A Wireless EEG System for Neurofeedback Training
Source abstract
Abstract
This paper presents a mobile, easy-to-maintain wireless electroencephalograph (EEG) system designed for work with children in a school environment. This EEG data acquisition platform is a small-sized, battery-powered system with a high sampling rate that is scalable to different channel numbers. The system was validated in a study of live z-score neurofeedback training for quantitative EEG (zNF-qEEG) for typical-reading children and those with developmental dyslexia (DD). This system reads and controls real-time neurofeedback (zNF) signals, synchronizing visual stimuli (low spatial frequency (LSF) illusions) with the alpha/theta (z-α/θ) score neural oscillations. The NF sessions were applied during discrimination of LSF illusions with different contrasts. Visual feedback was provided with color cues to remodulate neural activity in children with DD and their cognitive abilities. The combined zNF-qEEG and training with different visual magnocellular and parvocellular tasks (VTs) compensated for the deficits in the temporal areas affecting the occipitotemporal pathway more in the left-hemispheric ventral brain areas of the post-training children with dyslexia in the low-contrast LSF illusion and dorsal dysfunction in the high-contrast LSF illusion. The better α/θ scores for postD in the temporoparietal and middle occipital regions can be associated with an improvement in special frequency processing, while the better scores in the precentral and parietal cortices were due to an advancement in the temporal processing of the illusion. The improvements in the reading speeds were twice as high after 4 months of qEEG z-NF-VT training, with three times fewer omitted words and errors.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Neural dynamics and brain function · Neuroscience and Neural Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Neurobiology, Bulgarian Academy of Sciences, Acad. G. Bonchev Str., bl. 23, 1113 Sofia, Bulgaria
Funding
National Science Fund of the Ministry of Education and Science
References (61)
Show source-provided references
1. Soufineyestani, M., Dowling, D., and Khan, A. (2020). Electroencephalography (EEG) Technology, Applications and Available Devices. Appl. Sci., 10.10.3390/app10217453 ↗
2. Taskov, T., and Dushanova, J. (2022). Improving functional connectivity in developmental dyslexia through combined neurofeedback and visual training. Symmetry, 14.10.3390/sym14020369 ↗
3. Rakhmatulin, I. (2020). Deep learning and machine learning for EEG signal processing on the example of recognizing the disease of alcoholism. medRxiv.10.2139/ssrn.3717324 ↗
4. The impact of coherence neurofeedback on reading delays in learning disabled children: A randomized controlled study10.15540/nr.2.4.168 ↗
5. Increasing trend of wearables and multimodal interface for human activity monitoring: A review10.1016/j.bios.2016.12.001 ↗
6. Taskov, T., and Dushanova, J. (2020). Reading-network in developmental dyslexia before and after visual training. Symmetry, 12.10.3390/sym12111842 ↗
7. Taskov, T., and Dushanova, J. (2021). Functional connectivity in developmental dyslexia during speed discrimination. Symmetry, 13.10.3390/sym13050749 ↗
8. Dushanova, J., Lalova, Y., Kalonkina, A., and Tsokov, S. (2020). Speech–brain frequency entrainment of dyslexia with and without phonological deficits. Brain Sci., 10.10.3390/brainsci10120920 ↗
9. Vo, T.T., Nguyen, N.P., and Vo Van, T. (2017, January 27–29). WEEGEE: Wireless 8-Channel EEG Recording Device. Proceedings of the BME 2017: 6th International Conference on the Development of Biomedical Engineering in Vietnam (BME6), Ho Chi Minh, Vietnam. Available online: https://link.springer.com/chapter/10.1007/978-981-10-4361-1_106.10.1007/978-981-10-4361-1_106 ↗
10. Vo, H., Dang, L., Nguyen, I., and Huynh, V. (2019, January 12–13). A Survey of Machine Learning algorithms in EEG. Proceedings of the 2019 6th NAFOSTED Conference on Information and Computer Science (NICS), Hanoi, Vietnam.10.1109/NICS48868.2019.9023884 ↗
11. Review of challenges associated with the EEG artifact removal methods10.1016/j.bspc.2021.102741 ↗
12. Nonlinear prediction as a tool for tracking the dynamics of single-trial readiness potentials10.1016/S0165-0270(96)00103-3 ↗
