%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 }