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Henao Isaza, V., Cadavid Castro, V., Salas Villa, E., Gonzalez Cuartas, S., & Ochoa, J. F. (2024). Presentando la Fisiología Visual y los Potenciales Evocados en Estado Estable utilizando Electroencefalografía de Bajo Costo y Transferible para Evaluar la Activación Neuronal. International Journal of Psychological Research, 17(2), 25–35. https://doi.org/10.21500/20112084.7299
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The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
To give up copyright, the authors allow that, International Journal of Psychological Research, distribute the work more broadly, check for the reuse by others and take care of the necessary procedures for the registration and administration of copyright; at the same time, our editorial board represents the interests of the author and allows authors to re-use his work in various forms. In response to the above, authors transfer copyright to the journal, International Journal of Psychological Research. This transfer does not imply other rights which are not those of authorship (for example those that concern about patents). Likewise, preserves the authors rights to use the work integral or partially in lectures, books and courses, as well as make copies for educational purposes. Finally, the authors may use freely the tables and figures in its future work, wherever make explicit reference to the previous publication in International Journal of Psychological Research. The assignment of copyright includes both virtual rights and forms of the article to allow the editorial to disseminate the work in the manner which it deems appropriate.
The editorial board reserves the right of amendments deemed necessary in the application of the rules of publication.
Resumen
Propósito: La capacidad de ver y procesar imágenes depende de la función de los ojos y del procesamiento de la información visual por parte de las neuronas en la corteza cerebral, algo que podría medirse mediante electroencefalografía (EEG). Aunque el EEG se utiliza para evaluar las vías visuales en niños y enfermedades desmielinizantes, la utilización limitada de técnicas de grabación cerebral en otras aplicaciones como la terapia se debe principalmente a restricciones presupuestarias. El objetivo de este artículo es demostrar resultados del estudio de aspectos cerebrales de la visión, utilizando mediciones basadas en el análisis de actividad oscilatoria, equipos de bajo costo y portátiles, y un flujo de procesamiento basado en las bibliotecas de código abierto de Python. Estos estudios involucran a sujetos sanos que usan gafas para evaluar cambios en la percepción visual.
Métodos: Primero, se registraron señales electroencefalográficas mientras los sujetos observaban un estímulo visual estandarizado. Las señales fueron procesadas y filtradas para reducir artefactos, y se calculó la densidad espectral de potencia (PSD) para observar la presencia de potenciales visuales en estado estable (VEP) y confirmar la captura de la activación neuronal ante el estímulo visual.
Resultados: Fue posible establecer una diferencia entre los sujetos que llevaban y no llevaban sus gafas, permitiendo validar que la información adquirida con el equipo transferible es adecuada para el análisis de la actividad neuronal relacionada con el procesamiento visual, abriendo la posibilidad de ser utilizada en estudios futuros en terapia.
Conclusión: Este estudio contribuye al desarrollo de soluciones de EEG de bajo costo y portátiles para el análisis del sistema visual. Demuestra el potencial de aplicar dispositivos de EEG transferibles en entornos clínicos y resalta la importancia de estímulos visuales adaptados para una activación neural confiable.
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Referencias
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Beniczky, S., & Schomer, D. L. (2020). Electroencephalography: basic biophysical and technological aspects important for clinical applications. Epileptic Disorders, 22(6), 697–715. https://doi.org/10.1684/epd.2020.1217
Brienza, M., & Mecarelli, O. (2019). Neurophysiological Basis of EEG BT. In O. Mecarelli (Ed.), Clinical Electroencephalography (pp. 9–21). Springer International Publishing. https://doi.org/10.1007/978-3-030-04573-9_2
Cadavid, V., Salas, E., Gonzalez, S., Henao, V., Ortega, D., Suarez, J. C., & Ochoa, J. (2021). Captura y análisis de potenciales visuales en estado estacionario usando tecnología portable y de bajo costo. 2021 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2021 - Conference Proceedings. https://doi.org/10.1109/STSIVA53688.2021.9592014
Carvalho, S. N., Costa, T. B., Uribe, L. F., Soriano, D. C., Yared, G. F., Coradine, L. C., & Attux, R. (2015). Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs. Biomedical Signal Processing and Control, 21, 34–42. https://doi.org/10.1016/J.BSPC.2015.05.008
Chandran, K. S., & Kiruba Angeline, T. (2020). Identification of disease symptoms using taste disorders in electroencephalogram signal. Journal of Computational and Theoretical Nanoscience, 17 (5), 2051–2056.
