Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery BCIs
Tóm tắt
Phase synchrony has extensively been studied for understanding neural coordination in health and disease. There are a few studies concerning the implications in the context of BCIs, but its potential for establishing a communication channel in patients suffering from neuromuscular disorders remains totally unexplored. We investigate, here, this possibility by estimating the time-resolved phase connectivity patterns induced during a motor imagery (MI) task and adopting a supervised learning scheme to recover the subject’s intention from the streaming data. Electroencephalographic activity from six patients suffering from neuromuscular disease (NMD) and six healthy individuals was recorded during two randomly alternating, externally cued, MI tasks (clenching either left or right fist) and a rest condition. The metric of Phase locking value (PLV) was used to describe the functional coupling between all recording sites. The functional connectivity patterns and the associate network organization was first compared between the two cohorts. Next, working at the level of individual patients, we trained support vector machines (SVMs) to discriminate between “left” and “right” based on different instantiations of connectivity patterns (depending on the encountered brain rhythm and the temporal interval). Finally, we designed and realized a novel brain decoding scheme that could interpret the intention from streaming connectivity patterns, based on an ensemble of SVMs. The group-level analysis revealed increased phase synchrony and richer network organization in patients. This trend was also seen in the performance of the employed classifiers. Time-resolved connectivity led to superior performance, with distinct SVMs acting as local experts, specialized in the patterning emerged within specific temporal windows (defined with respect to the external trigger). This empirical finding was further exploited in implementing a decoding scheme that can be activated without the need of the precise timing of a trigger. The increased phase synchrony in NMD patients can turn to a valuable tool for MI decoding. Considering the fast implementation for the PLV pattern computation in multichannel signals, we can envision the development of efficient personalized BCI systems in assistance of these patients.
Tài liệu tham khảo
Lebedev MA, Nicolelis MA. Brain-machine interfaces: from basic science to neuroprostheses and neurorehabilitation. Physiol Rev. 2017;97(2):767–837.
Nam CS, Nijholt A, Lotte F. Brain–computer interfaces handbook. Technological and Theoretical Advances; 2018. p. 9.
Berger H. Über das elektrenkephalogramm des menschen. Archiv für Psychiatrie und Nervenkrankheiten. 1929;87(1):527–70.
Liparas D, Dimitriadis SI, Laskaris NA, Tzelepi A, Charalambous K, Angelis L. Exploiting the temporal patterning of transient VEP signals: a statistical single-trial methodology with implications to brain–computer interfaces (BCIs). J Neurosci Methods. 2014;232:189–98.
Riechmann H, Finke A, Ritter H. Using a cVEP-based brain-computer Interface to control a virtual agent. IEEE Trans Neural Syst Rehabil Eng. 2016;24(6):692–9.
Georgiadis K, Laskaris N, Nikolopoulos S, Kompatsiaris I. Discriminative codewaves: a symbolic dynamics approach to SSVEP recognition for asynchronous BCI. J Neural Eng. 2018;15(2):026008.
Xu M, Xiao X, Wang Y, Qi H, Jung TP, Ming D. A brain computer interface based on miniature event-related potentials induced by very small lateral visual stimuli. IEEE Trans Biomed Eng. 2018;65(5):1166–75.
Nakayashiki K, Saeki M, Takata Y, Hayashi Y, Kondo T. Modulation of event-related desynchronization during kinematic and kinetic hand movements. J Neuroeng Rehabil. 2014;11(1):90.
Andrade J, Cecílio J, Simões M, Sales F, Castelo-Branco M. Separability of motor imagery of the self from interpretation of motor intentions of others at the single trial level: an EEG study. J Neuroeng Rehabil. 2017;14(1):63.
Nam CS, Jeon Y, Kim YJ, Lee I, Park K. Movement imagery-related lateralization of event-related (de) synchronization (ERD/ERS): motor-imagery duration effects. Clin Neurophysiol. 2011;122(3):567–77.
Solis-Escalante T, Müller-Putz G, Pfurtscheller G. Overt foot movement detection in one single Laplacian EEG derivation. J Neurosci Methods. 2008;175(1):148–53.
Ge S, Wang R, Yu D. Classification of four-class motor imagery employing single-channel electroencephalography. PLoS One. 2014;9(6):e98019.
Deng S, Srinivasan R, Lappas T, D’Zmura M. EEG classification of imagined syllable rhythm using Hilbert spectrum methods. J Neural Eng. 2010;7(4):046006.
Wang L, Zhang X, Zhong X, Zhang Y. Analysis and classification of speech imagery EEG for BCI. Biomed Signal Proc Control. 2013;8(6):901–8.
Dimitriadis S, Sun Y, Laskaris N, Thakor N, Bezerianos A. Revealing cross-frequency causal interactions during a mental arithmetic task through symbolic transfer entropy: a novel vector-quantization approach. IEEE Trans Neural Syst Rehabil Eng. 2016;24(10):1017–28.
Wang Q, Sourina O. Real-time mental arithmetic task recognition from EEG signals. IEEE Trans Neural Syst Rehabil Eng. 2013;21(2):225–32.
