A basic study on neuro-feedback training to enhance a change of sensory-motor rhythm during motor imagery tasks

2017 ◽  
Author(s):  
Tomonari Omura ◽  
Shin'ichiro Kanoh
Keyword(s):  
Motor Imagery ◽  
Basic Study ◽  
Feedback Training ◽  
Sensory Motor ◽  
Motor Rhythm

The efficacy of EEG neurofeedback aimed at enhancing sensory-motor rhythm theta ratio in healthy subjects

2012 ◽  
Vol 221 (1) ◽  
pp. 69-74 ◽  
Author(s):  
Massimiliano de Zambotti ◽  
Marta Bianchin ◽  
Lorenzo Magazzini ◽  
Giorgia Gnesato ◽  
Alessandro Angrilli
Keyword(s):  
Healthy Subjects ◽  
Sensory Motor ◽  
Eeg Neurofeedback ◽  
Motor Rhythm

scholarly journals Can a highly accurate multi-class SSMVEP BCI induce sensory-motor rhythm in sensorimotor area?

2020 ◽  
Author(s):  
Xin Zhang ◽  
Guanghua Xu ◽  
Aravind Ravi ◽  
Sarah Pearce ◽  
Ning Jiang
Keyword(s):  
Sensorimotor Area ◽  
Sensory Motor ◽  
Motor Rhythm

scholarly journals Left Right Judgement Task and Sensory, Motor, and Cognitive Assessment in Participants with Wrist/Hand Pain

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
René Pelletier ◽  
Daniel Bourbonnais ◽  
Johanne Higgins ◽  
Maxime Mireault ◽  
Michel Alain Danino ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Stroop Test ◽  
The Body ◽  
Imagery Ability ◽  
Hand Pain ◽  
Pain Medications ◽  
Severity Scores ◽  
Pressure Pain Thresholds ◽  
Sensory Motor ◽  
Hands And Feet

The Left Right Judgement Task (LRJT) involves determining if an image of the body part is of the left or right side. The LRJT has been utilized as part of rehabilitation treatment programs for persons with pain associated with musculoskeletal injuries and conditions. Although studies often attribute changes and improvement in LRJT performance to an altered body schema, imaging studies suggest that the LRJT implicates other cortical regions. We hypothesized that cognitive factors would be related to LRJT performance of hands and feet and that sensory, motor, and pain related factors would be related to LRJT in the affected hand of participants with wrist/hand pain. In an observational cross-sectional study, sixty-one participants with wrist/hand pain participated in a study assessing motor imagery ability, cognitive (Stroop test), sensory (Two-Point Orientation Discrimination, pressure pain thresholds), motor (grip strength, Purdue Pegboard Test), and pain related measures (West Haven Yale Multidimensional Pain Inventory) as well as disability (Disability of the Arm, Shoulder and Hand). Multiple linear regression found Stroop test time and motor imagery ability to be related to LRJT performance. Tactile acuity, motor performance, participation in general activities, and the taking of pain medications were predictors of LRJT accuracy in the affected hand. Participants who took pain medications performed poorly in both LRJT accuracy (p=0.001) and reaction time of the affected hand (p=0.009). These participants had poorer cognitive (p=0.013) and motor function (p=0.002), and higher pain severity scores (p=0.010). The results suggest that the LRJT is a complex mental task that involves cognitive, sensory, motor, and behavioural processes. Differences between persons with and without pain and improvement in LRJT performance may be attributed to any of these factors and should be considered in rehabilitation research and practice utilizing this task.

A Comparative Study of Linear and Non-Linear Classifiers in Sensory Motor Imagery Based Brain Computer Interface

2019 ◽  
Vol 16 (12) ◽  
pp. 5134-5139 ◽  
Author(s):  
Poonam Chaudhary ◽  
Rashmi Agrawal
Keyword(s):  
Support Vector Machine ◽  
Comparative Study ◽  
Motor Imagery ◽  
Classification Accuracy ◽  
Support Vector ◽  
High Temporal Resolution ◽  
Neural Signals ◽  
Linear Classifiers ◽  
Sensory Motor ◽  
Non Linear

Over the period of 4–5 decades of the field electrophysiology on neural signal events in single cell recording, it can be concluded that there is production of far field potentials containing near synchronous field patterns might reach at the scalp electrodes. These neural signals then can be analyzed and converted into the control signals for computers and other electronic devices. Electroencephalography (EEG) is a method to acquire these neural signals from the scalp of human brain. EEG signals are simple, economical and have high temporal resolution properties. These properties make it advantageous to use widely in the medical as well as non-medical applications. The event related synchronization and desynchronization (ERS/ERD) pattern present in EEG during sensory motor imagery (SMI) process over the cortical area is an important feature to take BCI towards realistic approach. The accurate classification of these ERS/ERD pattern present in EEG signal is dependent on classification accuracy of different classifiers. So, the objective of the paper is to analyze the classification accuracy of linear and non-linear classifiers used for BCI system design. This paper also presents the comparative study of linear (Linear discriminant analysis and Support Vector Machine) and non-linear classifiers (Bayesian and Radial Basis Function-Support Vector Machine) using BCI competition IV dataset 2a. The result concluded that linear classifiers (LDA and SVM) have outperformed the non-linear classifiers on EEG data with the average performance across subjects 75.26±12.23, 72.42±11.12.

Acting performance and flow state enhanced with sensory-motor rhythm neurofeedback comparing ecologically valid immersive VR and training screen scenarios

2010 ◽  
Vol 480 (2) ◽  
pp. 112-116 ◽  
Author(s):  
John Gruzelier ◽  
Atsuko Inoue ◽  
Roger Smart ◽  
Anthony Steed ◽  
Tony Steffert
Keyword(s):  
Flow State ◽  
Sensory Motor ◽  
Motor Rhythm ◽  
And Training
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