Skip to main content
SpringerLink
Log in

A Review of Brain-Computer Interface (BCI) System: Advancement and Applications

  • Chapter
  • First Online:
Enabling Person-Centric Healthcare Using Ambient Assistive Technology

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1108))

  • 321 Accesses

Abstract

Brain-Computer Interface (BCI) is a cutting-edge and diverse area of ongoing research based on neuroscience, signal processing, biomedical sensors, and hardware. Numerous ground-breaking studies have been conducted in this area over the last few decades. However, the BCI domain has yet to be the subject of a thorough examination. As a result, this study provides an in-depth analysis of the BCI issue. In addition, this research supports this field's importance by examining several BCI applications. Finally, each BCI system component is briefly explained, including procedures, datasets, feature extraction techniques, evaluation measurement matrices, current BCI algorithms, and classifiers. A basic overview of BCI sensors is also presented. Next, the study describes some unsolved BCI issues and possible remedies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Berger, H. (1929). Über das Elektrenkephalogramm des Menschen. Archives für Psychiatrie, 87, 527–570. https://doi.org/10.1007/BF01797193

  2. Lindsley, D. B. (1952). Psychological phenomena and the electroencephalogram. Electroencephalography and Clinical Neurophysiology, 4(4), 443–456. https://doi.org/10.1016/0013-4694(52)90075-8. https://www.sciencedirect.com/science/article/pii/0013469452900758

  3. Vidal, J. J. (1973). Toward direct brain-computer connection. Annual Review of Biophysics and Bioengineering, 2, 157–180.

    Article  Google Scholar 

  4. Zeng, F. G., Rebscher, S., Harrison, W., Sun, X., & Feng, H. (2008). Cochlear implants: System design, integration, and evaluation. IEEE Reviews in Biomedical Engineering, 1, 115–142. https://doi.org/10.1109/RBME.2008.2008250. Epub 2008 November 5. PMID: 19946565; PMCID: PMC2782849.

  5. Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors, 12, 1211–1279. https://doi.org/10.3390/s120201211

    Article  Google Scholar 

  6. Abiri, R., Zhao, X., Jiang, Y., Sellers, E. W., & Borhani, S. (2019). A thorough analysis of brain-computer interaction paradigms based on EEG. Journal of Neural Engineering, 16(1), 011001.

    Google Scholar 

  7. Tiwari, N., Edla, D. R., Dodia, S., & Bablani, A. (2018). Brain computer interface: A comprehensive survey. Biologically Inspired Cognitive Architectures, 26, 118–129.

    Article  Google Scholar 

  8. Vasiljevic, G. A. M., & de Miranda, L. C. (2020). Brain-computer interface games based on consumer-grade EEG devices: A comprehensive literature analysis. International Journal of Human Computer Interaction, 36, 105–142.

    Article  Google Scholar 

  9. Panov, F., Oxley, T., Yaeger, K., Oermann, E. K., Opie, N. L., & Martini, M. L. (2020). A thorough literature evaluation of sensor modalities for brain-computer interface technologies. Neurosurgery, 86, E108–E117.

    Article  Google Scholar 

  10. Bablani, A., Edla, D. R., Tripathi, D., & Cheruku, R. (2019). Brain-computer interface survey: An emergent computational intelligence paradigm. ACM Computing Surveys (CSUR), 52, 20.

    Google Scholar 

  11. Fleury, M., Lioi, G., Barillot, C., & Lécuyer, A. (2020). A survey of haptic feedback's application to neurofeedback and brain-computer interfaces. Frontiers in Neuroscience, 14, 528.

    Google Scholar 

  12. Yoo, S. G., Hernández-lvarez, M., & Torres, P. E. P. (2020). EEG-based BCI emotion recognition: A survey. Sensors, 20, 5083.

    Google Scholar 

  13. Wang, X., Zhang, Y., Zhang, X., Yao, L., Monaghan, J. J., & Mcalpine, D. (2021). A review of recent developments and uncharted territory in deep learning-based non-invasive brain signals. Journal of Neural Engineering, 18, 031002.

