Comparing the Performance of a Novel BCI-Based Headset with a Conventional Hearing Aid While Watching TV for the Nanoporous Membrane Sensitivity
J. Environ. Nanotechnol., Volume 13, No 2 (July 2024) pp. 436-443
Abstract
Increasing levels of environmental and occupational noise in industrial environments necessitate the development of sensitive and adaptive nanoscale sensing technologies for real-time acoustic monitoring. The present study investigates the use of a nanoporous membrane-based acoustic nanosensor for compensating sensitivity losses under variable noise conditions, with an intelligent brain-computer interface (BCI) incorporated as an adaptive signal-control mechanism. Nanostructured sensing substrates within the 50–70 nm range were evaluated using nanomechanical characterization and controlled acoustic signal measurements. Twenty sensor components were assessed under simulated workplace acoustic conditions using both a con-ventional nanostructured acoustic substrate and a BCI-assisted nanosensor configuration. The BCI-integrated system em-ployed a quantum-dot-based signal amplification module to improve the reception and processing of weak acoustic signals under background-noise conditions. Sensor responses were recorded through individual diagnostic measurements and quan-titatively compared between the two configurations. The BCI-assisted nanostructured sensor exhibited significantly im-proved signal-reception performance compared with the conventional nanosensor configuration ((p < 0.001)). An overall enhancement of 18.25% was observed in the measured signal perception and decoding performance following integration of the adaptive nanoscale sensing and signal-processing system. The findings demonstrate the potential of combining engi-neered nanostructured membranes with intelligent signal-processing technologies to improve the sensitivity and reliability of nanoscale acoustic sensors operating under complex environmental noise conditions. Such adaptive nanomaterial-based sensing platforms may have potential applications in environmental acoustic monitoring, occupational exposure assessment, and real-time detection of acoustic disturbances in industrial environments. Further investigations involving different nano-material compositions, membrane morphologies, long-term stability, and environmentally relevant operating conditions are required to establish the scalability and practical applicability of the proposed nanosensor system.
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Acuña, K., Sapahia, R., Jiménez, I. N., Antonietti, M., Anzola, I., Cruz, M. and DeBuc, D. C., (2024). Func-tional Near-Infrared Spectrometry as a Useful Diag-nostic Tool for Understanding the Visual System: A Review, J. Clin. Med., 13(1), 282 (2024)
https://doi.org/10.3390/jcm13010282
Ahmadian, P., Cagnoni, S. and Ascari, L., How capable is non-invasive EEG data of predicting the next movement? A mini review, Front. hum. neurosci., 7, 124 (2013)
https://doi.org/10.3389/fnhum.2013.00124
Alkaff, Z. A., Malim, N. H. A. H., Sumari, P. and Abdul-lah, J. M., Applications of Brain Computer Interface in Present Healthcare Setting, (2024)
https://doi.org/10.5772/intechopen.112353
Araújo, J., Simons, B. D. and Goswami, U., Remediating phonological deficits in dyslexia with brain-computer interfaces, In Brain-Computer Interface Research: A State-of-the-Art Summary, 11, 13-19 (2024). Cham: Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-49457-4_2
Belkacem, A. N., Nishio, S., Suzuki, T., Ishiguro, H. and Hirata, M., Neuromagnetic decoding of simultane-ous bilateral hand movements for multidimensional brain–machine interfaces, IEEE Trans. Neural Syst. Rehabil. Eng., 26(6), 1301-1310 (2018)
https://doi.org/10.1109/TNSRE.2018.2837003
Belkacem, A. N., Saetia, S., Zintus-Art, K., Shin, D., Kambara, H., Yoshimura, N. and Koike, Y., Real‐Time Control of a Video Game Using Eye Move-ments and Two Temporal EEG Sensors, Comput. In-tell. Neurosci., 2015(1), 653639 (2015)
https://doi.org/10.1155/2015/653639
Calhoun, V. D., Amin, M. F., Hjelm, D., Damaraju, E. and Plis, S. M., A deep-learning approach to trans-late between brain structure and functional connec-tivity, IEEE Int. Trans. Acoust., Speech, Signal. Pro. (ICASSP), 6155-6159 (2017)
https://doi.org/10.1109/ICASSP.2017.7953339
Caria, A., Weber, C., Brötz, D., Ramos, A., Ticini, L. F., Gharabaghi, A. and Birbaumer, N., Chronic stroke recovery after combined BCI training and physio-therapy: a case report, Psychophysiology, 48(4), 578-582 (2011).
