Cited by 323EEG and Brain-Computer InterfacesGaze Tracking and Assistive TechnologyStroke Rehabilitation and Recovery

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The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study

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Nijboer·2010·Frontiers in Neuroscience

DOI 10.3389/fnins.2010.00055PMC PMC2916671

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Abstract

The current study investigated the effects of psychological well-being measured as quality of life (QoL), depression, current mood and motivation on brain-computer interface (BCI) performance in amyotrophic lateral sclerosis (ALS). Six participants with most advanced ALS were trained either for a block of 20 sessions with a BCI based on sensorimotor rhythms (SMR) or a block of 10 sessions with a BCI based on event-related potentials, or both. Questionnaires assessed QoL and severity of depressive symptoms before each training block and mood and motivation before each training session. The SMR-BCI required more training than the P300-BCI. The information transfer rate was higher with the P300-BCI (3.25 bits/min) than with the SMR-BCI (1.16 bits/min). Mood and motivation were related to the number of BCI sessions. Motivational factors, specifically challenge and mastery confidence, were positively related to BCI performance (controlled for the number of sessions) in tow participants, while incompetence fear was negatively related with performance in one participant. BCI performance was not related to motivational factors in three other participants nor to mood in any of the six participants. We conclude that motivational factors may be related to BCI performance in individual subjects and suggest that motivational factors and well-being should be assessed in standard BCI protocols. We also recommend using P300-based BCI as first choice in severely paralyzed patients who present with a P300 evoked potential.

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An inventory for measuring depression
BECK, A. T. (1961) · Archives of General Psychiatry · doi:10.1001/archpsyc.1961.01710120031004 · Cited by 38,032
Two new Later Stone Age sites from the Final Pleistocene in the Falémé Valley, eastern Senegal
Ndiaye, M., Lespez, L., Tribolo, C., Rassé, M., Hadjas, I., Davidoux, S., Huysecom, É., & Douze, K. (2024) · Monash University Research Portal (Monash University) · doi:10.3929/ethz-b-000667478 · Cited by 24,214
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
Motivational beliefs, values, and goals
Eccles, J. S., & Wigfield, A. (2002) · Annual Review of Psychology · doi:10.1146/annurev.psych.53.100901.135153 · Cited by 7,587
Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials.
Farwell, L. A., & Donchin, E. (1988) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(88)90149-6 · Cited by 3,228
Brain-computer interface technology: a review of the first international meeting
Wolpaw, J. R., Birbaumer, N., Heetderks, W., McFarland, D. J., Peckham, P. H., Schalk, G., Donchin, E., Quatrano, L. A., Robinson, C. J., & Vaughan, T. M. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/tre.2000.847807 · Cited by 2,322
Integrating response shift into health-related quality of life research: a theoretical model
Sprangers, M. A. G., & Schwartz, C. E. (1999) · Social Science & Medicine · doi:10.1016/s0277-9536(99)00045-3 · Cited by 2,228 · cited 4×
“All but one participant changed one or more domains of their QoL between time points 1 and 2 indicating a response shift, which is known to be adaptive when diagnosed with a chronic progressive disease (Sprangers and Schwartz, 1999).”
BackgroundInfluential
A Critical Appraisal of the Quality of Quality-of-Life Measurements
Gill, T. M. (1994) · JAMA · doi:10.1001/jama.1994.03520080061045 · Cited by 2,209
A spelling device for the paralysed
Birbaumer, N., Ghanayim, N., Hinterberger, T., Iversen, I., Kotchoubey, B., Kübler, A., Perelmouter, J., Taub, E., & Flor, H. (1999) · Nature · doi:10.1038/18581 · Cited by 1,577
Practice with Sleep Makes Perfect
Walker, M. P., Brakefield, T., Morgan, A., Hobson, J. A., & Stickgold, R. (2002) · Neuron · doi:10.1016/s0896-6273(02)00746-8 · Cited by 1,307

Flagged first, then by citations

Show all 85 references

Research works printed but not linked: 3

Neumann N. (2001). Gehirn-Computer-Kommunikation: Einflussfaktoren der Selbstregulation langsamer kortikaler Hirnpotentiale. Unpublished Dissertation, Eberhard-Karlsuniversität Tübingen, Tübingen.
Niedermeyer E. (2004). “The normal EEG of the waking adult,” in Electroencephalography: Basic Principles, Clinical Applications and Related Fields, 5th Edn, eds Niedermeyer E. Lopes da Silva F. H. (Baltimore: Lippincott Williams and Wilkins), 149– 173
Tangermann M. W. Krauledat M. Grzeska K. Sagebaum M. Blankertz B. Vidaurre C. Blankertz B. Müller K.-R. (2009). “Playing pinball with non-invasive BCI,” in Advances in Neural Information Processing Systems, Vol. 21 (Cambridge, MA: MIT Press), 1641– 1648

Sorted by citations

Items 101–200 of 321

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General principles of machine learning for brain-computer interfacing
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Person perception involves functional integration between the extrastriate body area and temporal pole
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Authors: Nijboer
Venue: Frontiers in Neuroscience
Publisher: Frontiers Media SA
Published: 2010
Volume: 4
ISSN: 1662-453X (ISSN-L)
DOI: 10.3389/fnins.2010.00055
PMC: PMC2916671
OpenAlex: W2105953877
Cited by: 323
References: 85
Citations by year: 2026: 8 · 2025: 10 · 2024: 12 · 2023: 17 · 2022: 11 · 2021: 24 · 2020: 18 · 2019: 25 · 2018: 23 · 2017: 21 · 2016: 35 · 2015: 30 · 2014: 11 · 2013: 27 · 2012: 26
FWCI: 14.72
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: Frontiers in Neuroscience (publishedVersion, cc-by)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
University of Twente Research Information (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
University of Twente Research Information (submittedVersion, other-oa)
Funders: National Institute of Biomedical Imaging and Bioengineering (R01EB000856)
Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD030146)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
Keywords: brain-computer interface, amyotrophic lateral sclerosis, P300, sensorimotor rhythm, motivation, mood, depression, quality of life, information transfer rate, augmentative and alternative communication

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The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis - a longitudinal study

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