Barry Robert J, De Blasio Frances M, Duda Alexander T, Munford Beckett S
Brain & Behaviour Research Institute, School of Psychology, University of Wollongong, Wollongong 2522, Australia.
Sensors (Basel). 2025 Mar 11;25(6):1733. doi: 10.3390/s25061733.
This study builds on the early brain dynamics work of Erol Başar, focusing on the human electroencephalogram (EEG) in relation to the generation of event-related potentials (ERPs) and behaviour. Scalp EEG contains not only oscillations but non-wave noise elements that may not relate to functional brain activity. These require identification and removal before the true impacts of brain oscillations can be assessed. We examined EEG/ERP/behaviour linkages in young adults during an auditory equiprobable Go/No-Go task. Forty-seven university students participated while continuous EEG was recorded. Using the algorithm, valid estimates of pink noise (PN) and white noise (WN) were obtained from each participant's prestimulus EEG spectra; within-participant subtraction revealed noise-free oscillation spectra. Frequency principal component analysis (f-PCA) was used to obtain noise-free frequency oscillation components. Go and No=Go ERPs were obtained from the poststimulus EEG, and separate temporal (t)-PCAs obtained their components. Exploratory multiple regression found that alpha and beta prestimulus oscillations predicted Go N2c, P3b, and SW1 ERP components related to the imperative Go response, while PN impacted No-Go N1b and N1c, facilitating early processing and identification of the No-Go stimulus. There were no direct effects of prestimulus EEG measures on behaviour, but the EEG-affected Go N2c and P3b ERPs impacted Go performance measures. These outcomes, derived via our mix of novel methodologies, encourage further research into natural frequency components in the noise-free oscillations immediately prestimulus, and how these affect task ERP components and behaviour.
本研究基于埃罗尔·巴萨尔早期的脑动力学研究，聚焦于与事件相关电位（ERP）产生及行为相关的人类脑电图（EEG）。头皮脑电图不仅包含振荡，还包含可能与脑功能活动无关的非波噪声成分。在评估脑振荡的真正影响之前，需要识别并去除这些成分。我们在一项听觉等概率的“Go/No-Go”任务中研究了年轻人的脑电图/事件相关电位/行为之间的联系。47名大学生参与实验，同时记录连续脑电图。使用该算法，从每个参与者刺激前的脑电图频谱中获得了粉红噪声（PN）和白噪声（WN）的有效估计值；参与者内部相减得到了无噪声振荡频谱。频率主成分分析（f-PCA）用于获得无噪声频率振荡成分。从刺激后的脑电图中获得“Go”和“No-Go”事件相关电位，并通过单独的时间（t）主成分分析获得其成分。探索性多元回归发现，刺激前的α和β振荡预测了与指令性“Go”反应相关的“Go”事件相关电位成分N2c、P3b和SW1，而粉红噪声影响了“No-Go”事件相关电位成分N1b和N1c，促进了对“No-Go”刺激的早期处理和识别。刺激前脑电图测量对行为没有直接影响，但受脑电图影响的“Go”事件相关电位成分N2c和P3b影响了“Go”任务的表现指标。通过我们混合使用的新颖方法得出的这些结果，鼓励进一步研究刺激前无噪声振荡中的自然频率成分，以及这些成分如何影响任务事件相关电位成分和行为。
