Prospects and technical challenges of non-invasive brain-computer interfaces in manned space missions.
10.11817/j.issn.1672-7347.2025.250389
- Author:
Yumeng JU
1
,
2
;
Jiajun LIU
1
;
Zejun LI
1
;
Yiming LIU
1
;
Hairuo HE
1
;
Jin LIU
1
;
Bangshan LIU
1
;
Mi WANG
1
;
Yan ZHANG
1
,
3
Author Information
1. Department of Psychiatry, Second Xiangya Hospital, Central South University, Changsha
2. yumeng.ju@csu.edu.cn.
3. yan.zhang@csu.edu.cn.
- Publication Type:Journal Article
- Keywords:
aerospace;
astronaut;
brain-computer interface;
cognition;
neuromodulation
- MeSH:
Brain-Computer Interfaces;
Humans;
Space Flight;
Astronauts/psychology*;
Neurofeedback;
Cognition;
Electroencephalography;
Man-Machine Systems
- From:
Journal of Central South University(Medical Sciences)
2025;50(8):1363-1370
- CountryChina
- Language:Chinese
-
Abstract:
During long-duration manned space missions, the complex and extreme space environment exerts significant impacts on astronauts' physiological, psychological, and cognitive functions, thereby posing direct risks to mission safety and operational efficiency. As a key bridge between the brain and external devices, brain-computer interface (BCI) technology enables precise acquisition and interpretation of neural signals, offering a novel paradigm for human-machine collaboration in manned spaceflight. Non-invasive BCI technology shows broad application prospects across astronaut selection, mission training, in-orbit task execution, and post-mission rehabilitation. During mission preparation, multimodal signal assessment and neurofeedback training based on BCI can effectively enhance cognitive performance and psychological resilience. During mission execution, BCI can provide real-time monitoring of physiological and psychological states and enable intention-based device control, thereby improving operational efficiency and safety. In the post-mission rehabilitation phase, non-invasive BCI combined with neuromodulation may improve emotional and cognitive functions, support motor and cognitive recovery, and contribute to long-term health management. However, the application of BCI in space still faces challenges, including insufficient signal robustness, limited system adaptability, and suboptimal data processing efficiency. Looking forward, integrating multimodal physiological sensors with deep learning algorithms to achieve accurate monitoring and individualized intervention, and combining BCI with virtual reality and robotics to develop intelligent human-machine collaboration models, will provide more efficient support for space missions.
