Yan Zi-Neng, Liu Peng-Ran, Zhou Hong, Zhang Jia-Yao, Liu Song-Xiang, Xie Yi, Wang Hong-Lin, Yu Jin-Bo, Zhou Yu, Ni Chang-Mao, Huang Li, Ye Zhe-Wei
Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
Curr Med Sci. 2024 Dec;44(6):1123-1131. doi: 10.1007/s11596-024-2927-6. Epub 2024 Sep 30.
The brain-computer interface (BCI) system serves as a critical link between external output devices and the human brain. A monitored object's mental state, sensory cognition, and even higher cognition are reflected in its electroencephalography (EEG) signal. Nevertheless, unprocessed EEG signals are frequently contaminated with a variety of artifacts, rendering the analysis and elimination of impurities from the collected EEG data exceedingly challenging, not to mention the manual adjustment thereof. Over the last few decades, the rapid advancement of artificial intelligence (AI) technology has contributed to the development of BCI technology. Algorithms derived from AI and machine learning have significantly enhanced the ability to analyze and process EEG electrical signals, thereby expanding the range of potential interactions between the human brain and computers. As a result, the present BCI technology with the help of AI can assist physicians in gaining a more comprehensive understanding of their patients' physical and psychological status, thereby contributing to improvements in their health and quality of life.
脑机接口(BCI)系统是外部输出设备与人类大脑之间的关键纽带。被监测对象的心理状态、感官认知乃至更高层次的认知都反映在其脑电图(EEG)信号中。然而,未经处理的EEG信号常常受到各种伪迹的污染,这使得从采集到的EEG数据中分析和去除杂质极具挑战性,更不用说人工调整了。在过去几十年里,人工智能(AI)技术的飞速发展推动了BCI技术的进步。源自AI和机器学习的算法显著增强了分析和处理EEG电信号的能力,从而扩大了人脑与计算机之间潜在交互的范围。因此,当前借助AI的BCI技术可以帮助医生更全面地了解患者的身心状况,从而有助于改善他们的健康和生活质量。