Skip to main content
Empirische Arbeit

Spezifische Effekte von EEG-basiertem Neurofeedbacktraining auf kognitive Leistungen nach einem Schlaganfall

Ein nutzvolles Werkzeug für die Rehabilitation?

Published Online:https://doi.org/10.1024/2235-0977/a000078

Der Schlaganfall ist weltweit die häufigste neurologische Erkrankung und oft treten Störungen kognitiver Funktionen als Folgeerscheinungen auf. In dieser Studie wurde untersucht, inwiefern ein Elektroenzephalographie (EEG) basiertes Neurofeedbacktraining (NFT) genutzt werden kann, um neuronale Plastizität nach einem Schlaganfall anzuregen und spezifische kognitive Leistungen von Schlaganfallpatienten zu verbessern. Vorgängerstudien an neurologisch gesunden Probanden konnten zeigen, dass eine willentliche Erhöhung des sensomotorischen Rhythmus (SMR, 12 – 15 Hz) mit einer Verbesserung des deklarativen Gedächtnisses (Langzeitgedächtnis) und eine Verringerung des Theta/Beta Quotienten (4 – 8 Hz/13 – 21 Hz) mit einer Verbesserung der Aufmerksamkeit und Impulskontrolle einhergehen. Sieben neurologisch gesunde Personen (Kontrollgruppe) und sieben Schlaganfallpatienten mit Gedächtnisdefiziten erhielten ein SMR Neurofeedbacktraining. Sechs Schlaganfallpatienten mit Störungen der Aufmerksamkeit und Inhibitionskontrolle nahmen an einem Theta/Beta Neurofeedbacktraining teil. Um die Spezifität der beiden Neurofeedbacktrainings zu überprüfen, wurden vor und nach den Trainings generelle kognitive Fähigkeiten mittels einer umfangreichen neuropsychologischen Testbatterie erhoben. Alle Teilnehmer erhielten 10 Neurofeedback Sitzungen (SMR oder Theta/Beta), wobei sie die Aufgabe hatten ein audio-visuelles Feedbacksignal, das ihre eigene Gehirnaktivität widerspiegelte, zu kontrollieren. Bei Schlaganfallpatienten konnten positive Effekte des Neurofeedbacktrainings auf die kognitive Leistung festgestellt werden. Die Patientengruppen wiesen vor dem Training beträchtliche kognitive Leistungsdefizite im Vergleich zur Kontrollgruppe auf. Nach dem Training unterschieden sie sich jedoch in ihrer kognitiven Leistung nicht mehr auffällig von den Kontrollpersonen. Zusätzliche Analysen bestätigten die Spezifität der unterschiedlichen Trainingsprotokolle. So zeigten die Kontrollgruppe und die SMR Patientengruppe die stärksten Verbesserungen und ebenso die geringsten Verschlechterungen in ihrer deklarativen Gedächtnisleistung im Vergleich zur Theta/Beta Patientengruppe. Währenddessen verbesserte sich die Theta/Beta Patientengruppe im Vergleich zu den anderen Gruppen vor allem in den Tests zu Inhibition und Flexibilität und wies gleichzeitig die geringsten Verschlechterungen auf.


Specific Effects of Neurofeedback Training on Cognitive Performance after Stroke: a Useful Tool for Rehabilitation?

Background: Cognitive impairments are common consequences of stroke and they reduce the quality of life of affected people enormously. Although the research interest in cognitive rehabilitation is increasing, there is still a lack of effective and well evaluated training methods. In this study we evaluated an adaptive human-computer interface architecture for improving cognitive function by changing brain activity using Electroencephalogram (EEG)-based neurofeedback (NF) as a cognitive rehabilitation tool for stroke patients.