Yuan H, He B. Brain–computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng. 2014;61(5):1425–35.
Pfurtscheller G, Da Silva FL. Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842–57.
Formaggio E, Storti SF, Galazzo IB, Gandolfi M, Geroin C, Smania N, Spezia L, Waldner A, Fiaschi A, Manganotti P. Modulation of event-related desynchronization in robot-assisted hand performance: brain oscillatory changes in active, passive and imagined movements. J Neuroeng Rehabil. 2013;10(1):24.
Pfurtscheller G, Brunner C, Schlögl A, Da Silva FL. Mu rhythm (de) synchronization and EEG single-trial classification of different motor imagery tasks. NeuroImage. 2006;31(1):153–9.
Ramoser H, Muller-Gerking J, Pfurtscheller G. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE Trans Rehabil Eng. 2000;8(4):441–6.
Ang KK, Chin ZY, Zhang H, Guan C. Filter bank common spatial pattern (FBCSP) in brain-computer interface. InNeural Networks, IJCNN 2008.(IEEE World Congress on Computational Intelligence). IEEE Int Joint Conf. 2008, 2008:2390–7 (pp) IEEE.
Robinson N, Guan C, Vinod AP, Ang KK, Tee KP. Multi-class EEG classification of voluntary hand movement directions. J Neural Eng. 2013;10(5):056018.
Thomas KP, Guan C, Lau CT, Vinod AP, Ang KK. A new discriminative common spatial pattern method for motor imagery brain–computer interfaces. IEEE Trans Biomed Eng. 2009;56(11):2730–3.
Lotte F, Guan C. Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans Biomed Eng. 2011;58(2):355–62.
Brunner C, Scherer R, Graimann B, Supp G, Pfurtscheller G. Online control of a brain-computer interface using phase synchronization. IEEE Trans Biomed Eng. 2006;53(12):2501–6.
Caramia N, Lotte F, Ramat S. Optimizing spatial filter pairs for EEG classification based on phase-synchronization. InAcoustics, Speech and Signal Processing (ICASSP), 2014. IEEE Int Conf. 2014:2049–53 IEEE.
Stavrinou ML, Moraru L, Cimponeriu L, Della Penna S, Bezerianos A. Evaluation of cortical connectivity during real and imagined rhythmic finger tapping. Brain Topogr. 2007;19(3):137–45.
Song L, Gordon E, Gysels E. Phase synchrony rate for the recognition of motor imagery in brain-computer interface. In Advances in Neural Information Processing Systems. 2006:1265–72.
Scherer R, Schloegl A, Lee F, Bischof H, Janša J, Pfurtscheller G. The self-paced Graz brain-computer interface: methods and applications. Comput Intell Neurosci. 2007:9.
Chae Y, Jeong J, Jo S. Toward brain-actuated humanoid robots: asynchronous direct control using an EEG-based BCI. IEEE Trans Robot. 2012;28(5):1131–44.
Leeb R, Friedman D, Müller-Putz GR, Scherer R, Slater M, Pfurtscheller G. Self-paced (asynchronous) BCI control of a wheelchair in virtual environments: a case study with a tetraplegic. Computational intelligence and neuroscience. 2007;1(2007):7.
Müller-Putz GR, Kaiser V, Solis-Escalante T, Pfurtscheller G. Fast set-up asynchronous brain-switch based on detection of foot motor imagery in 1-channel EEG. Med Biol Eng comput. 2010;48(3):229–33.
Kübler A, Nijboer F, Mellinger J, Vaughan TM, Pawelzik H, Schalk G, McFarland DJ, Birbaumer N, Wolpaw JR. Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface. Neurology. 2005;64(10):1775–7.
Bai O, Lin P, Huang D, Fei DY, Floeter MK. Towards a user-friendly brain–computer interface: initial tests in ALS and PLS patients. Clin Neurophysiol. 2010;121(8):1293–303.
King CE, Wang PT, Chui LA, Do AH, Nenadic Z. Operation of a brain-computer interface walking simulator for individuals with spinal cord injury. J Neuroeng Rehabil. 2013 Dec;10(1):77.
Conradi J, Blankertz B, Tangermann M, Kunzmann V, Curio G. Brain-computer interfacing in tetraplegic patients with high spinal cord injury. Int J Bioelectromagn. 2009;11(2):65–8.
Heremans E, Nieuwboer A, Spildooren J, De Bondt S, D'hooge AM, Helsen W, Feys P. Cued motor imagery in patients with multiple sclerosis. Neuroscience. 2012;206:115–21.
Allali G, Laidet M, Assal F, Beauchet O, Chofflon M, Armand S, Lalive PH. Adapted timed up and go: a rapid clinical test to assess gait and cognition in multiple sclerosis. Eur Neurol. 2012;67(2):116–20.
Leamy DJ, Kocijan J, Domijan K, Duffin J, Roche RA, Commins S, Collins R, Ward TE. An exploration of EEG features during recovery following stroke–implications for BCI-mediated neurorehabilitation therapy. J Neuroeng Rehabil. 2014;11(1):9.
