A Family Emotional Support System for MCS Patients Based on an EEG-to-Visual Translation Mechanism: Design, Implementation, and Preliminary Validation
Source abstract
Abstract
(1) Patients in a minimally conscious state (MCS) and their families face prolonged emotional distress and psychological challenges due to the uncertainty of recovery and limited means of emotional communication. This study aims to develop an EEG-based emotion visualization system to support affected families by translating patients’ neural activity into perceivable emotional imagery. (2) Using simulated MCS patient EEG data corresponding to different emotional states, we designed a dynamic visual interface via TouchDesigner to convert bio-signals into real-time emotional animations. User tests involving questionnaires and interviews were conducted to evaluate the system’s performance. (3) The results demonstrate that the system accurately conveys emotional states, enhances caregivers’ perception of patients’ internal conditions, and significantly alleviates family members’ anxiety. (4) These findings suggest that EEG-based emotion visualization offers a viable and compassionate tool for supporting MCS families, providing new pathways for interdisciplinary research combining neuroscience and design while establishing a foundation for future clinical and home-care applications.
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Research context
OpenAlex topics: EEG and Brain-Computer Interfaces · Functional Brain Connectivity Studies · Neurological disorders and treatments
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Industrial Design, Hubei University of Technology, Wuhan 430068, China
Funding
Humanities and Social Science Fund of Ministry of Education of China
References (57)
Show source-provided references
1. An EEG-Based Brain Computer Interface for Emotion Recognition and Its Application in Patients with Disorder of Consciousness10.1109/TAFFC.2019.2901456 ↗
2. A study of the psychological distress in family caregivers of patients with prolonged disorders of consciousness during in-hospital rehabilitation10.1177/0269215514521826 ↗
3. Coping and distress in caregivers of patients with disorders of consciousness10.3109/02699052.2013.793402 ↗
4. Gosseries, O., Schnakers, C., Vanhaudenhuyse, A., Martial, C., Aubinet, C., Charland-Verville, V., Thibaut, A., Annen, J., Ledoux, D., and Laureys, S. (2023). Needs and Quality of Life of Caregivers of Patients with Prolonged Disorders of Consciousness. Brain Sci., 13.10.3390/brainsci13020308 ↗
5. Clinical Application and Mechanism Research on Acupuncture and Moxibustion for Disorders of Consciousness: A Review
6. Factors Affecting Psychological Distress in Family Caregivers of Critically Ill Patients: A Qualitative Study10.4037/ajcc2023593 ↗
7. Hope in the context of life-threatening illness and the end of life10.1016/j.copsyc.2022.101513 ↗
8. Informal Caregivers of Patients with Disorders of Consciousness: A Qualitative Study of Communication Experiences and Information Needs with Physicians10.1007/s12152-022-09503-0 ↗
9. Caregivers’ lived experience in trying to read slight movements in a child with severe brain injury: A phenomenological study10.1111/jocn.14255 ↗
10. Family surrogate decision-makers’ perspectives in decision-making of patients with disorders of consciousness10.1080/09602011.2022.2116058 ↗
11. Surrogate expectations in severe brain injury10.3109/02699052.2013.804201 ↗
12. Steppacher, I., and Kissler, J. (2018). A problem shared is a problem halved? Comparing burdens arising for family caregivers of patients with disorders of consciousness in institutionalized versus at home care. BMC Psychol., 6.10.1186/s40359-018-0272-x ↗
13. Bedside detection of awareness in the vegetative state: A cohort study10.1016/S0140-6736(11)61224-5 ↗
14. Detecting Awareness in the Vegetative State10.1126/science.1130197 ↗
15. The neural correlates of arousal: Ventral posterolateral nucleus-global transient co-activation10.1016/j.celrep.2023.113633 ↗
16. Effect of transcutaneous auricular vagus nerve stimulation on patients with prolonged disorders of consciousness
17. A Comparative Study on the Clinical Effects of Short-term and Long-term Spinal Cord Stimulation in Patients with Prolonged Disorders of Consciousness
18. Li, R., Yang, D., Fang, F., Hong, K.S., Reiss, A.L., and Zhang, Y. (2022). Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review. Sensors, 22.10.3390/s22155865 ↗
19. Burden of caregivers of patients in Vegetative State and Minimally Conscious State10.1111/j.1600-0404.2012.01666.x ↗
20. The psychological impact of prolonged disorders of consciousness on caregivers: A systematic review of quantitative studies10.1177/0269215517695372 ↗
21. Perception of social support among family caregivers of vegetative patients: A qualitative study10.1016/j.concog.2016.02.015 ↗
22. Caregiving for Patients in Vegetative and Minimally Conscious States: Perceived Burden as a Mediator in Caregivers’ Expression of Needs and Symptoms of Depression and Anxiety10.1007/s10880-014-9399-y ↗
23. Post-discharge plight of patients with chronic disorders of consciousness: A systematic review of socioeconomic and health aspects
24. Abhang, P.A., Gawali, B.W., and Mehrotra, S.C. (2016). Introduction to EEG- and Speech-Based Emotion Recognition, Academic Press.10.1016/B978-0-12-804490-2.00007-5 ↗
25. Mindfulness meditation alters neural oscillations independently of arousal10.1016/j.ijpsycho.2024.112439 ↗
26. Emotion Classification Based on Biophysical Signals and Machine Learning Techniques
27. Wang, X., Ren, Y., Luo, Z., He, W., Hong, J., and Huang, Y. (2023). Deep learning-based EEG emotion recognition: Current trends and future perspectives. Front. Psychol., 14.10.3389/fpsyg.2023.1126994 ↗
28. Deep Learning for EEG-based Emotion Recognition: A Survey
29. Review of Research on Emotion Recognition Based on EEG Signals
30. (2025, June 14). Derivative [Internet]. 2024. The Echo Wave Project: Visualizing Mental Health with TouchDesigner. Available online: https://derivative.ca/community-post/echo-wave-project-visualizing-mental-health-touchdesigner/70734.
