Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces - Publication - Bridge of Knowledge


Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces


Background A lack of communication with people suffering from acquired brain injuries may lead to drawing erroneous conclusions regarding the diagnosis or therapy of patients. Information technology and neuroscience make it possible to enhance the diagnostic and rehabilitation process of patients with traumatic brain injury or post-hypoxia. In this paper, we present a new method for evaluation possibility of communication and the assessment of such patients’ state employing future generation computers extended with advanced human–machine interfaces. Methods First, the hearing abilities of 33 participants in the state of coma were evaluated using auditory brainstem response measurements (ABR). Next, a series of interactive computer-based exercise sessions were performed with the therapist’s assistance. Participants’ actions were monitored with an eye-gaze tracking (EGT) device and with an electroencephalogram EEG monitoring headset. The data gathered were processed with the use of data clustering techniques. Results Analysis showed that the data gathered and the computer-based methods developed for their processing are suitable for evaluating the participants’ responses to stimuli. Parameters obtained from EEG signals and eye-tracker data were correlated with Glasgow Coma Scale (GCS) scores and enabled separation between GCS-related classes. The results show that in the EEG and eye-tracker signals, there are specific consciousness-related states discoverable. We observe them as outliers in diagrams on the decision space generated by the autoencoder. For this reason, the numerical variable that separates particular groups of people with the same GCS is the variance of the distance of points from the cluster center that the autoencoder generates. The higher the GCS score, the greater the variance in most cases. The results proved to be statistically significant in this context. Conclusions The results indicate that the method proposed may help to assess the consciousness state of participants in an objective manner.


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Biomedical Engineering Online no. 19, pages 1 - 26,
ISSN: 1475-925X
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Bibliographic description:
Czyżewski A., Kurowski A., Odya P., Szczuko P.: Multifactor consciousness level assessment of participants with acquired brain injuries employing human–computer interfaces// Biomedical Engineering Online -Vol. 19,iss. 1 (2020), s.1-26
Digital Object Identifier (open in new tab) 10.1186/s12938-019-0746-y
Bibliography: test
  1. Peeters W, van den Brande R, Polinder S, Brazinova A, Steyerberg EW, Lingsma HF, Maas AIR. Epidemiology of trau- matic brain injury in Europe. Acta Neurochir. 2015;157(10):1683-96. https :// 1-015-2512-7. open in new tab
  2. Lancioni GE, Bosco A, O'Reilly MF, et al. Assessment and intervention with patients with severe disorders of con- sciousness. Adv Neurodev Disord. 2017;1(3):196-202. https :// 2-017-0025-5. open in new tab
  3. Juan E, Nguissi NAN, Tzovara A, Viceic D, Rusca M, Oddo M, Rossetti AO, De Lucia M. Evidence of trace conditioning in comatose patients revealed by the reactivation of EEG responses to alerting sounds. NeuroImage. 2016;141:530- 41. https :// image .2016.07.039. open in new tab
  4. Wang X, Fu R, Xia X, et al. Spatial properties of mismatch negativity in patients with disorders of consciousness. Neurosci Bull. 2018;34(4):700-8. https :// 4-018-0260-4. open in new tab
  5. Lugo ZR, Quitadamo LR, Bianchi L, et al. Cognitive processing in non-communicative patients: what can event- related potentials tell us? Front Hum Neurosci. 2016. https :// .2016.00569 . open in new tab
  6. Hakozaki M, Tajino T, Yamada H, Hasegawa O, Tasaki K, Watanabe K, Konno S. Radiological and pathological characteristics of giant cell tumor of bone treated with denosumab. Diagn Pathol. 2014;9(11):1-6. https ://doi. org/10.1186/1746-1596-9-111. open in new tab
  7. Mariën P, Beaton A. The enigmatic linguistic cerebellum: clinical relevance and unanswered questions on nonmotor speech and language deficits in cerebellar disorders. Cerebellum Ataxias. 2014;1(12):1-6. https ://doi. org/10.1186/2053-8871-1-12. open in new tab