Fox, M., Barber, C., Keating, D., & Perkins, A. (2014). Comparison of cathode ray tube and liquid crystal display stimulators for use in multifocal VEP. Documenta ophthalmologica. Advances in ophthalmology, 129(2), 115–122. doi:10.1007/S10633-014-9451-0
Guarnizo-Lemus, C. (2008, dec). Análisis de reducción de ruido en señales eeg orientado al reconocimiento de patrones. TecnoLógicas, (21), 67–80. https://doi.org/10.22430/22565337.248
Gunaydin, O., & Ozkan, M. (2010). Design of a Brain Computer Interface system based on electroencephalogra (EEG). 4th European Education and Research Conference (EDERC 2010).
Hemptinne, C., Liu-Shuang, J., Yuksel, D., & Rossion, B. (2019). Rapid Objective Assessment of Contrast Sensitivity and Visual Acuity with Sweep Visual Evoked Potentials and an Extended Electrode Array. Journal of Vision, 19(8), 87. doi: 10.1167/19.8.87
Kadri, A., & Apriani, N. (2022). Electroencephalography Findings in Traumatic Brain Injury. The Open Neurology Journal, 16(1), 1–11. https://doi.org/10.2174/1874205x-v16-e2206100
Kiiski, H. S., Riada, S. N., Lalor, E. C., Gonçalves, N. R., Nolan, H., Whelan, R., Lonergan, R., Kelly, S., O'Brien, M. C., Kinsella, K., Bramham, J., Burke, T., Ó Donnchadha, S., Hutchinson, M., Tubridy, N., & Reilly, R. B. (2016). Delayed P100-Like Latencies in Multiple Sclerosis: A Preliminary Investigation Using Visual Evoked Spread Spectrum Analysis. PloS one, 11(1). https://doi.org/10.1371/JOURNAL.PONE.0146084
Lantz, C. L., & Quinlan, E. M. (2021). High-Frequency Visual Stimulation Primes Gamma Oscillations for Visually Evoked Phase Reset and Enhances Spatial Acuity. Cerebral cortex communications, 2(2), tgab016. https://doi.org/10.1093/texcom/tgab016
Lei;, X., & Liao, K. (2017). Understanding the Influences of EEG Reference: A Large-Scale Brain Network Perspective. Front. Neurosci., 11. https://doi.org/10.3389/fnins.2017.00205
Leuchs, L. (2019, May 3rd). Choosing your reference – and why it matters. Brain Products Press Release. https://pressrelease.brainproducts.com/referencing/
Marcar, V. L., & Jäncke, L. (2018, jan). Stimuli to differentiate the neural response at successive stages of visual processing using the VEP from human visual cortex. Journal of neuroscience methods, 293, 199–209. https://doi.org/10.1016/J.JNEUMETH.2017.09.015
Meigen, T., & Bach, M. (1999). On the statistical significance of electrophysiological steady-state responses. Documenta ophthalmologica. Advances in ophthalmology, 98(3), 207–232. https://doi.org/10.1023/A:1002097208337
Mora-Cortes, A., Ridderinkhof, K. R., & Cohen, M. X. (2018). Evaluating the feasibility of the steady-state visual evoked potential (SSVEP) to study temporal attention. Psychophysiology, 55(5). https://doi.org/10.1111/PSYP.13029
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors, 12(2), 1211–1279. https://doi.org/10.3390/s120201211
Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015, 5). The steady-state visual evoked potential in vision research: A review. Journal of Vision, 15, 4-4. https://doi.org/10.1167/15.6.4
Nuwer, M. R. (2018). 10-10 electrode system for EEG recording. Clinical Neurophysiology, 129(5), 1103. https://doi.org/10.1016/j.clinph.2018.01.065
O’Hare, L. (2017, feb). Steady-state VEP responses to uncomfortable stimuli. European Journal of Neuroscience, 45(3), 410–422. https://doi.org/10.1111/EJN.13479
Orban, M., Elsamanty, M., Guo, K., Zhang, S., & Yang, H. (2022). A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application. Bioengineering, 9(12). https://doi.org/https://doi.org/10.3390/bioengineering9120768
Ortega, D, Henao, V, & Ochoa, J (2019, April). Ssvep study in monocular and binocular vision. 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA). https://doi.org/10.1109/STSIVA.2019.8730241
Patil, P. B., & Chavan, M. S. (2012). A wavelet based method for denoising of biomedical signal. International Conference on Pattern Recognition, Informatics and Medical Engineering, PRIME 2012, 278–283. https://doi.org/10.1109/ICPRIME.2012.6208358
Richard, B., Chadnova, E., & Baker, D. H. (2018). Binocular vision adaptively suppresses delayed monocular signals. NeuroImage, 172, 753–765. https://doi.org/10.1016/J.NEUROIMAGE.2018.02.021
Sahel, J. A., Boulanger-Scemama, E., Pagot, C., Arleo, A., Galluppi, F., Martel, J. N., . . .