    Google Scholar 

  14. Gu, X., Cao, Z., Jolfaei, A., Xu, P., Wu, D., Jung, T. P., & Lin, C. T. (2021). EEG-based brain-computer interfaces (BCIs): A summary of contemporary works on signal detecting technologies, computational intelligence techniques, and their applications. IEEE/ACM Transactions on Bioinformatics and Computing.

    Google Scholar 

  15. Nijholt, A. (2016). Brain-computer interaction in the future (keynote paper). In 5th International Conference on Informatics, Electronics, and Vision (ICIEV), Dhaka, Bangladesh, 13–14 May 2016, pp. 156–161.

    Google Scholar 

  16. Padfield, N., Zabalza, J., Zhao, H., Masero, V., & Ren, J. (2019). EEG-based brain-computer interfaces with motor imagery: Methods and problems. Sensors, 19, 1423.

    Google Scholar 

  17. Hara, Y. (2015). Brain plasticity and rehabilitation in stroke patients. Journal of Nippon Medical School, 82, 4–13.

    Article  Google Scholar 

  18. Bousseta, R., El Ouakouak, I., Gharbi, M., & Regragui, F. (2018). EEG based brain computer interface for controlling a robot arm’s movement with thoughts. Irbm, 39, 129–135.

    Article  Google Scholar 

  19. Perales, F. J., Riera, L., Ramis, S., & Guerrero, A. (2019). Using binaural auditory stimulation, a VR system for pain management is evaluated. Medical Tool Applications, 78, 32869–32890.

    Article  Google Scholar 

  20. Shim, M., Hwang, H. J., Kim, D. W., Lee, S. H., & Im, C. H. (2016). Machine-learning-based schizophrenia diagnosis utilising sensor-level and source-level EEG characteristics. Schizophrenia Research, 176, 314–319.

    Article  Google Scholar 

  21. Sharanreddy, M., & Kulkarni, P. (2013). Identification of primary brain tumour using wavelet transform and neural network in EEG signal. International Journal of Biomedical Research, 4, 2855–2859.

    Google Scholar 

  22. Poulos, M., Felekis, T., & Evangelou, A. (2012). Is it feasible to obtain a breast cancer fingerprint using EEG analysis? Medical Hypotheses, 78, 711–716.

    Google Scholar 

  23. Christensen, J. A., Koch, H., Frandsen, R., Kempfner, J., Arvastson, L., Christensen, S. R., Sorensen, H. B., Jennum, P. (2013). Patients with iRBD and Parkinson's disease are classified based on their eye movements during sleep. In Proceedings of the 2013 IEEE Engineering in Medicine and Biology Society (EMBC) 35th Annual International Conference, Osaka, Japan, 3–7 July 2013, pp. 441–444.

    Google Scholar 

  24. Mikoajewska, E., & Mikoajewski, D. (2014). The potential of brain-computer interface applications in children. Open Medicine, 9(74–79).

    Google Scholar 

  25. Mane, R., Chouhan, T., & Guan, C. (2020). BCI for stroke rehabilitation: Motor and beyond. Journal of Neural Engineering, 17, 041001.

    Google Scholar 

  26. Van Dokkum, L., Ward, T., & Laffont, I. (2015). Brain-computer interfaces for neurorehabilitation: Its present standing as a post-stroke rehabilitation method. Annals of Physical and Rehabilitation Medicine, 58, 3–8.

    Article  Google Scholar 

  27. Soekadar, S. R., Silvoni, S., Cohen, L. G., & Birbaumer, N. (2015). Brain-machine interfaces in stroke neurorehabilitation. In Clinical systems neuroscience (pp. 3–14). Springer.

    Google Scholar 

  28. Beudel, M., & Brown, P. (2016). Adaptive deep brain stimulation for Parkinson’s disease. Parkinsonism & Related Disorders, 22, S123–S126.

    Article  Google Scholar 

  29. Stein, A., Yotam, Y., Puzis, R., Shani, G., & Taieb-Maimon, M. (2018). EEG-triggered dynamic difficulty modification for multiplayer games. Entertainment Computing, 25, 14–25.