Specific components of the EEG are functionally associated with specific cognitive functions. Neurofeedbacktraining (NFT) studies provide evidence that the sensorimotor rhythm (SMR) in the EEG frequency range of 12 – 15 Hz can be used to improve learning and memory capacities. Increasing SMR due to repeated SMR based NFT led to improved declarative memory performance in neurologically healthy participants. Theta/Beta NF training has often been used to treat ADHD (attention deficit-hyperactivity disorder) in children and adults. Decreasing theta (4 – 8 Hz) and increasing faster activity in the beta frequency range (13 – 21 Hz) has positive effects on attention and inhibitory control. Using NFT, participants can learn to modulate their electrical brain activity voluntarily, which can lead to improved attention, reduced impulsivity and increased control over hyperactive behavior. Thus, Theta/Beta based NFT leads to improvements in inhibitory control.

Aims: The focus of the present study was to obtain first evidence of the effects of Theta/Beta and SMR based NFT on cognitive functions. First, stroke patients were assigned to different NF groups depending on their cognitive deficits. Patients with memory deficits attended SMR based NF and patients with deficits in attention and inhibition received Theta/Beta training. Hence, we could investigate whether different NFT (SMR and Theta/Beta) have specific effects on cognitive performance. Both patient groups were expected to show specific cognitive deficits when compared to the healthy controls before NFT, while after the NFT the cognitive performance of patients and controls was expected to improve.

Methods: Seven neurologically healthy controls (age: M = 65, SD = 5) and seven stroke patients with memory deficits performed an SMR based NFT and six stroke patients with deficits in attention and inhibition attended a Theta/Beta NFT. Five out of the 13 stroke patients took part in the study during their stay in the rehabilitation clinic Judendorf-Straßengel, Austria, (In-patients) and 8 stroke patients took advantage of home-based NFT after rehabilitation (Out-patients). All participants attended to 10 NFT sessions carried out three to five times a week. Each session included a 3-minute baseline trial and six 3-minute feedback runs. In the feedback runs, the controls and the SMR patient group were instructed to increase their SMR activity by means of audio-visual feedback. In contrast, the Theta/Beta patient group had to decrease their Theta/Beta ratio. For pre- and post-assessment, all participants had to perform standardized neuropsychological tests to assess their cognitive functions such as attention, divided attention, inhibition, flexibility, declarative memory (long term memory), and short term and working memory. Parallel forms of the memory tests were used to avoid learning effects.

Univariate ANOVAs indicated whether the cognitive performances at the pre- and post-measurement differed between groups. To investigate the effects of NFT, we conducted intraindividual comparisons with critical difference analysis. To identify significant improvement or decline for each person, the critical difference of the relevant test parameter was compared with the test score difference obtained at the post-measurement minus the pre-measurement. In order to analyze the time course of SMR or Theta/Beta power during the ten training sessions, regression analyses were carried out separately for the control group, the SMR and Theta/Beta patient group (predictor variable = run number; dependent variable = SMR/Theta/Beta power). In addition, one-sample t-tests were calculated for each group to verify the consistency of the learning effects.

Results: All groups were able to learn to control their own brain activity voluntarily. The control and SMR patient group – except one patient – showed a linear increase in SMR power over the training runs. Also, the Theta/Beta patient group was able to decrease the Theta/Beta ratio within sessions.

Group comparisons revealed significant differences between the controls and both patient groups in several cognitive test parameters before NFT, which confirmed the group assignment. After the trainings, the groups showed only significant differences in one test parameter of divided attention. Further analyses indicated that in comparison to the Theta/Beta group, controls and the SMR patient group showed a higher improvement and lower decline in their declarative memory performance after the training. Accordingly, the Theta/Beta group showed more improvements and less declines in their performance in the inhibition and cognitive flexibility task after NFT.

Discussion: We evaluated a Theta/Beta and SMR based NF training protocol and tested whether 10 NF training sessions lead to improvements in specific cognitive abilities in stroke patients. The results confirmed that SMR based NFT led to specific improvements in declarative memory performance and Theta/Beta NFT led to performance improvements in inhibition and cognitive flexibility. In the present study, stroke patients were assigned to different NFT protocols depending on their cognitive deficits for the first time. This is the first NFT study not only reporting on cognitive performance improvements due to NFT, but also reporting on negative effects of NFT. The present findings contribute to a better understanding of the mechanisms underlying NFT and cognitive processing. NFT seems to be an effective new cognitive rehabilitation tool following stroke that can provide cognitive improvements.