Roska, B. (2021). Partial recovery of visual function in a blind patient after optogenetic therapy. Nature Medicine, 27(7), 1223–1229. https://doi.org/10.1038/s41591-021-01351-4
Sanchez-Lopez, J., Pedersini, C. A., Di Russo, F., Cardobi, N., Fonte, C., Varalta, V., Prior, M., Smania, N., Savazzi, S., & Marzi, C. A. (2019). Visually evoked responses from the blind field of hemianopic patients. Neuropsychologia, 128, 127–139. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2017.10.008
Sarzaeim, F., Hashemzehi, M., Mohammad Masoud Shushtarian, S., Shojaei, A., & Naghib, J. (2022). Flash Visual Evoked Potential as a Suitable Technique to Evaluate the Extent of Injury to Visual Pathway Following Head Trauma. Journal of Ophthalmology and Research, 05(01), 20–23. https://doi.org/10.26502/fjor.2644-00240053
Smith, J. O. (2011). Spectral Audio Signal Processing. Center for Computer Research in Music and Acoustics (CCRMA), 1–674.
Tan, Y., Tong, X., Chen, W., Weng, X., He, S., & Zhao, J. (2018). Vernier But Not Grating Acuity Contributes to an Early Stage of Visual Word Processing. Neuroscience Bulletin, 34 (3), 517–526. https://doi.org/10.1007/s12264-018-0220-z
Woolson, R. F. (2008). Wilcoxon Signed-Rank Test. Wiley Encyclopedia of Clinical Trials, 1–3. https://doi.org/10.1002/9780471462422.EOCT979
Zheng, X., Xu, G., Wang, Y., & Han, C. (2019). Objective and quantitative assessment of visual acuity and contrast sensitivity based on steady-state motion visual evoked potentials using concentric-ring paradigm. Documenta Ophthalmologica., 139. https://doi.org/10.1007/s10633-019-09702-w
Zheng, X., Xu, G., Zhang, K., & Liang, R. (2020). Assessment of Human Visual Acuity Using Visual Evoked Potential: A Review. Sensors (Basel), 20(19). https://doi.org/10.3390/s20195542
Zhong, Y., Wei, H., Chen, L., & Wu, T. (2023, mar). Automated EEG Pathology Detection Based on Significant Feature Extraction and Selection. Mathematics, 11, 1619. https://doi.org/10.3390/math11071619
Alouani, A. T., & Elfouly, T. (2022). Traumatic Brain Injury (TBI) Detection: Past, Present, and Future. Biomedicines, 10(10), 2472. https://doi.org/10.3390/biomedicines10102472
Bach, M., & Heinrich, S. (2019). Acuity VEP: improved with machine learning. Doc.
Ophthalmol., 139 (2), 113–122. https://doi.org/10.1007/s10633-019-09701-x
Baker, D. H., Simard, M., Saint-Amour, D., & Hess, R. F. (2015). Steady-State Contrast Response Functions Provide a Sensitive and Objective Index of Amblyopic Deficits. Investigative Ophthalmology Visual Science, 56(2), 1208. https://doi.org/10.1167/IOVS.14-15611
Ballesteros Larrota, D. M. (2004). Aplicación de la transformada wavelet discreta en el filtrado de señales bioeléctricas. Umbral Científico, (5), 92–98.