    Article  Google Scholar 

  30. Zhang, B., Wang, J., & Fuhlbrigge, T. (2010). A review of commercial brain-computer interface technologies from the standpoint of industrial robotics. In Proceedings of the 2010 IEEE International Conference on Automation and Logistics, 16–20 August 2010, Hong Kong, China, pp. 379–384.

    Google Scholar 

  31. Todd, D., McCullagh, P. J., Mulvenna, M. D., & Lightbody, G. (2012). Examining the use of brain-computer connection to boost creativity. In Proceedings of the 3rd International Augmented Human Conference, Megève, France, March 8–9, pp. 1–8.

    Google Scholar 

  32. Binias, B., Myszor, D., & Cyran, K. A. (2018). A machine learning technique to detecting a pilot's reaction to unexpected occurrences using EEG data. Computer Intelligence and Neuroscience, 2703513.

    Google Scholar 

  33. Panoulas, K. J., Hadjileontiadis, L. J., & Panas, S. M. (2010). Brain-computer interface (BCI): Types, processing views, and applications (pp. 299–321). Springer.

    Google Scholar 

  34. Flink, R., & Kuruvilla, A. (2003). Intraoperative electrocorticography in epilepsy surgery: Helpful or not? Seizure, 12, 577–584.

    Article  Google Scholar 

  35. Homan, R. W., Herman, J., & Purdy, P. Cerebral implantation of international 10–20 system electrodes.

    Google Scholar 

  36. Wilson, J. A., Felton, E. A., Garell, P. C., Schalk, G., & Williams, J. C. (2006). ECoG variables underlie multimodal control of a brain-computer interface. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 14, 246–250.

    Article  Google Scholar 

  37. Weiskopf, N., Veit, R., Erb, M., Mathiak, K., Grodd, W., Goebel, R., & Birbaumer, N. (2003). Methods and example data for physiological self-regulation of regional brain activity using real-time functional magnetic resonance imaging (fMRI). NeuroImage, 19, 577–586.

    Article  Google Scholar 

  38. Ramadan, R. A., & Vasilakos, A. V. (2017). Brain computer interface: Control signals review. Neurocomputing, 223, 26–44.

    Article  Google Scholar 

  39. Huisman, T. (2010). Diffusion-weighted and diffusion tensor imaging of the brain simplified. Cancer Imaging, 10, S163.

    Google Scholar 

  40. Borkowski, K., & Krzyzak, A. T. (2018). Error analysis and correction in DTI-based tractography due to diffusion gradient inhomogeneity. Journal of Magnetic Resonance, 296, 5–11.

    Article  Google Scholar 

  41. Purnell, J., Klopfenstein, B., Stevens, A., Havel, P. J., Adams, S., Dunn, T., Krisky, C., & Rooney, W. (2011). Brain functional magnetic resonance imaging response to glucose and fructose infusions in humans. Diabetes, Obesity & Metabolism, 13, 229–234.

    Article  Google Scholar 

  42. Lahane, P., Jagtap, J., Inamdar, A., Karne, N., & Dev, R. (2019). A look at current developments in EEG-based Brain-Computer Interface. In Proceedings of the 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), 21–23 February 2019, Chennai, India, pp. 1–6.

    Google Scholar 

  43. Deng, S., Winter, W., Thorpe, S., & Srinivasan, R. (2011). EEG Surface Laplacian with realistic head geometry. International Journal of Bioelectromagn., 13, 173–177.

    Google Scholar 

  44. Shaw, L., & Routray, A. (2016). Statistical features extraction for multivariate pattern analysis in meditation EEG using PCA. In Proceedings of the 2016 IEEE EMBS International Student Conference (ISC), May 29–31, 2016, Ottawa, ON, Canada, pp. 1–4.

    Google Scholar 

  45. Subasi, A., & Gursoy, M. I. (2010). Classification of EEG signals using PCA, ICA, LDA, and support vector machines. Expert Systems with Applications, 37, 8659–8666.