Literatur

  • Angelakis, E. , Stathopoulou, S. , Frymiare, J. L. , Green, D. L. , Lubar, J. F. , Kounios, J. (2007). EEG neurofeedback: a brief overview and an example of peak alpha frequency training for cognitive enhancement in the elderly. The Clinical Neuropsychologist, 21, 110 – 129. First citation in articleCrossrefGoogle Scholar

  • Arns, M. , Ridder, S. , de Strehl, U. , Breteler, M. , Coenen, A. (2009). Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clinical EEG and Neuroscience, 40, 180 – 189. First citation in articleCrossrefGoogle Scholar

  • Barry, R. J. , Clarke, A. R. , Johnstone, S. J. (2003). A review of electrophysiology in attention-deficit/hyperactivity disorder: I. Qualitative and quantitative electroencephalography. Clinical Neurophysiology, 114, 171 – 183. First citation in articleCrossrefGoogle Scholar

  • Bearden, T. S. , Cassisi, J. E. , Pineda, M. (2003). Neurofeedback training for a patient with thalamic and cortical infarctions. Applied psychophysiology and biofeedback, 28, 241 – 253. First citation in articleCrossrefGoogle Scholar

  • Boman, I.-L. , Lindstedt, M. , Hemmingsson, H. , Bartfai, A. (2004). Cognitive training in home environment. Brain Injury, 18, 985 – 995. First citation in articleCrossrefGoogle Scholar

  • Collette, F. , Van der Linden, Martial , Laureys, S. , Delfiore, G. , Degueldre, C. , Luxen, A. et al. (2005). Exploring the unity and diversity of the neural substrates of executive functioning. Human brain mapping, 25, 409 – 423. First citation in articleCrossrefGoogle Scholar

  • Doppelmayr, M. , Nosko, H. , Pecherstorfer, T. , Fink, A. (2007).An Attempt to Increase Cognitive Performance After Stroke With Neurofeedback. Biofeedback, 3, 126 – 130. First citation in articleGoogle Scholar

  • Easterbrook, P. J. , Gopalan, R. , Berlin, J. A. , Matthews, D. R. (1991). Publication bias in clinical research. The Lancet, 337, 867 – 872. First citation in articleCrossrefGoogle Scholar

  • Egner, T. , Gruzelier, J. H. (2001). Learned self-regulation of EEG frequency components affects attention and eventrelated brain potentials in humans. Neuroreport, 12, 4155 – 4159. First citation in articleCrossrefGoogle Scholar

  • Egner, T. , Gruzelier, J. H. (2004). EEG biofeedback of low beta band components: frequency-specific effects on variables of attention and event-related brain potentials. Clinical Neurophysiology, 115, 131 – 139. First citation in articleCrossrefGoogle Scholar

  • Enriquez-Geppert, S. , Huster, R. J. , Herrmann, C. S. (2013). Boosting brain functions: improving executive functions with behavioral training, neurostimulation, and neurofeedback. International journal of psychophysiology, 88, 1 – 16. First citation in articleCrossrefGoogle Scholar

  • Enriquez-Geppert, S. , Huster, R. J. , Scharfenort, R. , Mokom, Z. N. , Zimmermann, J. , Herrmann, C. S. (2014). Modulation of frontal-midline theta by neurofeedback. Biological psychology, 95, 59 – 69. First citation in articleCrossrefGoogle Scholar

  • Fox, D. J. , Tharp, D. F. , Fox, L. C. (2005). Neurofeedback: an alternative and efficacious treatment for attention deficit hyperactivity disorder. Applied psychophysiology and biofeedback, 30, 365 – 373. First citation in articleCrossrefGoogle Scholar

