Research on auditory neurofeedback technology and its multi-disciplinary applications_Journal of Biomedical Engineering

Authors：

HU Xiuyan ¹ ,  GONG Anmin ^1,2 , SHI Xinyu ¹ , GONG Bowen ¹ , LI Sinan ² , SHI Ting ¹ ,  FU Yunfa ³

1. School of Information Engineering, Engineering University of the Chinese Armed Police Force, Xi’an 710086, P. R. China;
2. School of Life Science and Technology, Xi’an Jiaotong University, Xi’an 710049, P. R. China;
3. Brain Cognition and Brain-Computer Intelligence Integration Group/School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P. R. China;

Corresponding?author：

GONG Anmin, Email: gonganmincapf@163.com

Keywords：

Auditory neurofeedback; Brain-computer interface; Neuromodulation; Signal processing

DOI：

10.7507/1001-5515.202505004

Video：

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Abstract Full text Figures/Tables Video References Cited by

Auditory neurofeedback (ANF) utilizes sound to map brain activity in real-time, guiding individuals to self-regulate neural states via operant conditioning. Compared to visual neurofeedback, ANF offers distinct advantages in millisecond-level response speed and non-visual dependency, significantly enhancing ecological validity and reducing visual fatigue. This paper reviews the physiological basis, signal processing workflows, and three typical experimental paradigms of ANF: threshold regulation, parameter modulation, and state-dependent triggering. Furthermore, it discusses current applications in neurorehabilitation, psychiatric treatment, sports medicine, and auditory cognitive screening. Finally, the paper analyzes challenges regarding mechanistic evidence and parameter standardization, and prospects future trends such as ear electroencephalography-based portable design and multimodal fusion, providing theoretical insights for clinical translation.

Citation： HU Xiuyan, GONG Anmin, SHI Xinyu, GONG Bowen, LI Sinan, SHI Ting, FU Yunfa. Research on auditory neurofeedback technology and its multi-disciplinary applications. Journal of Biomedical Engineering, 2026, 43(3): 633-643. doi: 10.7507/1001-5515.202505004 Copy