    Article  Google Scholar 

  46. Jannat, N., Sibli, S. A., Shuhag, M. A. R., & Islam, M. R. (2020). EEG motor signal analysis-based improved motor activity recognition using optimal denoising algorithm (pp. 125–136). Springer.

    Google Scholar 

  47. Vahabi, Z., Amirfattahi, R., & Mirzaei, A. (2011). Improving the P300 wave of BCI systems using negentropy in adaptive wavelet denoising. Medical Signals and Sensors, 1, 165.

    Google Scholar 

  48. Johnson, M. T., Yuan, X., & Ren, Y. (2007). Adaptive wavelet thresholding for speech signal augmentation. Speech Communication, 49, 123–133.

    Article  Google Scholar 

  49. Islam, M. R., Rahim, M. A., Akter, H., Kabir, R., & Shin, J. (2018). Using EEG information, optimal IMF selection of EMD for sleep problem diagnosis. In Proceedings of the 3rd International Conference on Applications in Information Technology, 1–3 November 2018, Aizu-Wakamatsu, Japan, pp. 96–101.

    Google Scholar 

  50. Bashashati, A., Fatourechi, M., Ward, R. K., & Birch, G. E. (2007). A overview of signal processing techniques used in electrical brain signals-based brain-computer interfaces. Journal of Neural Engineering, 4, R32.

    Article  Google Scholar 

  51. Aborisade, D., Ojo, J., Amole, A., & Durodola, A. (2014). Compare textural characteristics generated from GLCM for ultrasound liver image categorization. International Journal of Computer Trends and Technology, 11, 6.

    Google Scholar 

  52. He, B., Yuan, H., Meng, J., & Gao, S. (2020). Brain-computer interfaces (pp. 131–183). Springer.

    Google Scholar 

  53. Phadikar, S., Sinha, N., & Ghosh, R. (2019). A overview of feature extraction strategies for emotion identification using EEG. In International Conference on Innovation in Contemporary Science and Technology (pp. 31–45). Springer.

    Google Scholar 

  54. Vaid, S., Singh, P., & Kaur, C. (2015). EEG signal analysis for BCI interface: A review. In Proceedings of the 2015 5th International Conference on Advanced Computing and Communication Technologies, Haryana, India, 21–22 February 2015, pp. 143–147.

    Google Scholar 

  55. Smith, J. O. (2007). Mathematics of the Discrete Fourier Transform (DFT): With audio applications. W3K Publishing.

    Google Scholar 

  56. Zabidi, A., Mansor, W., Lee, Y., & Fadzal, C. C. W. (2012). Short-time fourier transform analysis of the EEG signal obtained during simulated writing. In Proceedings of the 2012 International Conference on System Engineering and Technology (ICSET), Bandung, Indonesia, September 11–12, pp. 1–4.

    Google Scholar 

  57. Al-Fahoum, A. S., & Al-Fraihat, A. A. (2014). Techniques of extracting EEG signal characteristics using linear analysis in the frequency and time-frequency domains. International School Research Notices, 730218.

    Google Scholar 

  58. Djamal, E. C., Abdullah, M. Y., & Renaldi, F. (2017). Brain computer interface game control utilising rapid fourier transform and learning vector quantization. Journal of Telecommunication Electronic and Computer Engineering, 9, 71–74.

    Google Scholar 

  59. Conneau, A. C., & Essid, S. (2014). Evaluating novel spectral characteristics for emotion identification using EEG. In Proceedings of the 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Florence, Italy, 4–9 May 2014, pp. 4698–4702.

    Google Scholar 

  60. Petropulu, A. P. (2018). Higher-order spectral analysis. Handbook of Digital Signal Processing.

    Google Scholar 

  61. LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., He, B. (2013). Non-invasive motor imagery-based brain-computer interface quadcopter control in three dimensions. Journal of Neural Engineering, 10, 046003.

    Google Scholar 

  62. Darvishi, S., & Al-Ani, A. (2007). Brain-computer interface analysis utilising continuous wavelet transform and adaptive neuro-fuzzy classifier. In Proceedings of the IEEE Engineering in Medicine and Biology Society's 29th Annual International Conference, Lyon, France, 22–26 August 2007, pp. 3220–3223.