  • Fuchs, T. , Birbaumer, N. , Lutzenberger, W. , Gruzelier, J. H. , Kaiser, J. (2003). Neurofeedback treatment for attention-deficit/hyperactivity disorder in children: a comparison with methylphenidate. Applied psychophysiology and biofeedback, 28, 1 – 12. First citation in articleCrossrefGoogle Scholar

  • Gevensleben, H. , Holl, B. , Albrecht, B. , Schlamp, D. , Kratz, O. , Studer, P. et al. (2009). Distinct EEG effects related to neurofeedback training in children with ADHD: a randomized controlled trial. International journal of psychophysiology, 74, 149 – 157. First citation in articleCrossrefGoogle Scholar

  • Haddadi, P. , Rostami, R. , Moradi, A. , Pouladi, F. (2011). Neurofeedback Training to Enhance Learning and Memory in Patients with Cognitive Impairment. Procedia – Social and Behavioral Sciences, 30, 608 – 610. First citation in articleCrossrefGoogle Scholar

  • Hammond, D. C. (2007). What Is Neurofeedback? Journal of Neurotherapy, 10, 25 – 36. First citation in articleCrossrefGoogle Scholar

  • Hammond, D. C. , Kirk, L. (2008). First, Do No Harm: Adverse Effects and the Need for Practice Standards in Neurofeedback. Journal of Neurotherapy: Investigations in Neuromodulation, Neurofeedback and Applied Neuroscience, 12, 79 – 88. First citation in articleGoogle Scholar

  • Hoedlmoser, K. , Pecherstorfer, T. , Gruber, G. , Anderer, P. , Doppelmayr, M. , Klimesch, W. et al. (2008). Instrumental conditioning of human sensorimotor rhythm (12 – 15 Hz) and its impact on sleep as well as declarative learning. Sleep, 31, 1401– 1401. First citation in articleGoogle Scholar

  • Hoffmann, T. , Bennett, S. , Koh, C.-L. , McKenna, K. (2010). A systematic review of cognitive interventions to improve functional ability in people who have cognitive impairment following stroke. Topics in Stroke Rehabilitation, 17, 99 – 107. First citation in articleCrossrefGoogle Scholar

  • Hyndman, D. , Ashburn, A. (2003). People with stroke living in the community: Attention deficits, balance, ADL ability and falls. Disability & Rehabilitation, 25, 817 – 822. First citation in articleCrossrefGoogle Scholar

  • Kauhanen, M.-L. , Korpelainen, J. T. , Hiltunen, P. , Brusin, E. , Mononen, H. , Maatta, R. et al. (1999). Poststroke Depression Correlates With Cognitive Impairment and Neurological Deficits. Stroke, 30, 1875 – 1880. First citation in articleCrossrefGoogle Scholar

  • Kober, S. E. , Witte, M. , Ninaus, M. , Neuper, C. , Wood, G. (2013). Learning to modulate one's own brain activity: the effect of spontaneous mental strategies. Frontiers in Human Neuroscience, 7, 695 – 695. DOI: 10.3389/fnhum.2013.00695. First citation in articleGoogle Scholar

  • Kober, S. E. , Wood, G. , Hofer, D. , Kreuzig, W. , Kiefer, M. , Neuper, C. (2013). Virtual reality in neurologic rehabilitation of spatial disorientation. Journal of neuroengineering and rehabilitation, 10, 17 – 17. First citation in articleCrossrefGoogle Scholar

  • Leins, U. , Goth, G. , Hinterberger, T. , Klinger, C. , Rumpf, N. , Strehl, U. (2007). Neurofeeback for Children with ADHD: A Comparison of SCP and Theta/Beta Protocols. Applied Psychophysiology and Biofeedback, 32, 73 – 88. First citation in articleCrossrefGoogle Scholar

