Computational strategy for aphasia support
Estratégia computacional de apoio à afasia
Palavras-chave:
aphasia, electroencephalography, biomedical signal monitoring, language rehabilitation, computational strategyResumo
Aphasia, a language disorder resulting from brain lesions, impacts communication abilities, and is commonly diagnosed through formal tests assessing speech fluency, comprehension, naming, and repetition abilities. These tests aid in devising treatment plans and evaluating recovery potential. Technological advances have enabled the development of devices and techniques for recording brain activity. Despite their potential, the high cost, device robustness, and complexity often limit their application in rehabilitation centers outside hospitals. In this study, we propose a computational strategy that uses electroencephalography devices to support aphasia rehabilitation therapies. The research was conducted with aphasic participants undergoing rehabilitation at the Center for Prevention and Rehabilitation of People with Disabilities within the Unified Health System in Bahia-Brazil. Preliminary results obtained from the silent word generation task, as outlined in the adult language paradigm by the American Society for Functional Neuroradiology, suggest that increased electrical activation in the right hemisphere may indicate language migration to the non-dominant hemisphere, potentially leading to improved recovery.
Downloads
Referências
BADCOCK, N.A, Mousikou P, Mahajan Y, de Lissa P, Thie J, McArthur G. 2013. Validation of the Emotiv EPOC® EEG gaming system for measuring research quality auditory ERPs. PeerJ 1:e38.
BLACK, D. F., Vachha, B., Mian, A., Faro, S. H., Maheshwari, M., Sair, H. I., ... & Welker, K. American society of functional Neuroradiology–recommended fMRI paradigm algorithms for presurgical language assessment. American journal of Neuroradiology, v. 38, n. 10, p. E65-E73, 2017.
BRADY, M. C., Kelly, H., Godwin, J., Enderby, P., Campbell, P., & Cochrane Stroke Group. (1996). Speech and language therapy for aphasia following stroke. Cochrane database of systematic reviews, n. 6, 2016.
CRESWELL, J.W.; Creswell, J.D. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches; Sage Publications: Thousand Oaks, CA, USA, 2017.
FOUAD, Islam A. A robust and reliable online P300-based BCI system using Emotiv EPOC+ headset. Journal of Medical Engineering & Technology, v. 45, n. 2, p. 94-114, 2021.
KLEIH, Sonja C. et al. Toward a P300 based brain-computer interface for aphasia rehabilitation after stroke: presentation of theoretical considerations and a pilot feasibility study. Frontiers in human neuroscience, v. 10, p. 547, 2016.
KOTOWSKI, K., Stapor, K., Leski, J., & Kotas, M. Validation of Emotiv EPOC+ for extracting ERP correlates of emotional face processing. Biocybernetics and Biomedical Engineering, 38(4), 773-781, 2018.
MANYAKOV, Nikolay V. et al. Comparison of classification methods for P300 brain-computer interface on disabled subjects. Computational intelligence and neuroscience, v. 2011, p. 1-12, 2011.
MATTIOLI, Flavia. The clinical management and rehabilitation of post-stroke aphasia in Italy: evidences from the literature and clinical experience. Neurological Sciences, v. 40, n. 7, p. 1329-1334, 2019.
MELEK, Mesut; MANSHOURI, Negin; KAYIKÇIOĞLU, TEMEL. Low-cost brain-computer interface using the emotiv epoc headset based on rotating vanes. Traitement du Signal, v. 37, n. 5, 2020.
MINEIRO, A., Caldas, A. C., Leal, G., & Rodrigues, I. (2008). Revisiting Aphasias in PALPA-P. Cadernos de Saúde, 1(2), 135-145, 2008.
MÜLLER, Ingrid; BUCHHOLZ, Margret; FERM, Ulrika. Text messaging with picture symbols‐experiences of seven persons with cognitive and communicative disabilities. Journal of Assistive Technologies, v. 4, n. 4, p. 11-23, 2010.
PAGLIARIN, K. C., de Oliveira, C. R., da Silva, B. M., de Freitas Calvette, L., & Fonseca, R. P. Instruments for language assessment following a left brain damage. Revista CEFAC: Atualização Cientifica em Fonoaudiologia e Educação, 15(2), 444-455, 2013.
RILEY, Ellyn A.; MCFARLAND, Dennis J. EEG error prediction as a solution for combining the advantages of retrieval practice and errorless learning. Frontiers in Human Neuroscience, v. 11, p. 140, 2017.
SREEDHARAN, S., Chandran, A., Yanamala, V. R., Sylaja, P. N., Kesavadas, C., & Sitaram, R. Self-regulation of language areas using real-time functional MRI in stroke patients with expressive aphasia. Brain Imaging and Behavior, 14, 1714-1730, 2020.
YU, Je-Hun; SIM, Kwee-Bo. Classification of color imagination using Emotiv EPOC and event-related potential in electroencephalogram. Optik, v. 127, n. 20, p. 9711-9718, 2016.
ZAPATA, D., Francuz, P., Zapała, E., Kopiś, N., Wierzgała, P., Augustynowicz, P., ... & Kołodziej, M. (2018). The impact of different visual feedbacks in user training on motor imagery control in BCI. Applied psychophysiology and biofeedback, 43, 23-35, 2018.