    Google Scholar 

  63. Nivedha R., Brinda M., Vasanth D., Anvitha M., & Suma, K. (2017). SVM and PSO are used to recognise emotions in EEG data. In Proceedings of the 2017 International Conference on Intelligent Computing, Instrumentation, and Control Technologies (ICICICT), 6–7 July 2017, Kerala, India, pp. 1597–1600.

    Google Scholar 

  64. Xanthopoulos, P., Pardalos, P. M., & Trafalis, T. B. (2013). Linear discriminant analysis. In Robust data mining (pp. 27–33). Springer.

    Google Scholar 

  65. Temiyasathit, C. (2014). Improving four-class classification performance for motorimagery-based brain-computer interface. In 2014 International Conference on Computer, Information, and Telecommunication Systems (CITS). IEEE

    Google Scholar 

  66. Millan, J. R., Renkens, F., Mourino, J., & Gerstner, W. (2004). Human EEG-based non-invasive brain-actuated control of a mobile robot. IEEE Transactions on Biomedical Engineering, 51, 1026–1033.

    Article  Google Scholar 

  67. Sridhar, G., & Rao, P. M. (2012). A neural network method for EEG classification in BCI. International Journal of Computer Science Telecommunications, 3, 44–48.

    Google Scholar 

  68. Lu, N., Li, T., Ren, X., & Miao, H. (2017). A deep learning approach based on limited Boltzmann machines for motor imagery categorization. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25, 566–576.

    Article  Google Scholar 

  69. Zhao, Y., Yao, S., Hu, S., Chang, S., Ganti, R., Srivatsa, M., Li, S., & Abdelzaher, T. (2017). On the enhancement of identifying EEG recordings using neural networks (Big Data). In 2017 IEEE International Conference on Big Data. IEEE

    Google Scholar 

  70. Mohamed, E. A., Yusoff, M. Z. B., Selman, N. K., & Malik, A. S. (2014). Wavelet transform enhancement of EEG signals in brain computer interface. International Journal of Information and Electronics Engineering, 4, 234

    Google Scholar 

  71. Sakhavi, S., Guan, C., & Yan, S. (2015). Motor imagery categorization using a parallel convolutional-linear neural network. In Proceedings of the 2015 23rd European Signal Processing Conference (EUSIPCO), Nice, France, 31 August–4 September 2015, pp. 2736–2740.

    Google Scholar 

  72. Carrera-Leon, O., Ramirez, J. M., Alarcon-Aquino, V., Baker, M., D'Croz-Baron, D., & Gomez-Gil, P. (2012). A motor imagery BCI experiment using wavelet analysis and feature extraction from spatial patterns. In Proceedings of the 2012 Workshop on Engineering Applications, 2–4 May 2012, Bogota, Colombia, pp. 1–6.

    Google Scholar 

  73. Yang, J., Yao, S., & Wang, J. (2018). Deep fusion feature learning network for MI-EEG categorization. IEEE Access, 6, 79050–79059.

    Article  Google Scholar 

  74. Kanoga, S., Kanemura, A., & Asoh, H. (2018). A investigation of characteristics and classifiers in single-channel EEG-based motor imagery BCI. In Proceedings of the 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, November 26–29, pp. 474–478.

    Google Scholar 

  75. Yan, S., Sakhavi, S., & Guan, C. (2015). Parallel convolutional-linear neural network for motor imagery categorization. In 23rd European Signal Processing Conference (EUSIPCO). IEEE.

    Google Scholar 

  76. Yang H., and co. (2015). The use of convolutional neural networks and enhanced CSP features for multiclass motor imagery categorization of EEG data. In 2015 IEEE 37th Annual International Conference on Engineering in Medicine and Biology Society (EMBC). IEEE.

    Google Scholar 

  77. Choi, Y.-S., & Lee, H. K. (2018) A convolution neural networks technique for categorization of motor imagery EEG based on wavelet time-frequency picture. In International Conference on Information Networking (ICOIN). IEEE.