  8. Yan J, Cheng JL, Li CF, Lian YB, Zheng Y, Zhang XP, Wang CY. The findings of CT and MRI in patients with metanephric adenoma. Diagn Pathol. 2016;11(104):1-7. https :// 0-016-0535-x. open in new tab
  9. Harrison AH, Connolly JF. Finding a way in: a review and practical evaluation of fMRI and EEG for detection and assessment in disorders of consciousness. Neurosci Biobehav Rev. 2013;37(8):1403-19. https :// neubi orev.2013.05.004. open in new tab
  10. Bruno M-A, Vanhaudenhuyse A, Schnakers C, Boly M, Gosseries O, Demertzi A, Laureys S. Visual fixation in the vegetative state: an observational case series PET study. BMC Neurol. 2010;10(35):1-6. https ://doi. org/10.1186/1471-2377-10-35. open in new tab
  11. Lundervold A. On consciousness, resting state fMRI, and neurodynamics. Nonlinear Biomed Phys. 2010;4(1):1-18. https :// open in new tab
  12. Weiss N, Galanaud D, Carpentier A, Naccache L, Puybasset L. Clinical review: prognostic value of magnetic reso- nance imaging in acute brain injury and coma. Crit Care. 2007;11(5):1-12. https :// 7. Page 25 of 26 open in new tab
  13. Czyżewski et al. BioMed Eng OnLine (2020) 19:2 open in new tab
  14. Di Perri C, Thibaut A, Heine L, Soddu A, Demertzi A, Laureys S. Measuring consciousness in coma and related states. World J Radiol. 2014;6(8):589-97. https :// open in new tab
  15. Iversen I, Ghanayim N, Kübler A, Neumann N, Birbaumer N, Kaiser J. Conditional associative learning examined in a paralyzed patient with amyotrophic lateral sclerosis using brain-computer interface technology. Behav Brain Funct. 2008;4(53):1-14. https :// open in new tab
  16. Vessoyan K, Steckle G, Easton B, Nichols M, Mok Siu V, McDougall J. Using eye-tracking technology for com- munication in Rett syndrome: perceptions of impact. Augment Altern Commun. 2018;34(3):230-41. https ://doi. org/10.1080/07434 618.2018.14628 48. open in new tab
  17. Malinowska U, Chatelle C, Marie-Aurélie B, Noirhomme Q, Laureys S, Durka P. Electroencephalographic pro- files for differentiation of disorders of consciousness. Biomed Eng Online. 2013;12(109):1-18. https ://doi. org/10.1186/1475-925X-12-109. open in new tab
  18. Przybyło J, Kańtoch E, Augustyniak P. Eyetracking-based assessment of affect-related decay of human performance in visual tasks. Future Gener Comput Syst. 2019;92:504-15. https :// e.2018.02.012. open in new tab
  19. Mena JH, Sanchez AI, Rubiano AM, Peitzman AB, Sperry JL, Gutierrez MI, Puyana JC. Effect of the modified Glasgow Coma Scale score criteria for mild traumatic brain injury on mortality prediction: comparing classic and modified Glasgow Coma Scale score model scores of 13. J Trauma. 2011;71(5):1185-93. https :// e3182 3321f 8. open in new tab
  20. Szczuko P. Rough set-based classification of EEG signals related to real and imagery motion. In: Proceedings of 2016 signal processing: algorithms, architectures, arrangements, and applications (SPA), Poznań, Poland; 2016. https ://doi. org/10.1109/SPA.2016.77635 83. open in new tab
  21. Szczuko P. Real and imaginary motion classification based on rough set analysis of EEG signals for multimedia applications. Multimed Tools Appl Multimedia Tools Appl. 2017;76(24):25697-711. https :// 2-017-4458-7. open in new tab
  22. Szczuko P, Lech M, Czyżewski A. Comparison of classification methods for EEG signals of real and imaginary motion. In: Stańczyk U, Zielosko B, Jain L, editors. Advances in feature selection for data and pattern recognition, vol. 138., Intelligent Systems Reference LibraryCham: Springer; 2018. https :// -6_12. open in new tab
  23. Vidaurre C, Blankertz B. Towards a cure for BCI illiteracy. Brain Topogr. 2010;23(2):194-8. https :// s1054 8-009-0121-6. open in new tab
  24. Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet. 1974;304(7872):81-4. https :// -6736(74)91639 -0. open in new tab
  25. McNett M. A review of the predictive ability of Glasgow Coma Scale scores in head-injured patients. J Neurosci Nurs. 2007;39(2):68-75. https :// 517-20070 4000-00002 . open in new tab
  26. Baltrušaitis T, Ahuja C, Morency L. Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell. 2019;41(2):423-43. https :// .2018.27986 07. open in new tab
