Classifying Motor Imagery Actions in Electroencephalogram Signals of Older Adults Using Artificial Neural Networks

Authors

  • Ivan Carrillo-Rodríguez Universidad Autónoma de Baja California
  • Victoria Meza-Kubo Universidad Autónoma de Baja California
  • Luis Pellegrin Universidad Autónoma de Baja California

DOI:

https://doi.org/10.47756/aihc.y9i1.167

Keywords:

Brain computer-interfaces, older adults, motor imagery, machine learning

Abstract

Brain-computer interfaces (BCIs) have enabled users to control computing applications by measuring brain activity, such as through electroencephalograms (EEGs). These BCIs have the potential to assist individuals with cognitive and motor impairments. However, research on BCIs has shown that their performance can vary among study subjects, particularly in the case of older adults. Cognitive impairment is a natural condition that occurs in older adults, and if left untreated, it can lead to the development of dementia, which, in turn, results in dependency for performing basic daily life activities. This paper presents the results of a preliminary analysis on the use of BCIs to recognize motor imagery of up to five right-hand movements, using a supervised machine learning algorithm. This approach aims to facilitate the interaction of older adults with cognitive stimulation applications, mobility devices, and other tools that improve their quality of life.

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References

Alom, M. K., & Islam, S. M. R. (2020, November). Classification for the P300-based brain computer interface (BCI). In 2020 2nd international conference on advanced information and communication technology (ICAICT) (pp. 387-391). IEEE. DOI: https://doi.org/10.1109/ICAICT51780.2020.9333481

Bauer, A. C. M., & Andringa, G. (2020). The Potential of Immersive Virtual Reality for Cognitive Training in Elderly. Gerontology, 66(6), 614–623. https://doi.org/10.1159/000509830 DOI: https://doi.org/10.1159/000509830

Czaja, S. J., Charness, N., Fisk, A. D., Hertzog, C., Nair, S. N., Rogers, W. A., & Sharit, J. (2006). Factors predicting the use of technology: findings from the Center for Research and Education on Aging and Technology Enhancement (CREATE). Psychology and aging, 21(2), 333. DOI: https://doi.org/10.1037/0882-7974.21.2.333

Gamito, P., Oliveira, J., Morais, D., Coelho, C., Santos, N., Alves, C., Galamba, A., Soeiro, M., Yerra, M., French, H., Talmers, L., Gomes, T., & Brito, R. (2019). Cognitive Stimulation of Elderly Individuals with Instrumental Virtual Reality-Based Activities of Daily Life: Pre-Post Treatment Study. Cyberpsychology, Behavior, and Social Networking, 22(1), 69–75. https://doi.org/10.1089/cyber.2017.0679 DOI: https://doi.org/10.1089/cyber.2017.0679

Garro, F., & McKinney, Z. (2020, September). Toward a standard user-centered design framework for medical applications of brain-computer interfaces. In 2020 IEEE International Conference on Human-Machine Systems (ICHMS) (pp. 1-3). IEEE. DOI: https://doi.org/10.1109/ICHMS49158.2020.9209416

HALTAŞ, K., ERGÜZEN, A., & Erdal, E. (2019, October). Classification methods in EEG based motor imagery BCI systems. In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-5). IEEE. DOI: https://doi.org/10.1109/ISMSIT.2019.8932947

Hooren, S. A. H. V., Valentijn, A. M., Bosma, H., Ponds, R. W. H. M., Boxtel, M. P. J. V., & Jolles, J. (2007). Cognitive functioning in healthy older adults aged 64-81: A cohort study into the effects of age, sex, and education. Aging, Neuropsychology, and Cognition, 14(1), 40–54. https://doi.org/10.1080/138255890969483 DOI: https://doi.org/10.1080/138255890969483

Kevric, Jasmin, & Subasi, Abdulhamit. (2017). Comparison of signal decomposition methods in classification of EEG signals for motor-imagery BCI system. https://doi.org/10.1016/j.bspc.2016.09.007 DOI: https://doi.org/10.1016/j.bspc.2016.09.007

Kim, S., Yao, W., & Du, X. (2022). Exploring older adults’ adoption and use of a tablet computer during COVID-19: longitudinal qualitative study. JMIR aging, 5(1), e32957. DOI: https://doi.org/10.2196/32957

Klimova, B., & Valis, M. (2018). Smartphone applications can serve as effective cognitive training tools in healthy aging. Frontiers in Aging Neuroscience, 9(JAN), 1–4. https://doi.org/10.3389/fnagi.2017.00436 DOI: https://doi.org/10.3389/fnagi.2017.00436

Lopez-Samaniego, L., Garcia-Zapirain, B., & Mendez-Zorrilla, A. (2014). Memory and accurate processing brain rehabilitation for the elderly: LEGO robot and iPad case study. Bio-Medical Materials and Engineering, 24(6), 3549–3556. https://doi.org/10.3233/BME-141181 DOI: https://doi.org/10.3233/BME-141181