31. Affective Visualization Design: Leveraging the Emotional Impact of Data10.1109/TVCG.2023.3327385 ↗
32. Qin, C.Y., Constantinides, M., Aiello, L.M., and Quercia, D. (2020, January 25–30). Heartbees: Visualizing crowd affects. Proceedings of the IEEE VIS Arts Program (VISAP), Salt Lake City, UT, USA.10.1109/VISAP51628.2020.00007 ↗
33. Marín-Morales, J., Higuera-Trujillo, J.L., Greco, A., Guixeres, J., Llinares, C., Scilingo, E.P., Alcañiz, M., and Valenza, G. (2018). Affective computing in virtual reality: Emotion recognition from brain and heartbeat dynamics using wearable sensors. Sci. Rep., 8.10.1038/s41598-018-32063-4 ↗
34. Xu, Z., and Cho, Y. (2023). Exploring Artistic Visualization of Physiological Signals for Mindfulness and Relaxation: A Pilot Study. arXiv.
35. Review of electroencephalography signals approaches for mental stress assessment
36. Color-Emotion Associations in Art: Fuzzy Approach10.1109/ACCESS.2024.3375361 ↗
37. Rouw, R., Case, L., Gosavi, R., and Ramachandran, V. (2014). Color associations for days and letters across different languages. Front. Psychol., 5.10.3389/fpsyg.2014.00369 ↗
38. ISO [Internet] (2025, June 14). ISO 9241-303:2011. Available online: https://www.iso.org/standard/57992.html.
39. (2025, June 14). EEG—Electroencephalography—BCI | NeuroSky [Internet]. Available online: https://neurosky.com/biosensors/eeg-sensor/.
40. A Tale of Single-Channel Electroencephalography: Devices, Datasets, Signal Processing, Applications, and Future Directions
41. Stability, change, and reliable individual differences in electroencephalography measures: A lifespan perspective on progress and opportunities10.1016/j.neuroimage.2023.120116 ↗
42. Advances in methods and applications of electroencephalogram microstate analysis
43. Cross subject personality assessment based on electroencephalogram functional connectivity and domain adaptation
44. Identifying Stable Patterns over Time for Emotion Recognition from EEG10.1109/TAFFC.2017.2712143 ↗
45. Emotional consciousness preserved in patients with disorders of consciousness?10.1007/s10072-019-03848-w ↗
46. Pan, J., Xie, Q., Huang, H., He, Y., Sun, Y., Yu, R., and Li, Y. (2018). Emotion-related consciousness detection in patients with disorders of consciousness through an EEG-based BCI system. Front. Hum. Neurosci., 12.10.3389/fnhum.2018.00198 ↗
47. Domain-Specific Knowledge Systems in the Brain: The Animate-Inanimate Distinction10.1162/089892998563752 ↗
48. De Luca, R., Lauria, P., Bonanno, M., Corallo, F., Rifici, C., Castorina, M.V., Trifirò, S., Gangemi, A., Lombardo, C., and Quartarone, A. (2023). Neurophysiological and Psychometric Outcomes in Minimal Consciousness State after Advanced Audio–Video Emotional Stimulation: A Retrospective Study. Brain Sci., 13.10.3390/brainsci13121619 ↗
49. Systematic mapping study of tools to identify emotions and personality traits10.1007/s44163-025-00271-3 ↗
50. Gao, Y., Xue, Y., and Gao, J. (2025). Emotion recognition from multichannel EEG signals based on low-rank subspace self-representation features. Biomed. Signal Process. Control., 99.10.1016/j.bspc.2024.106877 ↗
51. Zhang, Z., Fort, J.M., and Giménez Mateu, L. (2024). Mini review: Challenges in EEG emotion recognition. Front. Psychol., 14.10.3389/fpsyg.2023.1289816 ↗
52. Deep Neural Networks for Automatic Sleep Stage Classification and Consciousness Assessment in Patients with Disorder of Consciousness10.1109/TCDS.2024.3382109 ↗
53. Wang, Z., Yu, J., Gao, J., Bai, Y., and Wan, Z. (2024). MutaPT: A Multi-Task Pre-Trained Transformer for Classifying State of Disorders of Consciousness Using EEG Signal. Brain Sci., 14.10.3390/brainsci14070688 ↗
54. The effects of user factors and symbol referents on public symbol design using the stereotype production method10.1016/j.apergo.2011.05.007 ↗
55. Liu, Z., Shore, J., Wang, M., Yuan, F., Buss, A., and Zhao, X. (2021). A systematic review on hybrid EEG/fNIRS in brain-computer interface. Biomed. Signal Process. Control., 68.10.1016/j.bspc.2021.102595 ↗
56. EEG and fNIRS emotion recognition based on modality attention graph convolution feature fusion
57. Rakkolainen, I., Farooq, A., Kangas, J., Hakulinen, J., Rantala, J., Turunen, M., and Raisamo, R. (2021). Technologies for Multimodal Interaction in Extended Reality—A Scoping Review. Multimodal Technol. Interact., 5.10.3390/mti5120081 ↗