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01-09-2015 | Original Paper

Predicting Inter-session Performance of SMR-Based Brain–Computer Interface Using the Spectral Entropy of Resting-State EEG

Authors: Rui Zhang, Peng Xu, Rui Chen, Fali Li, Lanjin Guo, Peiyang Li, Tao Zhang, Dezhong Yao

Published in: Brain Topography | Issue 5/2015

Abstract

Currently most subjects can control the sensorimotor rhythm-based brain–computer interface (SMR-BCI) successfully after several training procedures. However, 15–30 % of subjects cannot achieve SMR-BCI control even after long-term training, and they are termed as “BCI inefficiency”. This study focuses on the investigation of reliable SMR-BCI performance predictor. 40 subjects participated in the first experimental session and 26 of them returned in the second session, each session consists of an eyes closed/open resting-state EEG recording run and four EEG recording runs with hand motor imagery. We found spectral entropy derived from eyes closed resting-state EEG of channel C3 has a high correlation with SMR-BCI performance (r = 0.65). Thus, we proposed to use it as a biomarker to predict individual SMR-BCI performance. Receiver operating characteristics analysis and leave-one-out cross-validation demonstrated that the spectral entropy predictor provide outstanding classification capability for high and low aptitude BCI users. To our knowledge, there has been no discussion about the reliability of inter-session prediction in previous studies. We further evaluated the inter-session prediction performance of the spectral entropy predictor, and the results showed that the average classification accuracy of inter-session prediction up to 89 %. The proposed predictor is convenient to obtain because it derived from single channel resting-state EEG, it could be used to identify potential SMR-BCI inefficiency subjects from novel users. But there are still limitations because Kübler et al. have shown that some BCI users may need eight or more sessions before they develop classifiable SMR activity.

Ahn M, Cho H, Ahn S, Jun SC (2013) High theta and low alpha powers may be indicative of bci-illiteracy in motor imagery. PLoS One 8:e80886. doi:10.1371/journal.pone.0080886 PubMedCentralPubMedCrossRef

Babiloni C, Carducci F, Del Gratta C, Demartin M, Romani GL, Babiloni F, Rossini PM (2003) Hemispherical asymmetry in human SMA during voluntary simple unilateral movements. An fMRI study. Cortex 39:293–305. doi:10.1016/S0010-9452(08)70110-2 PubMedCrossRef

Blankertz B et al (2010) Neurophysiological predictor of SMR-based BCI performance. Neuroimage 51:1303–1309. doi:10.1016/j.neuroimage.2010.03.022 PubMedCrossRef

Clark CR, Veltmeyer MD, Hamilton RJ, Simms E, Paul R, Hermens D, Gordon E (2004) Spontaneous alpha peak frequency predicts working memory performance across the age span. Int J Psychophysiol 53:1–9. doi:10.1016/j.ijpsycho.2003.12.011 CrossRef

Daly JJ, Wolpaw JR (2008) Brain-computer interfaces in neurological rehabilitation. Lancet Neurol 7:1032–1043. doi:10.1016/S1474-4422(08)70223-0 PubMedCrossRef

Donchin E, Spencer K, Wijesinghe R (2000) The mental prosthesis: assessing the speed of a P300-based brain-computer interface. IEEE Trans Rehabil Eng 8:174–179. doi:10.1109/86.847808 PubMedCrossRef

Fatourechi M, Bashashati A, Ward RK, Birch GE (2007) EMG and EOG artifacts in brain computer interface systems: a survey. Clin Neurophysiol 118:480–494. doi:10.1016/j.clinph.2006.10.019 PubMedCrossRef

Gao Q, Duan X, Chen H (2011) Evaluation of effective connectivity of motor areas during motor imagery and execution using conditional Granger causality. Neuroimage 54:1280–1288. doi:10.1016/j.neuroimage.2010.08.071 PubMedCrossRef

