Research PaperNo Access

A Low-Cost Real-Time BCI Integration for Automated Door Opening System

Mustafa Guner

The Scientific and Technological, Research Council of Turkey, Istanbul, Turkey

E-mail Address: mustafa.guner1453@gmail.com

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and

Burcu Erkmen

https://orcid.org/0000-0002-5581-9764

Department of Electronics and Communication Engineering, Yıldız Technical University, Istanbul, Turkey

E-mail Address: bkapan@yildiz.edu.tr

Corresponding author.

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https://doi.org/10.1142/S0218126621500304Cited by:3 (Source: Crossref)

Abstract

Based on the ElectroEncephaloGraphy (EEG) signals obtained by noninvasive methods, Brain–Computer Interface (BCI) technologies provide real-time communication of individuals within the cyber-physical systems. However, this research area still needs low-cost solutions to become a research topic widely all around the world. In this paper, a low cost and productive BCI equipment was developed to assist daily living activities for disabled people. The control application of an integrated approach aims to open and close a door equipped with an EEG-based lock system using eye blink signals is developed. The proposed easy-to-use, portable BCI system includes cost-effective hardware/software components, modular and flexible configuration features and Bluetooth-enabled real-time data processing units. The developed BCI hardware is based on Texas Instruments ADS1299 AFE ADC, which features high flexibly in configuring of measure of various biopotentials from the human body using designed active/passive dry electrodes. Besides it is equipped with a built-in real-time data storage unit and accelerometer to make an online diagnosis, and data fusion at the interested signal investigation. Furthermore, desktop software has been implemented in the QT SDK environment, which can integrate remote BCI hardware during configuration. Experimental results of the designed system were compared with the results of the commercial-grade counterpart.

This paper was recommended by Regional Editor Piero Malcovati.

Keywords:

References

1. R. Chai, G. R. Naik, S. H. Ling and H. T. Nguyen, Hybrid brain computer interface for biomedical cyber physical system application using wireless embedded EEG systems, Biomed. Eng. Online 16 (2017) 1–23. Crossref, Web of Science, Google Scholar
2. G. R. Muller-Putz and G. Pfurtscheller, Control of an electrical prosthesis with an SSVEP-Based BCI, IEEE Trans. Biomed. Eng. 55 (2008) 61–64. Crossref, Web of Science, Google Scholar
3. Y. Yu, Z. Zhou, Y. Liu, J. Jiang, E. Yin, N. Zhang, Z. Wang, Y. Liu, X. Wu and D. Hu, Self-paced operation of a wheelchair based on a hybridbrain computer interface combining motor imagery and P300 potential, IEEE Trans. Neural Syst. Rehabil. Eng. 25 (2017) 2516–2526. Crossref, Web of Science, Google Scholar
4. M. Zaki, A. Alquraini and T. Sheltami, Home automation using EMOTIV: controlling TV by brainwaves, J. Ubiquit. Syst. Pervas. Networks 10 (2018) 27–32. Crossref, Google Scholar
5. N. Saga, Y. Tanaka, A. Doi, T. Oda, S. N. Kudoh and H. Fujie, Prototype of an Ankle Neuro rehabilitation system with Heuristic BCI Using Simplified Fuzzy Reasoning, Appl. Sci. 9 (2019) 2429. Crossref, Google Scholar
6. I. Lazarou, S. Nikolopoulos, P. C. Petrantonakis, I. Kompatsiaris and M. Tsolaki, EEG-based Brain–Computer interfaces for communication and rehabilitation of people with motor impairment: A Novel Approach of the 21st Century, Front Hum Neurosci. 12 (2018) 1–18. Crossref, Web of Science, Google Scholar
7. MD. A. Rahman, M. S. Hossaini and MD. M. Rashid, A Blockchain-Based non-invasive cyber-physical occupational therapy framework: BCI perspective, IEEE Access 7 (2019) 34874–34884. Crossref, Web of Science, Google Scholar
8. L. F. Nicolas-Alonso and J. Gomez-Gil, Brain computer interfaces, a review, Sensors 12 (2012) 1211–1279. Crossref, Web of Science, Google Scholar
9. B. He, L. Astolfi, P. A. Valdes-Sosa, D. Marinazzo, S. O. Palva, C. G. Benar, C. M. Michel and T. Koenig, Electrophysiological brain connectivity: theory and implementation, IEEE Trans. Biomed. Eng. 66 (2019) 2115–2137. Crossref, Web of Science, Google Scholar
10. S. Waldert, T. Pistohl, C. Braun, T. Ball, A. Aertsen and C. Mehring, A review on directional information in neural signals for brain-machine interfaces, J. Physiol. Paris 103 (2019) 244–254. Crossref, Google Scholar
11. H. Yuan and B. He, Brain–computer interfaces using sensorimotor rhythms: current state and future perspectives, IEEE Trans. Biomed. Eng. 61 (2014) 1425–1435. Crossref, Web of Science, Google Scholar
12. S. Tramontel, R. Sorbello1, C. Guger and A. Chella, Acceptability Study of A3-K3 robotic architecture for a neurorobotics painting, Front. Neurorobot. 12 (2019) 1–10. Web of Science, Google Scholar
13. N. Badcock, B. Mousikou, Y. Mahajan, P. Lissa, J. Thie and G. McArthur, Validation of the Emotiv EPOC EEG gaming system for measuring research quality auditory ERPs, PeerJ 1 (2013) 1–17. Crossref, Web of Science, Google Scholar
14. W. Alrajhi, D. Alaloola and A. Albarqawi, Smart home: toward daily use of BCI-Based systems, 2017 Int. Conf. Informatics, Health & Technology (ICIHT) (Riyadh, Saudi Arabia, 2017), pp. 1–5. Crossref, Google Scholar
15. W. T. Lee, H. Nisar, A. S. Malik and K. H. Yeap, A Brain computer interface for smart home control, IEEE 17th Int. Symp. Consumer Electronics (ISCE), 2013. Crossref, Google Scholar
16. G. V. S. Karthik, S. Y. Fathima, M. Z. Rahman, S. R. Ahamed and A. Lay-Ekuakille, Efficient signal conditioning techniques for brain activity in remote health monitoring network, IEEE Sensors J. 13 (2013) 3276–3283. Crossref, Web of Science, Google Scholar
17. M. Kim, H. B. Kim and S. Jo, Quantitative evaluation of a low-cost non invasive hybrid interface based on EEG and eye movement, IEEE Trans. Neural Syst. Rehabilit. Eng. 23 (2015) 159–168. Crossref, Web of Science, Google Scholar
18. Community, https://openbci.com/ (accessed February 24, 2019). Google Scholar
19. C. M. McCrimmon, M. Wang, L. S. Lopes, P. T. Wang, A. K. Bidhendi, C. Y. Liu, P. Heydari, Z. Nenadic and A. H. Do, A small, portable, battery-powered brain–computer interface system for motor rehabilitation, 38th Annual Int. Conf. IEEE Engineering in Medicine and Biology Society (EMBC) (Orlando, FL, USA, 2016), pp. 2776–2279. Crossref, Google Scholar
20. Y. H. Yu, S. W. Lu, C. H. Chuang, J. T. King, C. LunChang, S. A. Chen, S. F. Chen and C. T. Lin, An inflatable and wearable wireless system for making 32-channel electroencephalogram measurements, IEEE Trans. Neural Syst. Rehabilit. Eng. 24 (2016) 806–813. Crossref, Web of Science, Google Scholar
21. A. Jain, I. Kim and B. J. Gluckman, Low cost electroencephalographic acquisition amplifier to serve as teaching and research tool, 33rd Annual Int. Conf. of the IEEE, EMBS (Boston, MA, USA, 2011), pp. 1888–1891. Crossref, Google Scholar
22. J. A. Lovelace, T. S. Witt and F. R. Beyette, Bluetooth enabled electroencephalograph (EEG) platform, IEEE 56th Int. Midwest Symp. Circuits and Systems (MWSCAS), 2013, pp. 1172–1175. Crossref, Google Scholar
23. Z. Wang, W. Li, C. Chen, C. Sun and W. Chen, A multichannel reconfigurable EEG acquisition system design with felt-based soft material electrodes, IEEE Int. Instrumentation and Measurement Technology Conf. (I2MTC) (Houston, TX, USA, 2018), pp. 1–5. Crossref, Google Scholar
24. V. Stornelli et al., Fully differential DDA-based fifth and seventh order Bessel low pass filters and buffers for DCR radio systems, Analog. Integr. Circ. Sig. Process 75 (2013) 305–310. Crossref, Web of Science, Google Scholar
25. V. Stornelli, Low voltage low power fully differential buffer, J. Circuits Syst. Comput. 18 (2009) 497–502. Link, Web of Science, Google Scholar
26. M. Chiesi, M. Guermandi, S. Placati, E. F. Scarselli and R. Guerrieri, Creamino: A cost-effective, open-source EEG-based BCI system, IEEE Trans. Biomed. Eng. 66 (2019) 900–909. Crossref, Web of Science, Google Scholar
27. V. Randazzo, J. Ferretti and E. Pasero, A wearable smart device to monitor multiple vital parameters — VITAL ECG, Electronics 300 (2020) 1–13. Google Scholar
28. A. Villegas, D. McEneaney and O. Escalona, Arm-ECG wireless sensor system for wearable long-term surveillance of heart arrhythmias, Electronics 8 (2019) 1–26. Crossref, Web of Science, Google Scholar
29. D. G. Flumeri, P. Aricò, G. Borghini, N. Sciaraffa, A. D. Florio and F. Babiloni, The dry revolution: evaluation of three different EEG dry electrode types in terms of signal spectral features, mental states classification and usability, Sensors 19 (2019) 1365. Crossref, Web of Science, Google Scholar
30. S. Krachunov and A. J. Casson, 3D printed dry EEG electrodes, Sensors 16 (2016) 1635. Crossref, Web of Science, Google Scholar
31. A. Bruns, Fourier- Hilbert- and wavelet-based signal analysis: are they really different approaches? J. Neurosci. Methods 137 (2004) 321–332. Crossref, Web of Science, Google Scholar
32. M. Guner and B. Erkmen, Real-time, portable EEG signal acquisition system, 2018 Medical Technologies National Congress (TIPTEKNO) (Magusa, Cyprus, 2018), pp. 1–4. Crossref, Google Scholar
33. A. Laghari, Z. A. Memon, S. Ullah and I. Hussain, Cyber physical system for stroke detection, IEEE Access 6 (2018) 37444–37453. Crossref, Web of Science, Google Scholar
34. Y. M. Chi, T. P. Jung and G. Cauwenberghs, Dry-contact and noncontact biopotential electrodes: methodological review, IEEE Rev. Biomed. Eng. 3 (2018) 106–109. Crossref, Google Scholar
35. C.-C. Lo, T.-Y. Chien, Y.-C. Chen, S.-H. Tsai, W.-C. Fang and B.-S. Lin, A wearable channel selection-based brain-computer interface for motor imagery detection, Sensors 213 (2016) 1–14. Google Scholar