  • Loetscher, T. , Lincoln, N. B. (2013). Cognitive rehabilitation for attention deficits following stroke. Cochrane Database of Systematic Reviews, 5. DOI: 10.1002/14651858.CD002842.pub2. First citation in articleGoogle Scholar

  • Monastra, V. J. , Lubar, J. F. , Linden, M. , VanDeusen, P. , Green, G. , Wing, W. et al. (1999). Assessing attention deficit hyperactivity disorder via quantitative electroencephalography: An initial validation study. Neuropsychology, 13, 424 – 424. First citation in articleCrossrefGoogle Scholar

  • das Nair, R. , Lincoln, N. (2007). Cognitive rehabilitation for memory deficits following stroke. Cochrane Database of Systematic Reviews, 3. DOI: 10.1002/14651858.CD002293.pub2. First citation in articleCrossrefGoogle Scholar

  • Nijboer, F. , Birbaumer, N. , Kübler, A. (2010). The influence of psychological state and motivation on brain-computer interface performance in patients with amyotrophic lateral sclerosis – a longitudinal study. Frontiers in Neuroscience, 4, 55 – 55. DOI: 10.3389/fnins.2010.00055. First citation in articleGoogle Scholar

  • O'Donnell, M. J. , Xavier, D. , Liu, L. , Zhang, H. , Chin, S. L. , Rao-Melacini, P. et al. (2010). Risk factors for ischaemic and intracerebral haemorrhagic stroke in 22 countries (the INTERSTROKE study): a case-control study. The Lancet, 376, 112 – 123. First citation in articleCrossrefGoogle Scholar

  • Patel, M. , Coshall, C. , Rudd, A. G. , Wolfe, C. D. (2003). Natural history of cognitive impairment after stroke and factors associated with its recovery. Clinical Rehabilitation, 17, 158 – 166. First citation in articleCrossrefGoogle Scholar

  • Perry, R. J. , Watson, P. , Hodges, J. R. (2000). The nature and staging of attention dysfunction in early (minimal and mild) Alzheimer's disease: relationship to episodic and semantic memory impairment. Neuropsychologia, 38, 252 – 271. First citation in articleCrossrefGoogle Scholar

  • Pinter, M. M. , Brainin, M. (2012). Rehabilitation after stroke in older people. Maturitas, 71, 104 – 108. First citation in articleCrossrefGoogle Scholar

  • Rasquin, S. , Verhey, F. , Lousberg, R. , Winkens, I. , Lodder, J. (2002). Vascular cognitive disorders. Journal of the Neurological Sciences, 115 – 119. First citation in articleGoogle Scholar

  • Rheinberg, F. , Vollmeyer, R. , Burns, B. D. (2001). FAM: Ein Fragebogen zur Erfassung Aktueller Motivation in Lern- und Leistungssituationen. Diagnostica, 47, 57 – 66. First citation in articleLinkGoogle Scholar

  • Rozelle, G. R. , Budzynski, T. H. (1995). Neurotherapy for stroke rehabilitation: A single case study. Biofeedback and self-regulation, 20, 211 – 228. First citation in articleCrossrefGoogle Scholar

  • Schabus, M. , Gruber, G. , Parapatics, S. , Sauter, C. , Klösch, G. , Anderer, P. et al. (2004). Sleep spindles and their significance for declarative memory consolidition. Sleep physiology, 27, 1479 – 1485. First citation in articleCrossrefGoogle Scholar

  • Serruya, M. D. , Kahana, M. J. (2008). Techniques and devices to restore cognition. Behavioural brain research, 192, 149 – 165. First citation in articleCrossrefGoogle Scholar

  • Snyder, S. M. , Hall, J. R. (2006). A meta-analysis of quantitative EEG power associated with attention-deficit hyperactivity disorder. Journal of Clinical Neurophysiology, 23, 441 – 456. First citation in articleCrossrefGoogle Scholar

  • Strehl, U. (2013). Neurofeedback: Theoretische Grundlagen, Praktisches Vorgehen, Wissenschaftliche Evidenz. Stuttgart: Kohlhammer. First citation in articleGoogle Scholar