Gongora M, Peressutti C, Machado S, Teixeira S, Velasques B, Ribeiro P (2013) Progress and prospects in neurorehabilitation: clinical applications of stem cells and brain-computer interface for spinal cord lesions. Neurol Sci 34:427–433. doi:10.1007/s10072-012-1232-5 PubMedCrossRef

Grosse-Wentrup M, Schölkopf B (2012) High gamma-power predicts performance in sensorimotor-rhythm brain-computer interfaces. J Neural Eng 9:046001. doi:10.1088/1741-2560/9/4/046001 PubMedCrossRef

Grosse-Wentrup M, Schölkopf B (2013) A review of performance variations in SMR-based brain-computer interfaces (BCIs). In: Guger C, Allison BZ, Edlinger G (eds) Brain-computer interface research. springerbriefs in electrical and computer engineering. Springer, Berlin, pp 39–51. doi:10.1007/978-3-642-36083-1_5 CrossRef

Guger C, Edlinger G, Harkam W, Niedermayer I, Pfurtscheller G (2003) How many people are able to operate an EEG-based brain-computer interface (BCI)? IEEE Trans Neural Syst Rehabil Eng 11:145–147. doi:10.1109/TNSRE.2003.814481 PubMedCrossRef

Halder S et al (2011) Neural mechanisms of brain-computer interface control. Neuroimage 55:1779–1790. doi:10.1016/j.neuroimage.2011.01.021 PubMedCrossRef

Halder S, Varkuti B, Bogdan M, Kübler A, Rosenstiel W, Sitaram R, Birbaumer N (2013) Prediction of brain-computer interface aptitude from individual brain structure. Front Hum Neurosci 7:105. doi:10.3389/fnhum.2013.00105 PubMedCentralPubMedCrossRef

Hammer EM et al (2012) Psychological predictors of SMR-BCI performance. Biol Psychol 89:80–86. doi:10.1016/j.biopsycho.2011.09.006 PubMedCrossRef

Inouye T et al (1991) Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalogr Clin Neurophysiol 79:204–210. doi:10.1016/0013-4694(91)90138-T PubMedCrossRef

Kübler A, Blankertz B, Müller K, Neuper C (2011) A model of BCI-control. In: Proceedings of the 5th international brain-computer interface conference. Graz University of Technology, Austria, pp 100–103

Li Y, Long J, Yu T, Yu Z, Wang C, Zhang H, Guan C (2010) An EEG-based BCI system for 2-D cursor control by combining Mu/Beta rhythm and P300 potential. IEEE Trans Biomed Eng 57:2495–2505. doi:10.1109/TBME.2010.2055564 PubMedCrossRef

Lotte F, Guan C (2011) Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng 58:355–362. doi:10.1109/TBME.2010.2082539 PubMedCrossRef

Lu J, McFarland DJ, Wolpaw JR (2012) Adaptive Laplacian filtering for sensorimotor rhythm-based brain-computer interfaces. J Neural Eng 10:016002. doi:10.1088/1741-2560/10/1/016002 PubMedCentralPubMedCrossRef

Luo C et al (2012) Resting state basal ganglia network in idiopathic generalized epilepsy. Hum Brain Mapp 33:1279–1294. doi:10.1002/hbm.21286 PubMedCrossRef

Müller-Gerking J, Pfurtscheller G, Flyvbjerg H (1999) Designing optimal spatial filters for single-trial EEG classification in a movement task. Clin Neurophysiol 110:787–798. doi:10.1016/S1388-2457(98)00038-8 PubMedCrossRef

Mundy-Castle AC (1951) Theta and beta rhythm in the electroencephalograms of normal adults. Electroencephalogr Clin Neurophysiol 3:477–486PubMedCrossRef

Neuper C, Wörtz M, Pfurtscheller G (2006) ERD/ERS patterns reflecting sensorimotor activation and deactivation. Prog Brain Res 159:211–222. doi:10.1016/S0079-6123(06)59014-4 PubMedCrossRef

Nunes RR, Almeida MP, Sleigh JW (2004) Spectral entropy: a new method for anesthetic adequacy. Rev Bras Anestesiol 54:404–422PubMed

Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL (2001) A default mode of brain function. Proc Natl Acad Sci U S A 98:676–682. doi:10.1073/pnas.98.2.676 PubMedCentralPubMedCrossRef

Rezek IA, Roberts SJ (1998) Stochastic complexity measures for physiological signal analysis. IEEE Trans Biomed Eng 45:1186–1191. doi:10.1109/10.709563 PubMedCrossRef

Rogers BP, Carew JD, Meyerand ME (2004) Hemispheric asymmetry in supplementary motor area connectivity during unilateral finger movements. Neuroimage 22:855–859. doi:10.1016/j.neuroimage.2004.02.027 PubMedCrossRef

Vidaurre C, Blankertz B (2010) Towards a cure for BCI illiteracy. Brain Topogr 23:194–198. doi:10.1007/s10548-009-0121-6 PubMedCentralPubMedCrossRef

Vidaurre C, Kawanabe M, von Bunau P, Blankertz B, Müller K-R (2011) Toward unsupervised adaptation of LDA for brain-computer interfaces. IEEE Trans Biomed Eng 58:587–597. doi:10.1109/TBME.2010.2093133 PubMedCrossRef

Vuckovic A, Osuagwu BA (2013) Using a motor imagery questionnaire to estimate the performance of a brain-computer interface based on object oriented motor imagery. Clin Neurophysiol 124:1586–1595. doi:10.1016/j.clinph.2013.02.016 PubMedCrossRef

Wang Y, Wang R, Gao X, Hong B, Gao S (2006) A practical VEP-based brain-computer interface. IEEE Trans Neural Syst Rehabil Eng 14:234–239. doi:10.1109/TNSRE.2006.875576 PubMedCrossRef

Wolpaw JR, McFarland DJ (2004) Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. Proc Natl Acad Sci U S A 101:17849–17854. doi:10.1073/pnas.0403504101 PubMedCentralPubMedCrossRef

Wolpaw JR, Birbaumer N, McFarland DJ, Pfurtscheller G, Vaughan TM (2002) Brain-computer interfaces for communication and control. Clin Neurophysiol 113:767–791. doi:10.1016/S1388-2457(02)00057-3 PubMedCrossRef

Xu P, Hu X, Yao D (2013a) Improved wavelet entropy calculation with window functions and its preliminary application to study intracranial pressure. Comput Biol Med 43:425–433. doi:10.1016/j.compbiomed.2013.01.022 PubMedCrossRef

Xu P et al (2013b) Cortical network properties revealed by SSVEP in anesthetized rats. Sci Rep 3:2496. doi:10.1038/srep02496 PubMedCentralPubMed

Xu P, Liu T, Zhang R, Zhang Y, Yao D (2014) Using particle swarm to select frequency band and time interval for feature extraction of EEG based BCI. Biomed Signal Process Control 10:289–295. doi:10.1016/j.bspc.2013.08.012 CrossRef

Zhang R, Xu P, Guo L, Zhang Y, Li P, Yao D (2013) Z-score linear discriminant analysis for EEG based brain-computer interfaces. PLoS One 8:e74433. doi:10.1371/journal.pone.0074433 PubMedCentralPubMedCrossRef

Title: Predicting Inter-session Performance of SMR-Based Brain–Computer Interface Using the Spectral Entropy of Resting-State EEG
Authors: Rui Zhang
Peng Xu
Rui Chen
Fali Li
Lanjin Guo
Peiyang Li
Tao Zhang
Dezhong Yao
Publication date: 01-09-2015
Publisher: Springer US
Published in: Brain Topography / Issue 5/2015
Print ISSN: 0896-0267
Electronic ISSN: 1573-6792
DOI: https://doi.org/10.1007/s10548-015-0429-3

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Predicting Inter-session Performance of SMR-Based Brain–Computer Interface Using the Spectral Entropy of Resting-State EEG

Abstract

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Abstract

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