scholarly journals Electroencephalographic Functional Connectivity With the Tacit Learning System Prosthetic Hand: A Case Series Using Motor Imagery

2020 ◽  
Vol 12 ◽  
Author(s):  
Katsuyuki Iwatsuki ◽  
Minoru Hoshiyama ◽  
Shintaro Oyama ◽  
Hidemasa Yoneda ◽  
Shingo Shimoda ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Motor Imagery ◽  
Case Series ◽  
Learning System ◽  
Prosthetic Hand ◽  
Tacit Learning

scholarly journals Biomechanical Reconstruction Using the Tacit Learning System: Intuitive Control of Prosthetic Hand Rotation

2016 ◽  
Vol 10 ◽  
Author(s):  
Shintaro Oyama ◽  
Shingo Shimoda ◽  
Fady S. K. Alnajjar ◽  
Katsuyuki Iwatsuki ◽  
Minoru Hoshiyama ◽  
...  
Keyword(s):  
Learning System ◽  
Prosthetic Hand ◽  
Biomechanical Reconstruction ◽  
Tacit Learning

scholarly journals Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Alkinoos Athanasiou ◽  
Chrysa Lithari ◽  
Konstantina Kalogianni ◽  
Manousos A. Klados ◽  
Panagiotis D. Bamidis
Keyword(s):  
Functional Connectivity ◽  
Motor Imagery ◽  
Sensorimotor Cortex ◽  
Mu Rhythm ◽  
Source Detection ◽  
Motor Execution ◽  
Motor Tasks ◽  
Computer Interfaces ◽  
Preliminary Study ◽  
Real Hand

Introduction. Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas.Materials and Methods. Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome.Results and Discussion. Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution.Conclusions. Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces.

Modified Graded Motor Imagery for Musicians’ Focal Dystonia: A Case Series

2021 ◽  
Vol 36 (1) ◽  
pp. 10-17
Author(s):  
Marina Ramella ◽  
Francesca Borgnis ◽  
Giulia Giacobbi ◽  
Anna Castagna ◽  
Frncesca Baglio ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Treatment Protocol ◽  
Case Series ◽  
Focal Dystonia ◽  
Rehabilitative Treatment ◽  
Cortical Organization ◽  
Cortical Motor ◽  
Home Based ◽  
Before And After ◽  
Graded Motor Imagery

PURPOSE: This study aimed to assess the effectiveness of the “modified graded motor imagery” (mGMI) protocol as a rehabilitative treatment of musician’s focal dystonia (MFD). METHODS: Six musicians with MFD (age 43.83±17.24 yrs) performed the home-based mGMI protocol (laterality training, imagined hand movements and visual mirror feedback) once a day for 4 weeks. The mMGI protocol was designed to sequentially activate cortical motor networks and improve cortical organization. Subjects were evaluated before and after treatment with the dystonia evaluation scale (DES), arm dystonia disability scale (ADDS), Tubiana-Chamagne scale (TCS), and performing scale (PS). RESULTS: All participants were compliant with the mGMI treatment protocol without any adverse events. A significant improvement was measured in ADDS (p=0.047) and TCS scores (p=0.014) but not in DES (p=0.157). The severity of MFD decreased from moderate to mild in four patients. After mGMI treatment, all musicians were able to play easy pieces (TCS: median 3.5, IR 3.5–4). CONCLUSION: The findings from this pilot study suggest that home-based mGMI treatment is a feasible and promising rehabilitative approach for patients with mild to moderate MFD.

scholarly journals Functional Brain Connectivity during Multiple Motor Imagery Tasks in Spinal Cord Injury

2018 ◽  
Vol 2018 ◽  
pp. 1-20 ◽  
Author(s):  
Alkinoos Athanasiou ◽  
Nikos Terzopoulos ◽  
Niki Pandria ◽  
Ioannis Xygonakis ◽  
Nicolas Foroglou ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Spinal Cord Injury ◽  
Functional Connectivity ◽  
Motor Imagery ◽  
Healthy Subjects ◽  
Brain Connectivity ◽  
Descending Pathways ◽  
Brain Organization ◽  
Sensorimotor Network ◽  
Cord Injury