https://doi.org/10.1111/j.1469-8986.2010.01117.x
Hartley, D., Rochtchina, E., Newall, P., Golding, M. and Mitchell, P., Use of hearing aids and assistive listen-ing devices in an older Australian population, J Am Acad Audiol., 21(10), 642-653 (2010)
https://doi.org/10.3766/jaaa.21.10.4.
Falk, T. H., Guger, C. and Volosyak, I., Brain–computer interfaces: recent advances, challenges, and future directions, Handbook of Hum. Mach. Syst., 11-22 (2023).
https://doi.org/10.1002/9781119863663.ch2
Gordon-Salant, S. and Callahan, J. S., The benefits of hearing aids and closed captioning for television viewing by older adults with hearing loss, Ear and Hear., 30(4), 458-465 (2009)
https://doi.org/10.1097/AUD.0b013e3181a26ef4
Guger, C., Ince, N. F., Korostenskaja, M. and Allison, B. Z., Brain-Computer Interface Research: A State-of-the-Art Summary 11, In Brain-Computer Interface Research: A State-of-the-Art Summary, 11, 1-11 (2024). Cham: Springer Nature Switzerland
https://doi.org/10.1007/978-3-031-49457-4_1
Guger, C., Rezvani, S., Ince, N. F., Korostenskaja, M. and Allison, B. Z., A Summary of the 2022 BCI Award with Discussion of BCI Trends, In Brain-Computer Interface Research: A State-of-the-Art Summary, 11, 141-148 (2024).
https://doi.org/10.1007/978-3-031-49457-4_13.
Huo, C., Xu, G., Xie, H., Chen, T., Shao, G., Wang, J. and Li, Z., Functional near-infrared spectroscopy in non-invasive neuromodulation, Neural Regen. Res., 19(7), 1517-1522 (2024)
https://doi.org/10.4103/1673-5374.387970
Jerger, J., Chmiel, R., Florin, E., Pirozzolo, F. and Wilson, N., Comparison of conventional amplification and an assistive listening device in elderly persons. Ear and hear., 17(6), 490-504 (1996).
Lal, T., Hinterberger, T., Widman, G., Schröder, M., Hill, N., Rosenstiel, W. and Schölkopf, B., Methods to-wards invasive human brain computer interfac-es, Adv. Neural Inf. Process. Syst., 17 (2004).
Lekova, A. K., Tsvetkova, P. and Andreeva, A., En-hancing Brain Health and Cognitive Development Through Sensorimotor Play in Virtual Reality: Un-covering the Neural Correlates, Int. J. Games Soc. Impact, 2(1), 46-70 (2024)
https://www.doi.org/10.24140/ijgsi.v2.n1.03
Liu, S., Wang, L. and Gao, R. X., Cognitive neurosci-ence and robotics: Advancements and future re-search directions, Robot. Comput. Integr. Man-uf., 85, 102610 (2024).
https://doi.org/10.1016/j.rcim.2023.102610.
Maiseli, B., Abdalla, A. T., Massawe, L. V., Mbise, M., Mkocha, K., Nassor, N. A. and Kimambo, S., Brain–computer interface: trend, challenges, and threats, Brain Inform., 10(1), 20 (2023)
https://doi.org/10.1186/s40708-023-00199-3
McFarland, D. J. and Wolpaw, J. R., Brain-computer interfaces for communication and control, Com. ACM., 54(5), 60-66 (2011)
https://doi.org/10.1145/1941487.1941506
Munavalli, J. R., Sankpal, P. R., Sumathi, A. Oli, J. M., Introduction to Brain–Computer Interface: Applica-tions and Challenges, Brain‐Computer Interface: Using Deep Learning Applications, 1-24 (2023)
https://doi.org/10.1002/9781119857655.ch1
Nielsen., The total audience report Q1, 2017, The Niel-sen Company (US), LLC., New York, NY. (2017).