  • Tatemichi, T. K. , Desmond, D. W. , Stern, Y. , Paik, M. , Sano, M. , Bagiella, E. (1994). Cognitive impairment after stroke: frequency, patterns, and relationship to functional abilities. Journal of Neurology, Neurosurgery & Psychiatry, 57, 202 – 207. First citation in articleCrossrefGoogle Scholar

  • Thornton, K. E. , Carmody, D. P. (2009). Traumatic Brain Injury Rehabilitation: QEEG Biofeedback Treatment Protocols. Applied psychophysiology and biofeedback, 34, 59 – 68. First citation in articleCrossrefGoogle Scholar

  • Vernon, D. , Egner, T. , Cooper, N. , Compton, T. , Neilands, C. , Sheri, A. et al. (2003). The effect of training distinct neurofeedback protocols on aspects of cognitive performance. International journal of psychophysiology, 47, 75 – 85. First citation in articleCrossrefGoogle Scholar

  • Vernon, D. J. (2005). Can neurofeedback training enhance performance? An evaluation of the evidence with implications for future research. Applied psychophysiology and biofeedback, 30, 347 – 364. First citation in articleCrossrefGoogle Scholar

  • Choi, S. W. , Chi, S. E. , Chung, S. Y. , Kim, J. W. , Ahn, C. Y. , Kim, H. T. (2010). Is alpha wave neurofeedback effective with randomized clinical trials in depression? A pilot study. Neuropsychobiology, 63, 43 – 51. First citation in articleCrossrefGoogle Scholar

  • Härting, C. , Wechsler, D. (2000). Wechsler-Gedächtnistest-revidierte Fassung: WMS-R. Manual; deutsche Adaptation der revidierten Fassung der Wechsler Memory Scale. Bern: Huber. First citation in articleGoogle Scholar

  • Hautzinger, M. , Bailer, M. , Hofmeister, D. , Keller, F. (2012). Allgemeine Depressionsskala. 2., überarbeitete und neu normierte Auflage. Göttingen: Hogrefe. First citation in articleGoogle Scholar

  • Huber, H. P. (1973). Psychometrische Einzelfalldiagnostik. Weinheim: Beltz. First citation in articleGoogle Scholar

  • Jasper, H. H. (1958). The ten/twenty electrode system of the International Federation. Electroencephalography and Clinical Neurophysiology, 10, 371 – 375. First citation in articleGoogle Scholar

  • Kessler, J. , Markowitsch, H. J. , Denzler, P. (1990). Mini Mental Status Examination MMSE. German Version. Weinheim: Beltz. First citation in articleGoogle Scholar

  • Lofthouse, N. , Arnold, L. E. , Hersch, S. , Hurt, E. , DeBeus, R. (2012). A review of neurofeedback treatment for pediatric ADHD. Journal of Attention Disorders, 16, 351 – 372. First citation in articleCrossrefGoogle Scholar

  • Nelson, L. (2007). The Role of Biofeedback in Stroke Rehabilitation: Past and Future Directions. Topics in Stroke Rehabilitation, 14, 59 – 66. First citation in articleCrossrefGoogle Scholar

  • Niemann, H. , Sturm, W. , Thöne-Otto, A. , Willmes, K. (2008). CVLT–California Verbal Learning Test. Frankfurt: Pearson. First citation in articleGoogle Scholar

  • Tan, G. , Thornby, J. , Hammond, D. C. , Strehl, U. , Canady, B. , Arnemann, K. et al. (2009). Meta-analysis of EEG biofeedback in treating epilepsy. Clinical EEG and Neuroscience, 40, 173 – 179. First citation in articleCrossrefGoogle Scholar

  • Zimmermann, P. , Fimm, B. (2009). Testbatterie zur Aufmerksamkeitsprüfung Version 2.2. Herzogenrath: Psytest. First citation in articleGoogle Scholar