Nóbrega, M. do P. S. de S., Freitas, C. M., Jesus, B. G. S. de, Santos, J. C. dos, & Silva, M. S. G. O. da. (2022). COGNITIVE STIMULATION PROGRAMS FOR ELDERLY PEOPLE WITH AND WITHOUT DEMENTIA SYNDROMES SUPERVISED OR APPLIED BY NURSES: INTEGRATIVE REVIEW. En Cogitare Enfermagem (Vol. 27). Universidade Federal do Parana. https://doi.org/10.5380/ce.v27i0.78943 DOI: https://doi.org/10.5380/ce.v27i0.78943

Oliveira, J., Gamito, P., Souto, T., Conde, R., Ferreira, M., Corotnean, T., Fernandes, A., Silva, H., & Neto, T. (2021). Virtual reality-based cognitive stimulation on people with mild to moderate dementia due to alzheimer’s disease: A pilot randomized controlled trial. International Journal of Environmental Research and Public Health, 18(10). https://doi.org/10.3390/ijerph18105290 DOI: https://doi.org/10.3390/ijerph18105290

Palumbo, V., & Paterno, F. (2021). Micogito: A serious gamebook based on daily life scenarios to cognitively stimulate older adults. GoodIT 2021 - Proceedings of the 2021 Conference on Information Technology for Social Good, 163–168. https://doi.org/10.1145/3462203.3475889 DOI: https://doi.org/10.1145/3462203.3475889

Sazgar, M., Young, M. G., Sazgar, M., & Young, M. G. (2019). Overview of EEG, electrode placement, and montages. Absolute epilepsy and EEG rotation review: Essentials for trainees, 117-125. DOI: https://doi.org/10.1007/978-3-030-03511-2_5

TORIBIO-GUZMÁN, J. M., VIDALES, E. P., RODRÍGUEZ, M. a J. V., AGUADO, Y. B., BARTOLOMÉ, M. a T. C., & FRANCO-MARTÍN, M. A. (2018). Rehabilitación cognitiva por ordenador en personas mayores: Programa gradior. Aula, 24(0), 61. https://doi.org/10.14201/aula2018246175 DOI: https://doi.org/10.14201/aula2018246175

Varela, L. (2004). Características del deterioro cognitivo en el adulto mayor hospitalizado a nivel nacional hospitalized peruvian elderly adults. Revista Soc. Per. Med. Inter., 17, 2. https://doi.org/10.36393/spmi.v17i2.235

Vourvopoulos, A., & Liarokapis, F. (2011). Brain-controlled NXT Robot: Tele-operating a robot through brain electrical activity. Games and Virtual Worlds for …. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5962098 DOI: https://doi.org/10.1109/VS-GAMES.2011.27

Vuckovic, A., & Osuagwu, B. A. (2013). Using a motor imagery questionnaire to estimate the performance of a brain-computer interface based on object oriented motor imagery. Clinical Neurophysiology, 124(8), 1776-1784. DOI: https://doi.org/10.1016/j.clinph.2013.02.016

Wolpaw, J. R., Birbaumer, N., Heetderks, W. J., McFarland, D. J., Peckham, P. H., Schalk, G., Donchin, E., Quatrano, L. A., Robinson, C. J., & Vaughan, T. M. (2000). Brain-computer interface technology: A review of the first international meeting. IEEE Transactions on Rehabilitation Engineering, 8(2), 164–173. https://doi.org/10.1109/TRE.2000.847807 DOI: https://doi.org/10.1109/TRE.2000.847807

Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002). Brain-computer interfaces for communication and control. En Clinical Neurophysiology (Vol. 113, Número 6, pp. 767–791). https://doi.org/10.1016/S1388-2457(02)00057-3 DOI: https://doi.org/10.1016/S1388-2457(02)00057-3

Wolpaw, J. R., Millan, J. D. R., & Ramsey, N. F. (2020). Brain-computer interfaces: Definitions and principles. Handbook of clinical neurology, 168, 15-23. DOI: https://doi.org/10.1016/B978-0-444-63934-9.00002-0

Zhang, W., Tan, C., Sun, F., Wu, H., & Zhang, B. (2018). A Review of EEG-Based Brain-Computer Interface Systems Design. Brain Science Advances, 4(2), 156–167. https://doi.org/10.26599/bsa.2018.9050010 DOI: https://doi.org/10.26599/BSA.2018.9050010

Zhang, J. (2019). Cognitive Functions of the Brain: Perception, Attention and Memory. http://arxiv.org/abs/1907.02863

Zuo C., Jin, J., Yin, E., Saab, R., Miao, Y., Wang, X., ... & Cichocki, A. (2020). Novel hybrid brain–computer interface system based on motor imagery and P300. Cognitive Neurodynamics, 14, 253-265. DOI: https://doi.org/10.1007/s11571-019-09560-x

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Published

2024-11-30

How to Cite

[1]
Carrillo-Rodríguez, I. et al. 2024. Classifying Motor Imagery Actions in Electroencephalogram Signals of Older Adults Using Artificial Neural Networks. Avances en Interacción Humano-Computadora. 9, 1 (Nov. 2024), 196–202. DOI:https://doi.org/10.47756/aihc.y9i1.167.

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