Okorokova, E. V., Sobinov, A. R., Downey, J. E., He, Q., van Driesche, A., Satzer, D. and Bensmaia, S. J., May the Force Be with You: Biomimetic Grasp Force Decoding for Brain Controlled Bionic Hands, In Brain-Computer Interface Research: A State-of-the-Art Summary 11, 109-121 (2024).
https://doi.org/10.1007/978-3-031-49457-4_11
Romine, C. B. and Reynolds, C. R., Sequential memory: a developmental perspective on its rela-tion to frontal lobe functioning, Neuropsychol. Rev., 14, 43-64 (2004)
https://doi.org/10.1023/B:NERV.0000026648.94811.32
Sancho-Aldridge, J. and Davis, A., The impact of hear-ing impairment on television viewing in the UK, Br. J. Audiol., 27(3), 163-173 (1993)
https://doi.org/10.3109/03005369309076690
Schalk, G., McFarland, D. J., Hinterberger, T., Birbaumer, N. and Wolpaw, J. R., BCI2000: a gen-eral-purpose brain-computer interface (BCI) sys-tem. IEEE Trans. Biomed. Eng., 51(6), 1034-1043 (2004).
https://doi.org/10.1109/TBME.2004.827072
Schalk, G. and Allison, B. Z., Noninvasive brain–computer interfaces, In Neuromodulation, 357-377 (2018).
https://doi.org/10.1016/B978-0-12-805353-9.00026-7
Sendelbaugh, J. W., Television viewing habits of hear-ing-impaired teenagers in the Chicago metropolitan area, Am. Ann. Deaf, 536-541 (1978).
http://www.jstor.org/stable/44399792
Tao, Q., Chao, H., Fang, D. and Dou, D., Progress in neurorehabilitation research and the support by the National Natural Science Foundation of China from 2010 to 2022, Neural Regen. Res., 19(1), 226-232 (2024).
https://doi.org/10.4103/1673-5374.375342
Woodman, G. F., A brief introduction to the use of event-related potentials in studies of perception and attention, Atten. Percept. Psychophys., 72, 2031-2046 (2010).
https://doi.org/10.3758/BF03196680
Kumar, Y., Kumar, J. and Sheoran, P., Integration of cloud computing in BCI: A reviewBiomed. Signal Process. Control., 87, 105548 (2024)
https://doi.org/10.1016/j.bspc.2023.105548
Mansouri, S., Application of Neural Networks in the Medical Field, Journal of Wireless Mobile Net-works, Ubiquitous Computing, and Dependable Applications, 14(1), 69-81 (2023)
https://doi.org/10.58346/JOWUA.2023.I1.006
Ilić, P., Nešković Markić, D. and Stojanović Bjelić, L., Measuring and Mapping Noise Pollution in the City of Banja LUKA, Archives for Technical Sciences, 1(18), 89–96 (2018)
https://doi.org/10.7251/afts.2018.1018.089I
Sargunapathi, R., Vinayagamoorthy, P., Sumathi, P. and Sirajunissa Begum, S., Mapping of Scientific Articles on Brain Tumors: A Scientometric Study, Indian Journal of Information Sources and Ser-vices, 10(2), 26–34 (2020)
https://doi.org/10.51983/ijiss.2020.10.2.490
Dülger, G. and Dülger, B., Antibacterial Activity of Sta-chys sylvatica Against Some Human Eye Patho-gens, Natural and Engineering Sciences, 7(2), 131-135 (2022).
https://doi.org/10.28978/nesciences.1159224
Kadhim, A. A., Mohammed, S. J. and Al-Gayem, Q., Digital Video Broadcasting T2 Lite Performance Evaluation Based on Rotated Constellation Rates, Journal of Internet Services and Information Securi-ty, 13(3), 127-137 (2023)
