The comparison of motor learning performance with and without feedback

References

• Blankertz B, Dornhege G, Krauledat M, Müller KR, Kunzmann V, Losch F, Curio G. The Berlin Brain–Computer Interface: EEG-based communication without subect training. IEEE Trans Neural Syst Rehabil Eng 2006; 14(2)147–152
   PubMedGoogle Scholar
• Choi K, Chichocki A. Control of a wheelchair by motor imagery in real time. Lect Notes Comput Sci 2008; 5326: 330–337
   Google Scholar
• Curran EA, Stokes MJ. Learning to control brain activity: A review of the production and control of EEG components for driving brain–computer interface (BCI) systems. Brain Cogn 2003; 51: 326–336
   PubMed Web of Science ®Google Scholar
• Daly JJ, Wolpaw JR. Brain–computer interfaces in neurological rehabilitation. Lancet Neural 2008; 7: 1032–1043
   PubMed Web of Science ®Google Scholar
• Daubechies I, 1992. Ten lectures on wavelets. CBM-NSF Regional Conference Series in Applied Mathematics, Philadelphia
   Google Scholar
• Edlinger G, Holzner C, Guger C, Groenegrees C, Slater C, 2009. Brain–computer interfaces for goal oriented control of a virtual smart home environment. The 4th International IEEE EMBS Conference on Neural Engineering, 29 April–2 May 2009. pp 463–465
   Google Scholar
• Guger C, Ramoser H, Pfurtscheller G. Real-time EEG analysis with subject-specific spatial patterns for a brain computer interface (BCI). IEEE Trans Rehabil Eng 2000; 8(4)447–456
   PubMedGoogle Scholar
• Hari R, Forss N, Avikainen S, Kirveskari E, Saleniu S, Rizzolatti G. Activation of human primary motor cortex during action observation: A neuromagnetic study. Proc Natl Acad Sci USA 1998; 95: 15061–15065
   PubMed Web of Science ®Google Scholar
• Huang S, Wu X. Feature extraction and classification of EEG for imagery movement based on mu/beta rhythms. BMEI 2010; 2: 891
   Google Scholar
• Kuhlman WN. EEG feedback training: Enhancement of somatosensory control activity. Electroencephalogr Clin Neurophysiol 1978; 45: 290–294
   PubMedGoogle Scholar
• Lemm S, Blankertz B, Curio G, Müller KR. Spatio-spectral filters for improving the classification of single trial EEG. IEEE Trans Biomed Eng 2005; 52(9)1541–1548
   PubMed Web of Science ®Google Scholar
• Lopes da Silva FH. Neural mechanisms underlying brain waves: From neural membranes to networks. Electroencephalogr Clin Neurophysiol 1991; 79: 81–93
   PubMedGoogle Scholar
• Lopes da Silva FH, Pfurtscheller G. Basic concepts on EEG synchronization and desynchronization. Handbook of electroencephalography and clinical neurophysiology. Rev ed, G Pfurtscheller, FH Lopes da Silva. Elsevier, Amsterdam 1999; 6: 3–11
   Google Scholar
• McFarland DJ, McCane LM, Wolpaw JR. EEG-based communication and control: Short-term role of feedback. IEEE Trans Rehabil Eng 1998; 6(1)7–11
   PubMedGoogle Scholar
• Müller-Gerking J, Pfurtscheller G, Flyvbjerg H. Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol 1998; 110: 777–798
   Google Scholar
• Murugappan M, Nagarajan R, Yaacob S. Combining spatial filtering and wavelet transform for classifying human emotions using EEG signals. J Med Biol Eng 2011; 31(1)45–51
   Google Scholar
• Neuper C, Pfurtscheller G. Motor imagery and ERD. Handbook of electroencephalography and clinical neurophysiology. Rev ed, G Pfurtscheller, FH Lopes da Silva. Elsevier, Amsterdam 1999; 6: 303–322
   Google Scholar
• Neuper C, Scherer R, Wriessnegger S, Pfurtscheller G. Motor imagery and action observation: Modulation of sensorimotor brain rhythms during mental control of a brain–computer interface. Clin Neurophysiol 2009; 120: 239–247
   PubMed Web of Science ®Google Scholar
• Orand A, Ushiba J, Tomita Y, Honda S. Classifying EEG-based motor imagery tasks by means of independent component analysis and common spatial pattern. SICE JCMSI 2011; 4(6)445–451
   Google Scholar
• Pfurtscheller G, Lopes da Silva FH. Event-related EEG/MEG synchronization and desynchronization: Basic principles. Clin Neurophysiol 1999; 110: 1842–1857
   PubMed Web of Science ®Google Scholar
• Pfurtscheller G. Induced oscillations in the alpha band: Functional meaning. Epilepsia 2003; 44(12)2–8
   PubMedGoogle Scholar
• Pfurtscheller G, Neuper C, Brunner C, Lopes da Silva F. Beta rebound after different types of motor imagery in man. Neurosci Lett 2005; 378: 156–159
   PubMed Web of Science ®Google Scholar
• Pfurtscheller G, Robert L, Claudia K, Doron F, Christa N. Walking from thought. Cogn Brain Res 2006; 1071: 145–152
   Google Scholar
• Pineda JA, Silverman DS, Vankov A, Hestenes J. Learning to control brain rhythms: Making a brain–computer interface possible. IEEE Trans Neural Syst Rehabil Eng 2003; 11(2)181–184
   PubMed Web of Science ®Google Scholar
• Ramoser H, Müller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng 2000; 8(4)441–446
   PubMedGoogle Scholar
• Salmoni AW, Schmidt RA, Walter CB. Knowledge of results and motor learning: A review and critical reappraisal. Psychol Bull 1984; 95(3)355–386
   PubMed Web of Science ®Google Scholar
• Subasi A. Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients. Expert Syst Applic 2005; 28: 1–11
   Google Scholar
• Vaughan TM, Wolpaw JR, Donchin E. EEG-based communication: Prospects and problems. IEEE Trans Rehabil Eng 1996; 4(4)425–430
   PubMedGoogle Scholar
• Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM. Brain–computer interface for communication and control. Clin Neurophysiol 2002; 113: 767–791
   PubMed Web of Science ®Google Scholar