  27. Jiquan N, Khosla A, Kim M, Nam J, Lee H, Ng AY. Multimodal deep learning. In: ICML; 2011.
  28. Cadena C, Dick AR, Reid ID. Multi-modal auto-encoders as joint estimators for robotics scene understanding. In: Proceedings of robotics: science and systems conference, Arbor, Michigan, USA; 2016. https :// / RSS.2016.XII.041. open in new tab
  29. Droniou A, Ivaldi S, Sigaud O. Deep unsupervised network for multimodal perception, representation and classifica- tion. Robot Auton Syst. 2015;71:83-98. https :// .2014.11.005. open in new tab
  30. Choi J-H, Lee J-S. EmbraceNet: a robust deep learning architecture for multimodal classification. Inf Fusion. 2019;51:259-70. https :// s.2019.02.010. open in new tab
  31. Macaš M, Vavrecka M, Gerla V, Lhotská L. Classification of the emotional states based on the EEG signal processing. In: Proceedings of the 9th international conference in information technology and applications in biomedicine, Larnaca, Cyprus. 2009;1-4. 29. open in new tab
  32. Jenke R, Peer A, Buss M. Feature extraction and selection for emotion recognition from EEG. IEEE Trans Affect Com- put. 2014;5(3):327-39. https :// .2014.23398 34. open in new tab
  33. Koelstra S, Muhl C, Soleymani M, Lee J-S, Yazdani A, et al. DEAP: a database for emotion analysis using physiological signals. IEEE Trans Affect Comput. 2011;3(1):18-31. https :// open in new tab
  34. Orhan U, Hekim M, Ozer M. EEG signals classification using the K-means clustering and multilayer perceptron neural network model. Expert Syst Appl. 2011;38(10):13475-81. https :// open in new tab
  35. Gürkök H, Nijholt A. Brain-computer interfaces for multimodal interaction: a survey and principles. Int J Hum Com- put Interact. 2011;28(5):292-307. https :// 318.2011.58202 2. open in new tab
  36. Leamy DJ, Kocijan J, Domijan K, Duffin J, Roche RAP, Commins S, Collins R, Ward TE. An exploration of EEG features during recovery following stroke-implications for BCI-mediated neurorehabilitation therapy. J Neuroeng Rehabilit. 2014;11(9):1-16. https :// open in new tab
  37. Li Y, Pan J, He Y, Wang F, Laureys S, Xie Q, Yu R. Detecting number processing and mental calculation in patients with disorders of consciousness using a hybrid brain-computer interface system. BMC Neurol. 2015;15(259):1-14. https :// 3-015-0521-z. open in new tab
  38. Giacino JT, Kalmar K, Whyte J. The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility. Arch Phys Med Rehabil. 2004;85(12):2020-9. https :// open in new tab
  39. Rodrigues RA, Busssiere M, Froeschl M, Nathan HJ. Auditory-evoked potentials during coma: do they improve our prediction of awakening in comatose patients? J Crit Care. 2014;29(1):93-100. https :// jcrc.2013.08.020. open in new tab
  40. Skoe E, Kraus N. Auditory brainstem response to complex sounds: a tutorial. Ear Hear. 2010;31(3):302-24. https ://doi. org/10.1097/AUD.0b013 e3181 cdb27 2. open in new tab
  41. Echodia. Echodia Elios user guide. 2015. ver. 2.1.1. Saint Beauzire, France; 2015.
  42. Tobii. Tobii-EyeX controller technical specification; 2017. http://www.tobii .com/xperi ence/produ cts/#Speci ficat ion.
  43. Emotiv. Emotiv insight user manual, Revision 1.0, user manual provided by the manufacturer with the hardware equipment; 2015.
  44. Czyżewski et al. BioMed Eng OnLine (2020) 19:2 • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year research ? Choose BMC and benefit from: open in new tab
  45. Emotiv. Emotiv EPOC user manual. https ://emoti v.zende les/20122 2455-Where -can-I-find-a- user-manua l. Accessed 14 Nov 2018. open in new tab
  46. Emotiv. Manufacturers web site available at https ://emoti v.zende open in new tab
  47. Vincent P, Larochelle H, Bengio Y, Manzagol PA. Extracting and composing robust features with denoising autoen- coders. In: Proceedings of the 25th international conference on Machine learning ICML '08, New York, NY, USA. 2008;1096-103. https :// 56.13902 94. open in new tab
  48. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge: MIT Press; 2016.
  49. Jones E, et al. SciPy. Open source scientific tools for python; 2001. http://www.scipy .org. Accessed 21 Dec 2018.
  50. NumPy. Package for scientific computing with Python; 2017. http://www.numpy .org. Accessed 21 Dec 2018.
  51. Chollet F. Keras machine learning library; 2015. Software available at https ://keras .io.
  52. Abadi M, et al. TensorFlow: large-scale machine learning on heterogeneous systems; 2015. Software available from https ://www.tenso rflow .org. open in new tab
  53. Hinton GE, Salakhutdinov RR. Reducing the dimensionality of data with neural networks. Science. 2006. https ://doi. org/10.1126/scien ce.11276 47. open in new tab
  54. Wang Y, Liu S, Afzal N, Rastegar-Mojarad M, Wang L, Shen F, Liu H. A comparison of word embeddings for biomedi- cal natural language processing. J Biomed Inform. 2018;87:12-20. https :// open in new tab
  55. Tran T, Luo W, Phung D, Gupta S, Rana S, Kennedy R, Larkins A, Venkatesh S. A framework for feature extraction from hospital medical data with applications in risk prediction. BMC Bioinform. 2014;15:6596. https :// s1285 9-014-0425-8. open in new tab
  56. Buciński A, Bączek T, Krysiński J, Szoszkiewicz R, Załuski J. Clinical data analysis using artificial neural networks (ANN) and principal component analysis (PCA) of patients with breast cancer after mastectomy. Rep Pract Oncol Radio- ther. 2007;12(1):9-17. https :// -1367(10)60036 -3. open in new tab
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