13. Guideline twelve: Guidelines for long-term monitoring for epilepsy
14. Wearable EEG: What is it, why is it needed and what does it entail?
15. Development of 16-channels Compact EEG System Using Real-time High-speed Wireless Transmission10.4236/eng.2013.55B019 ↗
16. Pinho, F., Correia, J.H., Sousa, N.J., Cerqueira, J.J., and Dias, N.S. (2014, January 14–16). Wireless and wearable EEG acquisition platform for ambulatory monitoring. Proceedings of the IEEE 3nd International Conference on Serious Games and Applications for Health (SeGAH), Rio de Janeiro, Brazil.10.1109/SeGAH.2014.7067078 ↗
17. Senevirathna, B., Berman, L., Bertoni, N., Pareschi, F., Mangia, M., Rovatti, R., Setti, G., Simon, J., and Abshire, P. (2016, January 22–25). Low cost mobile EEG for characterization of cortical auditory responses. Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS), Montreal, QC, Canada.10.1109/ISCAS.2016.7527437 ↗
18. NeuroMonitor: A low-power, wireless, wearable EEG device with DRL-less AFE10.1049/iet-cds.2016.0256 ↗
19. Uktveris, T., and Jusas, V. (2018). Development of a Modular Board for EEG Signal Acquisition. Sensors, 18.10.3390/s18072140 ↗
20. Rashid, U., Niazi, I., Signal, N., and Taylor, D. (2018). An EEG experimental study evaluating the performance of Texas Instruments ADS1299. Sensors, 18.10.3390/s18113721 ↗
21. Vargas, J.F., Brainwear, H.B., Zhou, B., and Lukowicz, P. Brainwear: Towards multi-modal garment integrated EEG. 2021 International Symposium on Wearable Computers, Association for Computing Machinery.10.1145/3460421.3478814 ↗
22. Feng, S., Tang, M., Quivira, F., Dyson, T., Cuckov, F., and Schirner, G. (2016, January 1–7). EEGu2: An embedded device for brain/body signal acquisition and processing. Proceedings of the International Symposium on Rapid System Prototyping (RSP), Pittsburgh, PA, USA.10.1145/2990299.2990304 ↗
23. Data acquisition system of 16-channel EEG based on ATSAM3X8E ARM Cortex-M3 32-bit microcontroller and ADS129910.1063/1.4991253 ↗
24. Uchitel, J., Vidal-Rosas, E.E., Cooper, R.J., and Zhao, H. (2021). Wearable, Integrated EEG–fNIRS Technologies: A Review. Sensors, 21.10.3390/s21186106 ↗
25. Rakhmatulin, I., and Gan, Y. (2021). Review Dry and Non-Contact EEG Electrodes for 2010–2021 Years. Preprints, 2021030555.10.20944/preprints202103.0555.v1 ↗
26. Texas Instruments (2015, January 01). ADS129x Low-Power, 8-Channel, 24-Bit Analog Front-End for Biopotential Measurements. Available online: https://www.ti.com/document-viewer/ADS1298/datasheet/specifications#SBAS459528.
27. Nordic Semiconductor (2010, April 01). NRF24L01+ Single Chip 2.4GHz Transceiver. Available online: https://www.micro-semiconductor.com/datasheet/ce-NRF24L01P-R7.pdf.
28. Microchip Technology Inc (2008, February 01). PIC18F2420-2520 Microcontrollers. Available online: https://ww1.microchip.com/downloads/aemDocuments/documents/OTH/ProductDocuments/DataSheets/PIC18F2420-2520-4420-4520-28-40-44-Pin-Microcontrollers-with-XLP-Technology-30009613F.pdf.
29. (2017, February 10). Brain Rhythm Inc. Available online: https://www.bri.com.tw.
30. (2018, February 14). Easycap GmbH. Available online: https://www.easycap.de/.