    Google Scholar 

  78. Ko, W., Yoon, J., Kang, E., Jun, E., Choi, J. S., & Suk, H. I. (2018). Deep recurrent spatiotemporal neural network for BCI based on motor imagery. In 2018 6th International Conference on Brain-Computer Interface (BCI). IEEE.

    Google Scholar 

  79. Yi, W., Qiu, S., Qi, H., Zhang, L., Wan, B., & Ming, D. (2013). EEG feature comparison and categorization of simple and complex limb motor imagery. Journal of Neuroengineering and Rehabilitation, 10, 106.

    Article  Google Scholar 

  80. Chen, C.-Y. et al. (2014). A new categorization approach for motor images based on brain-computer interface, neural networks (IJCNN). IEEE.

    Google Scholar 

  81. Sagee, G. S., & Hema, S. (2017). EEG feature extraction and classification in multiclass multiuser motor imagery brain computer interface using Bayesian Network and ANN. In IEEE International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT).

    Google Scholar 

  82. Gong, X., Zhang, J., & Yan, C. (2017). Deep convolutional neural network for brain computer interface decoding based on motor imagery. In IEEE International Conference on Signal Processing, Communications, and Computing (ICSPCC).

    Google Scholar 

  83. Schirrmeister, R. T., Springenberg, J. T., Fiederer, L. D., Glasstetter, M., Eggensperger, K., Tangermann, M., Hutter, F., Burgard, W., & Ball, T. (2017). EEG decoding and visualisation using deep learning using convolutional neural networks. Human Brain Mapping, 38, 5391–5420.

    Google Scholar 

  84. Vesin, J.-M., Garcia, G. N., & Ebrahimi, T. (2003). Neural engineering categorization of EEG support vectors in the Fourier and time-frequency domains. In Proceedings of the First International IEEE EMBS Conference on Neural Engineering. IEEE.

    Google Scholar 

  85. Carrera-Leon, O., Ramirez, J. M., Alarcon-Aquino, V., Baker, M., D’Croz-Baron, D., & Gomez-Gil, P. (2012). A motor imagery BCI experiment using wavelet analysis and extraction of spatial pattern features. In 2012 Workshop on Engineering Applications (pp. 1–6). IEEE.

    Google Scholar 

  86. Mohamed, E. A., Yusoff, M. Z. B., Selman, N. K., & Malik, S. A. (2014). Using wavelet transform to boost EEG signals in the brain-computer interface. International Journal of Information and Electronics Engineering, 4(3).

    Google Scholar 

  87. Jun, Y., Shaowen, Y., & Jin, W. (2018). Deep learning fusion of features for MIEEG categorization. IEEE Access, 6, 79050–79059.

    Article  Google Scholar 

  88. Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R. (2017). Destining new shores! Addressing BCI design pitfalls. Brain Computing Interfaces, 4, 60–73.

    Article  Google Scholar 

  89. Kirar, J. S., & Agrawal, R. K. (2018). Relevant feature selection from a mix of spectral-temporal and spatial variables for categorization of motor imagery EEG. Journal of Medical Systems, 42, 78.

    Google Scholar 

  90. He, B., LaFleur, K., Cassady, K., Doud, A., Shades, K., Rogin, E., & Rogin, E. (2013). Control of a quadcopter in three-dimensional space via a non-invasive brain–computer interface based on motor imagery. Jounal of Neural Engineering, 10, 046003.

    Google Scholar 

  91. Praveen, S. P., Murali Krishna, T. B., Anuradha, C. H., Mandalapu, S. R., Sarala, P., & Sindhura, S. (2022). A robust framework for handling health care information based on machine learning and big data engineering techniques. International Journal of Healthcare Management, 1–18. https://doi.org/10.1080/20479700.2022.2157071

  92. Lawhern, V. J., Solon, A. J., Waytowich, N. R., Gordon, S. M., Hung, C. P., & Lance, B. J. (2018). A convolutional neural network for EEG-based brain–computer interactions. Journal of Neural Engineering, 15, 056013.

