contributions of uni‐ and multi‐modal statistical mismatch responses in humans. The applied paradigm was based on a combination of a statistical learning paradigm and a multisensory oddball one, combining an audiovisual, an auditory and a visual stimulation stream, along with the corresponding deviances. Plasticity effects due to musical expertise were investigated by comparing the behavioral and MEG responses of musicians to non‐musicians. The behavioral results indicated that the learning was successful for both musicians and non‐musicians. The unimodal MEG responses are consistent with previous studies, revealing the contribution of Heschls gyrus for the identification of auditory statistical mismatches and the contribution of medial temporal and visual association areas for the visual modality. The cortical network underlying audiovisual statistical learning was found to be partly common and partly distinct from the corresponding unimodal networks, comprising right temporal and left inferior frontal sources. Musicians showed enhanced activation in superior temporal and superior frontal gyrus. Connectivity and information processing flow amongst the sources comprising the cortical network of audiovisual statistical learning, as estimated by transfer entropy, was reorganized in musicians, indicating enhanced top‐down processing. This neuroplastic effect showed a cross‐modal stability between the auditory and audiovisual modalities. HighlightsStatistical Learning of multisensory stimulation streams was investigated.Cortical responses and information transfer were measured using MEG.Musical expertise effects, were investigated comparing musicians and non‐musicians.Distinction between uni‐ and multi‐modal Statistical Learning responses was revealed.Increased top down modulation in musicians, revealed by enhanced cortical connectivity.

Experimental testing of a prototype wireless tele-alerting system for monitoring sleeping infants (smart cot MAIA)

Real time in situ medical monitoring has become increasingly common in the past few decades, driven by the development of a wide variety of affordable sensors and power autonomous, energy efficient microcontroller platforms. Portable and unobtrusive multi-sensor data acquisition is considered routine, including real-time data processing and alerting based on measurements within or around the human body. Project MAIA has developed an intelligent baby cot system capable of monitoring sleeping infants and remotely notifying their carers under pre-programmed circumstances. It aims to add a layer of protection for infants during the first 6 months of their life, primarily targeting medical emergencies such as choking, suffocation and the elusive Sudden Infant Death Syndrome (SIDS). The MAIA system also aims to contribute to current paediatric medical knowledge by providing rare in situ research data.

Commercial BCI Control and Functional Brain Networks in Spinal Cord Injury: A Proof-of-Concept

Spinal Cord Injury (SCI), along with disability, results in changes of brain organization and structure. While sensorimotor networks of patients and healthy individuals share similar patterns, unique functional interactions have been identified in SCI networks. Brain-Computer Interfaces (BCIs) have emerged as a promising technology for movement restoration and rehabilitation of SCI patients. We describe an experimental methodology to combine high-resolution electroencephalography (EEG) for investigation of functional connectivity following SCI and non-invasive BCI control of robotic arms. Two BCI-naïve female subjects, a SCI patient and a healthy control subject participated in the proof-of-concept implementation. They were instructed to perform motor imagery (MI) while watching multiple movements of either arms or legs during walking, while under 128-channel EEG recording. They were, subsequently, asked to control two robotic arms (Mercury v2.0) using a commercial class EEG-BCI. They both achieved comparable performance levels of robotic control, 52.5% for the SCI patient and 56.9% for the healthy control. We performed a feasibility analysis of functional networks on the EEG-BCI recordings. Visual MI allows training on multiple imagined movements and shows promise in investigating differences in functional cortical networks associated with different motor tasks. This approach could allow the implementation of functional network-based BCIs in the future for complex movement control.

Evaluating the AffectLecture Mobile App within an Elementary School Class Teaching Process

Elementary school students experience significant personal changes; their bodies change, as well as their inner selves. Their emotional status can be affected by both intrinsic and extrinsic factors; therefore, it is vital to highlight the emotional factors that influence the learning process, as a negative emotional status may lead to reduced motivation and low school performance, whereas a positive one may bring the opposite results. The aim of this study is to evaluate the effects of the teaching process onto the students emotional status, and how it affects their academic performance. The study was conducted on 15 3rd-grade elementary school students in Greece. The students scores on weekly reviewing tests were used, in the subjects of Language, Math, History, and Environmental Study. The affective state of the students was measured by the AffectLecture app of the AUTh Medical Physics lab. The results indicate that after attending the Environmental Study class, students have a more positive emotional status; on the other hand, this wasnt the case with the other subjects. Additionally, the students emotions and their test marks, exhibit a strong positive correlation. These findings suggest that the affective state of students and how it alters should be taken into account by education professionals, researchers and policy makers.