Reciprocal communication of the central and peripheral nervous systems is compromised during spinal cord injury due to neurotrauma of ascending and descending pathways. Changes in brain organization after spinal cord injury have been associated with differences in prognosis. Changes in functional connectivity may also serve as injury biomarkers. Most studies on functional connectivity have focused on chronic complete injury or resting-state condition. In our study, ten right-handed patients with incomplete spinal cord injury and ten age- and gender-matched healthy controls performed multiple visual motor imagery tasks of upper extremities and walking under high-resolution electroencephalography recording. Directed transfer function was used to study connectivity at the cortical source space between sensorimotor nodes. Chronic disruption of reciprocal communication in incomplete injury could result in permanent significant decrease of connectivity in a subset of the sensorimotor network, regardless of positive or negative neurological outcome. Cingulate motor areas consistently contributed the larger outflow (right) and received the higher inflow (left) among all nodes, across all motor imagery categories, in both groups. Injured subjects had higher outflow from left cingulate than healthy subjects and higher inflow in right cingulate than healthy subjects. Alpha networks were less dense, showing less integration and more segregation than beta networks. Spinal cord injury patients showed signs of increased local processing as adaptive mechanism. This trial is registered with NCT02443558.

scholarly journals EEG and Deep Learning Based Brain Cognitive Function Classification

Computers ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 104
Author(s):  
Saraswati Sridhar ◽  
Vidya Manian
Keyword(s):  
Deep Learning ◽  
Motor Imagery ◽  
Cognitive Aging ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Principal Component ◽  
Learning System ◽  
Detection Accuracy ◽  
Band Power ◽  
Signal Features

Electroencephalogram signals are used to assess neurodegenerative diseases and develop sophisticated brain machine interfaces for rehabilitation and gaming. Most of the applications use only motor imagery or evoked potentials. Here, a deep learning network based on a sensory motor paradigm (auditory, olfactory, movement, and motor-imagery) that employs a subject-agnostic Bidirectional Long Short-Term Memory (BLSTM) Network is developed to assess cognitive functions and identify its relationship with brain signal features, which is hypothesized to consistently indicate cognitive decline. Testing occurred with healthy subjects of age 20–40, 40–60, and >60, and mildly cognitive impaired subjects. Auditory and olfactory stimuli were presented to the subjects and the subjects imagined and conducted movement of each arm during which Electroencephalogram (EEG)/Electromyogram (EMG) signals were recorded. A deep BLSTM Neural Network is trained with Principal Component features from evoked signals and assesses their corresponding pathways. Wavelet analysis is used to decompose evoked signals and calculate the band power of component frequency bands. This deep learning system performs better than conventional deep neural networks in detecting MCI. Most features studied peaked at the age range 40–60 and were lower for the MCI group than for any other group tested. Detection accuracy of left-hand motor imagery signals best indicated cognitive aging (p = 0.0012); here, the mean classification accuracy per age group declined from 91.93% to 81.64%, and is 69.53% for MCI subjects. Motor-imagery-evoked band power, particularly in gamma bands, best indicated (p = 0.007) cognitive aging. Although the classification accuracy of the potentials effectively distinguished cognitive aging from MCI (p < 0.05), followed by gamma-band power.

Space-time recurrences for functional connectivity evaluation and feature extraction in motor imagery brain-computer interfaces

2019 ◽  
Vol 57 (8) ◽  
pp. 1709-1725 ◽  
Author(s):  
Paula G. Rodrigues ◽  
Carlos A. Stefano Filho ◽  
Romis Attux ◽  
Gabriela Castellano ◽  
Diogo C. Soriano
Keyword(s):  
Feature Extraction ◽  
Functional Connectivity ◽  
Motor Imagery ◽  
Space Time ◽  
Brain Computer Interfaces ◽  
Computer Interfaces
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