Beniczky, S., & Schomer, D. L. (2020). Electroencephalography: basic biophysical and technological aspects important for clinical applications. Epileptic Disorders, 22(6), 697–715. https://doi.org/10.1684/epd.2020.1217
Brienza, M., & Mecarelli, O. (2019). Neurophysiological Basis of EEG BT. In O. Mecarelli (Ed.), Clinical Electroencephalography (pp. 9–21). Springer International Publishing. https://doi.org/10.1007/978-3-030-04573-9_2
Cadavid, V., Salas, E., Gonzalez, S., Henao, V., Ortega, D., Suarez, J. C., & Ochoa, J. (2021). Captura y análisis de potenciales visuales en estado estacionario usando tecnología portable y de bajo costo. 2021 22nd Symposium on Image, Signal Processing and Artificial Vision, STSIVA 2021 - Conference Proceedings. https://doi.org/10.1109/STSIVA53688.2021.9592014
Carvalho, S. N., Costa, T. B., Uribe, L. F., Soriano, D. C., Yared, G. F., Coradine, L. C., & Attux, R. (2015). Comparative analysis of strategies for feature extraction and classification in SSVEP BCIs. Biomedical Signal Processing and Control, 21, 34–42. https://doi.org/10.1016/J.BSPC.2015.05.008
Chandran, K. S., & Kiruba Angeline, T. (2020). Identification of disease symptoms using taste disorders in electroencephalogram signal. Journal of Computational and Theoretical Nanoscience, 17 (5), 2051–2056.
Fox, M., Barber, C., Keating, D., & Perkins, A. (2014). Comparison of cathode ray tube and liquid crystal display stimulators for use in multifocal VEP. Documenta ophthalmologica. Advances in ophthalmology, 129(2), 115–122. doi:10.1007/S10633-014-9451-0
Guarnizo-Lemus, C. (2008, dec). Análisis de reducción de ruido en señales eeg orientado al reconocimiento de patrones. TecnoLógicas, (21), 67–80. https://doi.org/10.22430/22565337.248
Gunaydin, O., & Ozkan, M. (2010). Design of a Brain Computer Interface system based on electroencephalogra (EEG). 4th European Education and Research Conference (EDERC 2010).
Hemptinne, C., Liu-Shuang, J., Yuksel, D., & Rossion, B. (2019). Rapid Objective Assessment of Contrast Sensitivity and Visual Acuity with Sweep Visual Evoked Potentials and an Extended Electrode Array. Journal of Vision, 19(8), 87. doi: 10.1167/19.8.87
Kadri, A., & Apriani, N. (2022). Electroencephalography Findings in Traumatic Brain Injury. The Open Neurology Journal, 16(1), 1–11. https://doi.org/10.2174/1874205x-v16-e2206100
Kiiski, H. S., Riada, S. N., Lalor, E. C., Gonçalves, N. R., Nolan, H., Whelan, R., Lonergan, R., Kelly, S., O'Brien, M. C., Kinsella, K., Bramham, J., Burke, T., Ó Donnchadha, S., Hutchinson, M., Tubridy, N., & Reilly, R. B. (2016). Delayed P100-Like Latencies in Multiple Sclerosis: A Preliminary Investigation Using Visual Evoked Spread Spectrum Analysis. PloS one, 11(1). https://doi.org/10.1371/JOURNAL.PONE.0146084
Lantz, C. L., & Quinlan, E. M. (2021). High-Frequency Visual Stimulation Primes Gamma Oscillations for Visually Evoked Phase Reset and Enhances Spatial Acuity. Cerebral cortex communications, 2(2), tgab016. https://doi.org/10.1093/texcom/tgab016
Lei;, X., & Liao, K. (2017). Understanding the Influences of EEG Reference: A Large-Scale Brain Network Perspective. Front. Neurosci., 11. https://doi.org/10.3389/fnins.2017.00205
Leuchs, L. (2019, May 3rd). Choosing your reference – and why it matters. Brain Products Press Release. https://pressrelease.brainproducts.com/referencing/
Marcar, V. L., & Jäncke, L. (2018, jan). Stimuli to differentiate the neural response at successive stages of visual processing using the VEP from human visual cortex. Journal of neuroscience methods, 293, 199–209. https://doi.org/10.1016/J.JNEUMETH.2017.09.015
Meigen, T., & Bach, M. (1999). On the statistical significance of electrophysiological steady-state responses. Documenta ophthalmologica. Advances in ophthalmology, 98(3), 207–232. https://doi.org/10.1023/A:1002097208337
