Literature DB >> 23564206

Differential EMG biofeedback for children with ADHD: a control method for neurofeedback training with a case illustration.

S Maurizio1, M D Liechti, D Brandeis, L Jäncke, R Drechsler.   

Abstract

The objective of the present paper was to develop a differential electromyographic biofeedback (EMG-BF) training for children with attention-deficit/hyperactivity disorder (ADHD) matching multiple neurofeedback training protocols in order to serve as a valid control training. This differential EMG-BF training method feeds back activity from arm muscles involved in fine motor skills such as writing and grip force control. Tonic EMG-BF training (activation and deactivation blocks, involving bimanual motor tasks) matches the training of EEG frequency bands, while phasic EMG-BF training (short activation and deactivation trials) was developed as an equivalent to the training of slow cortical potentials. A case description of a child who learned to improve motor regulation in most task conditions and showed a clinically relevant reduction of behavioral ADHD symptoms illustrates the training course and outcome. Differential EMG-BF training is feasible and provides well-matched control conditions for neurofeedback training in ADHD research. Future studies should investigate its value as a specific intervention for children diagnosed with ADHD and comorbid sensorimotor problems.

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Mesh:

Year:  2013        PMID: 23564206     DOI: 10.1007/s10484-013-9213-x

Source DB:  PubMed          Journal:  Appl Psychophysiol Biofeedback        ISSN: 1090-0586


  3 in total

1.  Neurofeedback in children with attention-deficit/hyperactivity disorder (ADHD)--a controlled multicenter study of a non-pharmacological treatment approach.

Authors:  Martin Holtmann; Benjamin Pniewski; Daniel Wachtlin; Sonja Wörz; Ute Strehl
Journal:  BMC Pediatr       Date:  2014-08-13       Impact factor: 2.125

2.  Beware: Recruitment of Muscle Activity by the EEG-Neurofeedback Trainings of High Frequencies.

Authors:  Katarzyna Paluch; Katarzyna Jurewicz; Jacek Rogala; Rafał Krauz; Marta Szczypińska; Mirosław Mikicin; Andrzej Wróbel; Ewa Kublik
Journal:  Front Hum Neurosci       Date:  2017-03-20       Impact factor: 3.169

3.  Impact of EEG Frequency Bands and Data Separation on the Performance of Person Verification Employing Neural Networks.

Authors:  Renata Plucińska; Konrad Jędrzejewski; Marek Waligóra; Urszula Malinowska; Jacek Rogala
Journal:  Sensors (Basel)       Date:  2022-07-25       Impact factor: 3.847

  3 in total

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