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1. Marzbani H, Marateb H, Mansourian M. Methodological note: neurofeedback: a comprehensive review on system design, methodology and clinical applications. Basic Clin Neurosci, 2016, 7(2): 143-158.
2. Sterman M B, Friar L. Suppression of seizures in an epileptic following sensorimotor EEG feedback training. Electroencephalogr Clin Neurophysiol, 1972, 33(1): 89-95.
3. Sitaram R, Ros T, Stoeckel L, et al. Closed-loop brain training: the science of neurofeedback. Nat Rev Neurosci, 2017, 18(2): 86-100.
4. Arns M, De Ridder S, Strehl U, et al. Efficacy of neurofeedback treatment in ADHD: the effects on inattention, impulsivity and hyperactivity: a meta-analysis. Clin EEG Neurosci, 2009, 40(3): 180-189.
5. Croft R J, Barry R J. Removal of ocular artifact from the EEG: a review. Neurophysiol Clin, 2000, 30(1): 5-19.
6. Wiyor H D, Ntuen C A, Stephens J D W, et al. Classifying visual fatigue severity based on neurophysiological signals and psychophysiological ratings. Int J Hum Factors Ergon, 2013, 2(1): 11.
7. Yu B, You Y, Li Y, et al. Effects of intermittent visual feedback on EEG characteristics during motor preparation and execution in a goal-directed task. Front Hum Neurosci, 2024, 18: 1371476.
8. Shabani F, Nisar S, Philamore H, et al. Haptic vs. visual neurofeedback for brain training: a proof-of-concept study. IEEE Trans Haptics, 2021, 14(2): 297-302.
9. Saha S, Mamun K A, Ahmed K, et al. Progress in brain computer interface: challenges and opportunities. Front Syst Neurosci, 2021, 15: 578875.
10. Ejaz O, Hasan M A, Raees F, et al. Assessing the effectiveness of audio-visual vs. visual neurofeedback for attention enhancement: a pilot study with neurological, behavioural, and neuropsychological measures. Brain Topogr, 2025, 38(1): 7.
11. Faller J, Cummings J, Saproo S, et al. Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task. Proc Natl Acad Sci USA, 2019, 116(13): 6482-6490.
12. Güntensperger D, Thüring C, Meyer M, et al. Neurofeedback for tinnitus treatment – review and current concepts. Front Aging Neurosci, 2017, 9: 386.
13. 何峰, 何蓓蓓, 王仲朋, 等. 腦-機交互運動訓練的神經反饋方法及康復應用. 中國生物醫學工程學報, 2021, 40(6): 719-730.
14. Shelton J, Kumar G P. Comparison between auditory and visual simple reaction times. Neurosci Med, 2010, 1(1): 30-32.
15. Jain A, Bansal R, Kumar A, et al. A comparative study of visual and auditory reaction times on the basis of gender and physical activity levels of medical first year students. Int J Appl Basic Med Res, 2015, 5(2): 124-127.
16. N??t?nen R, Picton T. The N1 wave of the human electric and magnetic response to sound: a review and an analysis of the component structure. Psychophysiology, 1987, 24(4): 375-425.
17. Di Russo F, Martínez A, Sereno M I, et al. Cortical sources of the early components of the visual evoked potential. Hum Brain Mapp, 2001, 15(2): 95-111.
18. Molholm S, Ritter W, Murray M M, et al. Multisensory auditory-visual interactions during early sensory processing in humans: a high-density electrical mapping study. Cogn Brain Res, 2002, 14(1): 115-128.
19. Urigüen J A, Garcia-Zapirain B. EEG artifact removal—state-of-the-art and guidelines. J Neural Eng, 2015, 12(3): 031001.
20. Mullen T R, Kothe C A E, Chi M, et al. Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans Biomed Eng, 2015, 62(11): 2553-2567.
21. Liu M, Ahmad F, Hao J, et al. Enhancing P300 feature extraction through multi-scale separable convolution//SoutheastCon 2024. Atlanta: IEEE, 2024: 1420-1425.
22. Khambampati S, Dondapati S, Kalyan Bhuma C, et al. Frequency and time domain EEG analysis for prognostication of postanoxic comatose patients//2023 Computing in Cardiology Conference. Atlanta: IEEE, 2023: 1-4.
23. Saha P K, Rahman M A, Alam M K, et al. Common spatial pattern in frequency domain for feature extraction and classification of multichannel EEG signals. SN Comput Sci, 2021, 2(3): 149.
24. Hassan M, Wendling F. Electroencephalography source connectivity: aiming for high resolution of brain networks in time and space. IEEE Signal Process Mag, 2018, 35(3): 81-96.
25. Islam M, Lee T. Functional connectivity analysis in multi-channel EEG for emotion detection with phase locking value and 3D CNN//2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society. Sydney: IEEE, 2023: 1-4.