Mora-Cortes, A., Ridderinkhof, K. R., & Cohen, M. X. (2018). Evaluating the feasibility of the steady-state visual evoked potential (SSVEP) to study temporal attention. Psychophysiology, 55(5). https://doi.org/10.1111/PSYP.13029
Nicolas-Alonso, L. F., & Gomez-Gil, J. (2012). Brain computer interfaces, a review. Sensors, 12(2), 1211–1279. https://doi.org/10.3390/s120201211
Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R., & Rossion, B. (2015, 5). The steady-state visual evoked potential in vision research: A review. Journal of Vision, 15, 4-4. https://doi.org/10.1167/15.6.4
Nuwer, M. R. (2018). 10-10 electrode system for EEG recording. Clinical Neurophysiology, 129(5), 1103. https://doi.org/10.1016/j.clinph.2018.01.065
O’Hare, L. (2017, feb). Steady-state VEP responses to uncomfortable stimuli. European Journal of Neuroscience, 45(3), 410–422. https://doi.org/10.1111/EJN.13479
Orban, M., Elsamanty, M., Guo, K., Zhang, S., & Yang, H. (2022). A Review of Brain Activity and EEG-Based Brain–Computer Interfaces for Rehabilitation Application. Bioengineering, 9(12). https://doi.org/https://doi.org/10.3390/bioengineering9120768
Ortega, D, Henao, V, & Ochoa, J (2019, April). Ssvep study in monocular and binocular vision. 2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA). https://doi.org/10.1109/STSIVA.2019.8730241
Patil, P. B., & Chavan, M. S. (2012). A wavelet based method for denoising of biomedical signal. International Conference on Pattern Recognition, Informatics and Medical Engineering, PRIME 2012, 278–283. https://doi.org/10.1109/ICPRIME.2012.6208358
Richard, B., Chadnova, E., & Baker, D. H. (2018). Binocular vision adaptively suppresses delayed monocular signals. NeuroImage, 172, 753–765. https://doi.org/10.1016/J.NEUROIMAGE.2018.02.021
Sahel, J. A., Boulanger-Scemama, E., Pagot, C., Arleo, A., Galluppi, F., Martel, J. N., . . .
Roska, B. (2021). Partial recovery of visual function in a blind patient after optogenetic therapy. Nature Medicine, 27(7), 1223–1229. https://doi.org/10.1038/s41591-021-01351-4
Sanchez-Lopez, J., Pedersini, C. A., Di Russo, F., Cardobi, N., Fonte, C., Varalta, V., Prior, M., Smania, N., Savazzi, S., & Marzi, C. A. (2019). Visually evoked responses from the blind field of hemianopic patients. Neuropsychologia, 128, 127–139. https://doi.org/10.1016/J.NEUROPSYCHOLOGIA.2017.10.008
Sarzaeim, F., Hashemzehi, M., Mohammad Masoud Shushtarian, S., Shojaei, A., & Naghib, J. (2022). Flash Visual Evoked Potential as a Suitable Technique to Evaluate the Extent of Injury to Visual Pathway Following Head Trauma. Journal of Ophthalmology and Research, 05(01), 20–23. https://doi.org/10.26502/fjor.2644-00240053
Smith, J. O. (2011). Spectral Audio Signal Processing. Center for Computer Research in Music and Acoustics (CCRMA), 1–674.
Tan, Y., Tong, X., Chen, W., Weng, X., He, S., & Zhao, J. (2018). Vernier But Not Grating Acuity Contributes to an Early Stage of Visual Word Processing. Neuroscience Bulletin, 34 (3), 517–526. https://doi.org/10.1007/s12264-018-0220-z
Woolson, R. F. (2008). Wilcoxon Signed-Rank Test. Wiley Encyclopedia of Clinical Trials, 1–3. https://doi.org/10.1002/9780471462422.EOCT979
Zheng, X., Xu, G., Wang, Y., & Han, C. (2019). Objective and quantitative assessment of visual acuity and contrast sensitivity based on steady-state motion visual evoked potentials using concentric-ring paradigm. Documenta Ophthalmologica., 139. https://doi.org/10.1007/s10633-019-09702-w
Zheng, X., Xu, G., Zhang, K., & Liang, R. (2020). Assessment of Human Visual Acuity Using Visual Evoked Potential: A Review. Sensors (Basel), 20(19). https://doi.org/10.3390/s20195542
Zhong, Y., Wei, H., Chen, L., & Wu, T. (2023, mar). Automated EEG Pathology Detection Based on Significant Feature Extraction and Selection. Mathematics, 11, 1619. https://doi.org/10.3390/math11071619
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