Ke Yufeng, Wang Tao, He Feng, Liu Shuang, Ming Dong
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, People's Republic of China.
Haihe Laboratory of Brain-computer Interaction and Human-machine Integration, Tianjin, People's Republic of China.
J Neural Eng. 2023 Dec 12;20(6). doi: 10.1088/1741-2552/ad0f3d.
. The day-to-day variability of electroencephalogram (EEG) poses a significant challenge to decode human brain activity in EEG-based passive brain-computer interfaces (pBCIs). Conventionally, a time-consuming calibration process is required to collect data from users on a new day to ensure the performance of the machine learning-based decoding model, which hinders the application of pBCIs to monitor mental workload (MWL) states in real-world settings.. This study investigated the day-to-day stability of the raw power spectral density (PSD) and their periodic and aperiodic components decomposed by the Fitting Oscillations and One-Over-F algorithm. In addition, we validated the feasibility of using periodic components to improve cross-day MWL classification performance.. Compared to the raw PSD (69.9% ± 18.5%) and the aperiodic component (69.4% ± 19.2%), the periodic component had better day-to-day stability and significantly higher cross-day classification accuracy (84.2% ± 11.0%).. These findings indicate that periodic components of EEG have the potential to be applied in decoding brain states for more robust pBCIs.
脑电图（EEG）的日常变异性对基于EEG的被动脑机接口（pBCI）中解码人类大脑活动构成了重大挑战。传统上，需要一个耗时的校准过程来在新的一天从用户那里收集数据，以确保基于机器学习的解码模型的性能，这阻碍了pBCI在现实环境中监测心理负荷（MWL）状态的应用。本研究调查了原始功率谱密度（PSD）及其通过拟合振荡和1/f算法分解的周期性和非周期性成分的日常稳定性。此外，我们验证了使用周期性成分来提高跨日MWL分类性能的可行性。与原始PSD（69.9%±18.5%）和非周期性成分（69.4%±19.2%）相比，周期性成分具有更好的日常稳定性和显著更高的跨日分类准确率（84.2%±11.0%）。这些发现表明，EEG的周期性成分有潜力应用于解码脑状态，以实现更稳健的pBCI。