26. Ramirez R, Palencia-Lefler M, Giraldo S, et al. Musical neurofeedback for treating depression in elderly people. Front Neurosci, 2015, 9: 354.
27. Nan W, Yang L, Wan F, et al. Alpha down-regulation neurofeedback training effects on implicit motor learning and consolidation. J Neural Eng, 2020, 17(2): 026014.
28. Lotte F, Bougrain L, Cichocki A, et al. A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J Neural Eng, 2018, 15(3): 031005.
29. Lawhern V J, Solon A J, Waytowich N R, et al. EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J Neural Eng, 2018, 15(5): 056013.
30. Rahman N, Khan D M, Masroor K, et al. Advances in brain-computer interface for decoding speech imagery from EEG signals: a systematic review. Cogn Neurodyn, 2024, 18(6): 3565-3583.
31. Luo W, Al-qaness M A A, Li Y, et al. EEG-based brain-computer interface: fundamentals, methods, applications, and challenges. IEEE Internet Things J, 2025, 12(24): 52024-52041.
32. Chaudhary U, Vlachos I, Zimmermann J B, et al. Spelling interface using intracortical signals in a completely locked-in patient enabled via auditory neurofeedback training. Nat Commun, 2022, 13(1): 1236.
33. Gruzelier J H. EEG-neurofeedback for optimising performance. I: a review of cognitive and affective outcome in healthy participants. Neurosci Biobehav Rev, 2014, 44: 124-141.
34. 王鵬飛. 神經反饋訓練對ADHD兒童腦電信號同步特性影響的研究. 天津: 天津醫科大學, 2019.
35. 李昕, 蘇芮, 史春燕, 等. 神經反饋訓練改善輕度認知障礙腦功能狀態研究. 高技術通訊, 2020, 30(12): 1292-1299.
36. Hantzsch L, Parrell B, Niziolek C A. A single exposure to altered auditory feedback causes observable sensorimotor adaptation in speech. eLife, 2022, 11: e73694.
37. Kovacevic N, Ritter P, Tays W, et al. ‘My virtual dream’: collective neurofeedback in an immersive art environment. PLoS One, 2015, 10(7): e0130129.
38. Apavou F, Bouchara T, Bourdot P. Accessibility of shooting task for blind and visually impaired: a sonification method comparison//Proceedings of the 29th International Conference on Auditory Display. Troy: International Community for Auditory Display, 2024: 81-88.
39. Strehl U. What learning theories can teach us in designing neurofeedback treatments. Front Hum Neurosci, 2014, 8: 894.
40. Fernández T, Bosch-Bayard J, Harmony T, et al. Neurofeedback in learning disabled children: visual versus auditory reinforcement. Appl Psychophysiol Biofeedback, 2016, 41(1): 27-37.
41. Ros T, Moseley M J, Bloom P A, et al. Optimizing microsurgical skills with EEG neurofeedback. BMC Neurosci, 2009, 10(1): 87.
42. McCreadie K A, Coyle D H, Prasad G. Is sensorimotor BCI performance influenced differently by mono, stereo, or 3-D auditory feedback? IEEE Trans Neural Syst Rehabil Eng, 2014, 22(3): 431-440.
43. Cheng M Y, Huang C J, Chang Y K, et al. Sensorimotor rhythm neurofeedback enhances golf putting performance. J Sport Exerc Psychol, 2015, 37(6): 626-636.
44. Baykara E, Ruf C A, Fioravanti C, et al. Effects of training and motivation on auditory P300 brain-computer interface performance. Clin Neurophysiol, 2016, 127(1): 379-387.
45. Ogino M, Mitsukura Y. A mobile auditory brain-computer interface system with sequential auditory feedback//2020 IEEE 9th Global Conference on Consumer Electronics. Kobe: IEEE, 2020: 590-593.
46. Ros T, Baars B J, Lanius R A, et al. Tuning pathological brain oscillations with neurofeedback: a systems neuroscience framework. Front Hum Neurosci, 2014, 8: 1008.
47. Sidhu A, Cooke A. Electroencephalographic neurofeedback training can decrease conscious motor control and increase single and dual-task psychomotor performance. Exp Brain Res, 2021, 239(1): 301-313.
48. Nieuwboer A, Kwakkel G, Rochester L, et al. Cueing training in the home improves gait-related mobility in Parkinson's disease: the RESCUE trial. J Neurol Neurosurg Psychiatry, 2007, 78(2): 134-140.
49. Carrick F R, Pagnacco G, Hankir A, et al. The treatment of autism spectrum disorder with auditory neurofeedback: a randomized placebo controlled trial using the Mente Autism device. Front Neurol, 2018, 9: 537.
50. Tosti B, Corrado S, Mancone S, et al. Integrated use of biofeedback and neurofeedback techniques in treating pathological conditions and improving performance: a narrative review. Front Neurosci, 2024, 18: 1358481.
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Research on auditory neurofeedback technology and its multi-disciplinary applications

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