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Evaluation of EEG Headset Mounting for Brain-Computer Interface-Based Stroke Rehabilitation by Patients, Therapists, and Relatives

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Specialty Neurology
Date 2020 Mar 3
PMID 32116602
Citations 12
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Abstract

Brain-computer interfaces (BCIs) have successfully been used for motor recovery training in stroke patients. However, the setup of BCI systems is complex and may be divided into (1) mounting the headset and (2) calibration of the BCI. One of the major problems is mounting the headset for recording brain activity in a stroke rehabilitation context, and usability testing of this is limited. In this study, the aim was to compare the translational aspects of mounting five different commercially available headsets from a user perspective and investigate the design considerations associated with technology transfer to rehabilitation clinics and home use. No EEG signals were recorded, so the effectiveness of the systems have not been evaluated. Three out of five headsets covered the motor cortex which is needed to pick up movement intentions of attempted movements. The other two were as control and reference for potential design considerations. As primary stakeholders, nine stroke patients, eight therapists and two relatives participated; the stroke patients mounted the headsets themselves. The setup time was recorded, and participants filled in questionnaires related to comfort, aesthetics, setup complexity, overall satisfaction, and general design considerations. The patients had difficulties in mounting all headsets except for a headband with a dry electrode located on the forehead (control). The therapists and relatives were able to mount all headsets. The fastest headset to mount was the headband, and the most preferred headsets were the headband and a behind-ear headset (control). The most preferred headset that covered the motor cortex used water-based electrodes. The patients reported that it was important that they could mount the headset themselves for them to use it every day at home. These results have implications for design considerations for the development of BCI systems to be used in rehabilitation clinics and in the patient's home.

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References
1.
Zander T, Andreessen L, Berg A, Bleuel M, Pawlitzki J, Zawallich L . Evaluation of a Dry EEG System for Application of Passive Brain-Computer Interfaces in Autonomous Driving. Front Hum Neurosci. 2017; 11:78. PMC: 5329046. DOI: 10.3389/fnhum.2017.00078. View

2.
Mayaud L, Congedo M, Van Laghenhove A, Orlikowski D, Figere M, Azabou E . A comparison of recording modalities of P300 event-related potentials (ERP) for brain-computer interface (BCI) paradigm. Neurophysiol Clin. 2013; 43(4):217-27. DOI: 10.1016/j.neucli.2013.06.002. View

3.
Ekandem J, Davis T, Alvarez I, James M, Gilbert J . Evaluating the ergonomics of BCI devices for research and experimentation. Ergonomics. 2012; 55(5):592-8. DOI: 10.1080/00140139.2012.662527. View

4.
Raduntz T, Meffert B . User Experience of 7 Mobile Electroencephalography Devices: Comparative Study. JMIR Mhealth Uhealth. 2019; 7(9):e14474. PMC: 6751099. DOI: 10.2196/14474. View

5.
Morone G, Pisotta I, Pichiorri F, Kleih S, Paolucci S, Molinari M . Proof of principle of a brain-computer interface approach to support poststroke arm rehabilitation in hospitalized patients: design, acceptability, and usability. Arch Phys Med Rehabil. 2015; 96(3 Suppl):S71-8. DOI: 10.1016/j.apmr.2014.05.026. View