31. Automated cortical projection of EEG sensors: Anatomical correlation via the international 10-10 system10.1016/j.neuroimage.2009.02.006 ↗
32. Algorithm to find high density EEG scalp coordinates and analysis of their correspondence to structural and functional regions of the brain10.1016/j.jneumeth.2014.04.020 ↗
33. Berhardt, L.V. (2022). Protocol for Visual Intervention of Developmental Dyslexia. Advances in Medicine and Biology, Nova Science Pub.10.52305/GKLN5926 ↗
34. Attention, executive function, behavior, and electrocortical function, significantly improved with 19-channel z-score neurofeedback in a clinical setting: A pilot study10.1177/1087054715577135 ↗
35. Short-delay Neurofeedback facilitates training of the parietal alpha rhythm10.1088/1741-2552/abc8d7 ↗
36. Patil, A.U., Madathil, D., Fan, Y.-T., Tzeng, O.J.L., Huang, C.-M., and Huang, H.-W. (2022). Neurofeedback for the Education of Children with ADHD and Specific Learning Disorders: A Review. Brain Sci., 12.10.3390/brainsci12091238 ↗
37. Prestimulus oscillatory activity in the alpha band predicts visual discrimination ability10.1523/JNEUROSCI.1853-07.2008 ↗
38. Dynamic regulation of interregional cortical communication by slow brain oscillations during working memory10.1038/s41467-019-12057-0 ↗
39. When two are better than one: Bilateral mesial temporal lobe contributions are associated with better vocabulary skills in children and adolescents10.1016/j.bandl.2018.06.001 ↗
40. Attentional selection of location and modality in vision and touch modulates low-frequency activity in associated sensory cortices10.1152/jn.00973.2011 ↗
41. Investigating the stability of and relationships among global/local processing measures10.3758/s13414-012-0416-7 ↗
42. Maksimenko, V., Kuc, A., Frolov, N.S., Hramov, A., Pisarchik, A., and Lebedev, M. (2020). Neuronal adaptation in the course of the prolonged task improves visual stimuli processing. bioRxiv.10.1101/2020.04.07.029959 ↗
43. Weak language lateralization affects both verbal and spatial skills: An fMRI study in 297 subjects10.1016/j.neuropsychologia.2014.10.010 ↗
44. Dorsal and ventral attention systems: Distinct neural circuits but collaborative roles10.1177/1073858413494269 ↗
45. Asymmetrical hemisphere activation enhances global-local processing10.1016/j.bandc.2013.09.012 ↗
46. Neuronal activity in early visual areas during global and local processing: A comment on Heinze, Hinrichs, Scholz, Burchert and Mangun10.1162/089892900562039 ↗
47. The neural bases of spatial frequency processing during scene perception10.3389/fnint.2014.00037 ↗
48. Neural correlates of sustained spatial attention in human early visual cortex10.1152/jn.00677.2006 ↗
49. Hemispheric asymmetries for different components of global/local attention occur in distinct temporo-parietal loci10.1093/cercor/bhh187 ↗
50. Exploring the parahippocampal cortex response to high and low spatial frequency spaces10.1097/WNR.0b013e328353766a ↗
51. Top-down facilitation of visual recognition10.1073/pnas.0507062103 ↗
52. Flicker fusion thresholds as a clinical identifier of a magnocellular-deficit dyslexic subgroup10.1038/s41598-020-78552-3 ↗
53. A mobile app that uses neurofeedback and multi-sensory learning methods improve reading abilities in dyslexia: A pilot study10.1080/21622965.2021.1908897 ↗
54. Raichev, P., Geleva, T., Valcheva, M., Rasheva, M., and Raicheva, M. (2005). Protocol on neurological and neuropsychological studies of children with specific learning disabilities. Integr. Learn. Resour. Teach., (In Bulgarian).
55. Matanova, V., and Todorova, E. (2013). DDE-2 Test Battery for Evaluation of Dyslexia of Development - Bulgarian Adaptation, OS Bulgaria Ltd.. Available online: https://www.giuntipsy.bg/bg/prod-19-testova-bateriq-za-ocenka-nadisleksiq-na-razvitieto.htm.
56. Sartori, G., Remo, J., and Tressoldi, P.E. (2007). Updated and revised edition for the evaluation of dyslexia. DDE-2, Battery for the Developmental Dyslexia and Evolutionary Disorders-2, 1995, Giunti, O.S.
57. Tyubele, S., and Iossifova, R. (2016). Normative indicators for the test battery for a written speech assessment. Logopedical Diagnostics (30-38), Rommel Publishing House.
58. Raven, J., Raven, J.C., and Court, J.H. (1998). Manual for Raven’s Progressive Matrices and Vocabulary Scales. Section 2: The Coloured Progressive Matrices, Oxford Psychologists Press.
59. Girolami-Boulinier, A. (1985). Contrôle des Aptitudes à la Lecture et àl’Ecriture (CALE), CALE. (In French).
60. Yakimova, R. (2004). Narusheniya na Pismenata rech. Abnormalities of Written Speech, Rommel Publishing House. (In Bulgarian).
61. A classification of hand preference by association analysis10.1111/j.2044-8295.1970.tb01248.x ↗