    Google Scholar 

  93. Liu, Y. H., Wang, S. H., & Hu, M. R. (2016). An autonomous P300 healthcare brain-computer interface system with SSVEP-based switching control and kernel FDA+ SVM-based detector. Applied Sciences, 6, 142.

    Article  Google Scholar 

  94. Zhang, X., Li, J., Liu, Y., Zhang, Z., Wang, Z., Luo, D., Zhou, X., Zhu, M., Salman, W., Hu, G., & Wang, C. (2017). The development of a tiredness detection system for high-speed trains based on the attentiveness of the driver utilising a wireless worn EEG. Sensors, 17, 486.

    Google Scholar 

  95. Belwafi, K., Romain, O., Gannouni, S., Ghaffari, F., Djemal, R., & Ouni, B. (2018). An embedded implementation for brain–computer interface systems based on an adaptive filter bank. Journal of Neuroscience Methods, 305, 1–16.

    Article  Google Scholar 

  96. Tayeb, Z., Fedjaev, J., Ghaboosi, N., Richter, C., Everding, L., Qu, X., Wu, Y., Cheng, G., & Conradt, J. (2019). Validation of deep neural networks for online decoding of motor imagery movements extracted from EEG data. Sensors, 19, 210.

    Article  Google Scholar 

  97. Convolutional neural networks for the identification of P300 with application to brain-computer interfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 433–445.

    Google Scholar 

  98. Tsui, C. S. L., Gan, J. Q., & Roberts, S. J. (2009). A self-paced brain–computer interface for commanding a robot simulator: An online event labelling paradigm and an extended Kalman filter based algorithm for online training. Medical and Biological Engineering and Computing, 47(2), 257–267.

    Google Scholar 

  99. Jin, Z., Zhou, G., Gao, D., & Zhang, Y. (2020). EEG classification with sparse Bayesian extreme learning machine for brain–computer interface. Neural Computing and Applications, 32, 6601–6609.

    Article  Google Scholar 

  100. Zhang, Y., Wang, Y., Zhou, G., Jin, J., Wang, B., Wang, X., & Cichocki, A. (2018). Multi-kernel extreme learning machine for EEG categorization in brain-computer interfaces. Expert System in Artificial Intelligence, 96, 302–310.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majhitar, Sikkim, 737136, India

    Bishal Kumar Gupta, Tawal Kumar Koirala & Jyoti Rai

  2. LTIMindtree, 1 American Row, 3Rd Floor, Hartford, CT, 06103, USA

    Baidyanath Panda

  3. Directorate of Research, Sikkim Manipal University, Gangtok, Sikkim, 737102, India

    Akash Kumar Bhoi

  4. KIET Group of Institutions, Delhi-NCR, Ghaziabad, 201206, India

    Akash Kumar Bhoi

Authors
  1. Bishal Kumar Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tawal Kumar Koirala
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jyoti Rai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Baidyanath Panda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Akash Kumar Bhoi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Akash Kumar Bhoi .

Editor information

Editors and Affiliations

  1. National Research Council, Institute of Information Science and Technologies, Pisa, Italy

    Paolo Barsocchi

  2. Department of Computer Science and Engineering, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, India

    Naga Srinivasu Parvathaneni

  3. KIET Group of Institutions, Ghaziabad, India

    Amik Garg

  4. KIET Group of Institutions, Ghaziabad, India

    Akash Kumar Bhoi

  5. National Research Council, Institute of Information Science and Technologies, Pisa, Italy

    Filippo Palumbo

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gupta, B.K., Koirala, T.K., Rai, J., Panda, B., Bhoi, A.K. (2023). A Review of Brain-Computer Interface (BCI) System: Advancement and Applications. In: Barsocchi, P., Parvathaneni, N.S., Garg, A., Bhoi, A.K., Palumbo, F. (eds) Enabling Person-Centric Healthcare Using Ambient Assistive Technology. Studies in Computational Intelligence, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-031-38281-9_9

Download citation

Publish with us

Policies and ethics

Search

Navigation