OBJECTIVE: This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain-computer interface (BCI) for verbal communication. METHODS: EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a 'telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time-frequency maps of event-related (de-)synchronization (ERD/ERS). RESULTS: The patient learned to 'produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70\% (correct responses) for letter selection. 'Copy spelling' was performed with a rate of approximately one letter per min. CONCLUSIONS: The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. 'Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.
%0 Journal Article
%1 Neuper2003a
%A Neuper, C.
%A M?ller, G. R.
%A K?bler, A.
%A Birbaumer, N.
%A Pfurtscheller, G.
%D 2003
%J Clin Neurophysiol
%K Adult; Biofeedback (Psychology); Cerebral Palsy; Communication Aids for Disabled; Barriers; Electroencephalography; Humans; Male; Motor Cortex; Paralysis; Severity of Illness Index; Somatosensory User-Computer Interface
%N 3
%P 399--409
%T Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment.
%V 114
%X OBJECTIVE: This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain-computer interface (BCI) for verbal communication. METHODS: EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a 'telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time-frequency maps of event-related (de-)synchronization (ERD/ERS). RESULTS: The patient learned to 'produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70\% (correct responses) for letter selection. 'Copy spelling' was performed with a rate of approximately one letter per min. CONCLUSIONS: The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. 'Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.
@article{Neuper2003a,
abstract = {OBJECTIVE: This case study describes how a completely paralyzed patient, diagnosed with severe cerebral palsy, was trained over a period of several months to use an electroencephalography (EEG)-based brain-computer interface (BCI) for verbal communication. METHODS: EEG feedback training was performed in the patient's home (clinic), supervised from a distant laboratory with the help of a 'telemonitoring system'. Online feedback computation was based on single-trial analysis and classification of specific band power features of the spontaneous EEG. Task-related changes in brain oscillations over the course of training steps was investigated by quantifying time-frequency maps of event-related (de-)synchronization (ERD/ERS). RESULTS: The patient learned to 'produce' two distinct EEG patterns, beta band ERD during movement imagery vs. no ERD during relaxing, and to use this for BCI-controlled spelling. Significant learning progress was found as a function of training session, resulting in an average accuracy level of 70\% (correct responses) for letter selection. 'Copy spelling' was performed with a rate of approximately one letter per min. CONCLUSIONS: The proposed BCI training procedure, based on electroencephalogram (EEG) biofeedback and concomitant adaptation of feature extraction and classification, may improve actual levels of communication ability in locked-in patients. 'Telemonitoring-assisted' BCI training facilitates clinical application in a larger number of patients.},
added-at = {2014-07-19T20:54:23.000+0200},
author = {Neuper, C. and M?ller, G. R. and K?bler, A. and Birbaumer, N. and Pfurtscheller, G.},
biburl = {https://www.bibsonomy.org/bibtex/2d1294e30b47f9de1036cd599b3b7947b/ar0berts},
groups = {public},
interhash = {d429368e45985a415284d7c91ae68f68},
intrahash = {d1294e30b47f9de1036cd599b3b7947b},
journal = {Clin Neurophysiol},
keywords = {Adult; Biofeedback (Psychology); Cerebral Palsy; Communication Aids for Disabled; Barriers; Electroencephalography; Humans; Male; Motor Cortex; Paralysis; Severity of Illness Index; Somatosensory User-Computer Interface},
month = Mar,
number = 3,
pages = {399--409},
pii = {S1388245702003875},
pmid = {12705420},
timestamp = {2014-07-19T20:54:23.000+0200},
title = {Clinical application of an EEG-based brain-computer interface: a case study in a patient with severe motor impairment.},
username = {ar0berts},
volume = 114,
year = 2003
}