Shindo K, Kawashima K, Ushiba J, Ota N, Ito M, Ota T, Kimura A, Liu M. Effects of neurofeedback training with an electroencephalogram-based brain–computer interface for hand paralysis in patients with chronic stroke: a preliminary case series study. J Rehabil Med. 2011;43(10):951–7.
Cincotti F, Mattia D, Aloise F, Bufalari S, Schalk G, Oriolo G, Cherubini A, Marciani MG, Babiloni F. Non-invasive brain–computer interface system: towards its application as assistive technology. Brain Res Bull. 2008;75(6):796–803.
Nikolopoulos S, Petrantonakis PC, Georgiadis K, Kalaganis F, Liaros G, Lazarou I, Adam K, Papazoglou-Chalikias A, Chatzilari E, Oikonomou VP, Kumar C. A multimodal dataset for authoring and editing multimedia content: the MAMEM project. Data in Brief. 2017;15:1048–56.
Delorme A, Sejnowski T, Makeig S. Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. NeuroImage. 2007;34(4):1443–9.
Lachaux JP, Rodriguez E, Martinerie J, Varela FJ. Measuring phase synchrony in brain signals. Hum Brain Mapp. 1999;8(4):194–208.
Fornito A, Zalesky A, Bullmore E. Fundamentals of brain network analysis. San Diego: Academic Press; 2016.
Fallani FD, Richiardi J, Chavez M, Achard S. Graph analysis of functional brain networks: practical issues in translational neuroscience. Phil Trans R Soc B. 2014;369(1653):20130521.
Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87(19):198701.
Dimitriadis SI, Laskaris NA, Tsirka V, Vourkas M, Micheloyannis S, Fotopoulos S. Tracking brain dynamics via time-dependent network analysis. J Neurosci Methods. 2010;193(1):145–55.
Dimitriadis SI, Laskaris NA, Tzelepi A. On the quantization of time-varying phase synchrony patterns into distinct functional connectivity microstates (FCμstates) in a multi-trial visual ERP paradigm. Brain Topogr. 2013;26(3):397–409.
Gonuguntla V, Wang Y, Veluvolu KC. Event-related functional network identification: application to EEG classification. IEEE J Selected Topics in Signal Proc. 2016;10(7):1284–94.
learning BCMM, recognition p. Information science and statistics. Heidelberg: Springer; 2006.
Park C, Looney D, ur Rehman N, Ahrabian A, Mandic DP. Classification of motor imagery BCI using multivariate empirical mode decomposition. IEEE Trans Neural Syst Rehabil Eng. 2013;21(1):10–22.
Tam WK, Tong KY, Meng F, Gao S. A minimal set of electrodes for motor imagery BCI to control an assistive device in chronic stroke subjects: a multi-session study. IEEE Trans Neural Sys Rehabil Eng. 2011;19(6):617–27.
Platt J. Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. Advances in Large Margin Classifiers. 1999;10(3):61–74.
Efron B, Tibshirani RJ. An introduction to the bootstrap. United States of America: CRC Press; 1994. p. 15.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc. 1995;57:289–300.
Calhoun VD, Miller R, Pearlson G, Adalı T. The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery. Neuron. 2014;84(2):262–74.
Liu J, Liao X, Xia M, He Y. Chronnectome fingerprinting: identifying individuals and predicting higher cognitive functions using dynamic brain connectivity patterns. Hum Brain Mapp. 2018;39(2):902–15.
Iakovidou ND, Laskaris NA, Tsichlas C, Manolopoulos Y, Christodoulakis M, Papathanasiou ES, Papacostas SS, Mitsis GD. A symbolic dynamics approach to Epileptic Chronnectomics: Employing strings to predict crisis onset. Theoretical Computer Science. 2018;710:116–25.
Mahyari AG, Zoltowski DM, Bernat EM, Aviyente S. A tensor decomposition-based approach for detecting dynamic network states from EEG. IEEE Trans Biomed Eng. 2017;64(1):225–37.
Bruña R, Maestú F, Pereda E. Phase Locking Value revisited: teaching new tricks to an old dog. ArXiv preprint arXiv. 2017;1710:08037.
Gordan M, Kotropoulos C, Pitas I. A temporal network of support vector machine classifiers for the recognition of visual speech. InHellenic Conference on Artificial Intelligence. Berlin, Heidelberg: Springer; 2002. p. 355–65.
Bardideh M, Razzazi F, Ghassemian H. An SVM based confidence measure for continuous speech recognition. InSignal Processing and Communications, 2007. ICSPC 2007. IEEE International Conference 2007 (pp. 1015–1018). IEEE.
Hsu SH, Mullen TR, Jung TP, Cauwenberghs G. Real-time adaptive EEG source separation using online recursive independent component analysis. IEEE Trans Neural Syst Rehabil Eng. 2016;24(3):309–19.
Yong X, Fatourechi M, Ward RK, Birch GE. Automatic artefact removal in a self-paced hybrid brain-computer interface system. J Neuroeng Rehabil. 2012;9(1):50.