Blanco Angel David, Chugani Karan, Braboszcz Claire, Kroupi Eleni, Soria-Frisch Aureli
Starlab Barcelona S.L., Neuroscience Business Unit, Barcelona, Spain.
Biol Psychol. 2025 Jul 23;200:109084. doi: 10.1016/j.biopsycho.2025.109084.
This study seeks to assess the applicability of EEG spectral biomarkers in application fields where cognitive characterization is required, e.g. Virtual Reality, User Experience Assessment (UXA), and Ergonomics. It aims to gauge users' cognitive states across varying task settings. We have gathered EEG data from three distinct datasets for this purpose. The first dataset encompasses EEG recordings from 36 participants under two conditions: at rest and while performing arithmetic operations. Additionally, participants were categorized as skilled or unskilled performers, making this dataset valuable for evaluating the effectiveness of different EEG features related to working memory. The second dataset comprises EEG data from 14 participants memorizing different quantities of characters (specifically, 2, 4, 6, or 8 characters) for three seconds. This dataset aims to replicate and assess how the identified biomarkers can distinguish between various levels of working memory within the same participant. The third dataset involves EEG recordings from 27 participants engaged in a 90-minute Virtual Reality (VR) driving task, wherein they needed to maintain the car within the lane amid random deviations. This dataset serves the purpose of evaluating the descriptors' capacity to differentiate between states of high and low attention, as measured by their values before lane deviations. It also facilitates an exploration of how fatigue and time-on-task impact these markers. Our findings indicate that the Theta-to-Alpha ratio (TAR) measured at midline electrodes or as the ratio of frontal theta to parietal alpha effectively characterizes cognitive effort during mental arithmetic and memory tasks. In contrast, the Theta-Alpha-to-Beta Ratio (TA2BR) measured at temporal scalp locations emerges as the most efficient descriptor for assessing heightened vigilance states, particularly in tasks requiring external attention and rapid responses, such as the VR driving task. The influence of time-on-task on descriptor reliability varied depending on participants' performance levels.
本研究旨在评估脑电图频谱生物标志物在需要认知特征描述的应用领域中的适用性，例如虚拟现实、用户体验评估（UXA）和人体工程学。其目的是衡量用户在不同任务设置下的认知状态。为此，我们从三个不同的数据集中收集了脑电图数据。第一个数据集包含36名参与者在两种条件下的脑电图记录：休息时和进行算术运算时。此外，参与者被分为熟练或不熟练的执行者，这使得该数据集对于评估与工作记忆相关的不同脑电图特征的有效性很有价值。第二个数据集包括14名参与者在三秒内记忆不同数量字符（具体为2、4、6或8个字符）的脑电图数据。该数据集旨在复制和评估所识别的生物标志物如何区分同一参与者内不同水平的工作记忆。第三个数据集涉及27名参与者进行90分钟虚拟现实（VR）驾驶任务的脑电图记录，其中他们需要在随机偏差的情况下将汽车保持在车道内。该数据集用于评估描述符区分高注意力和低注意力状态的能力，通过车道偏差前的值来衡量。它还有助于探索疲劳和任务持续时间如何影响这些标志物。我们的研究结果表明，在中线电极处测量的θ与α比率（TAR）或作为额叶θ与顶叶α的比率有效地表征了心算和记忆任务期间的认知努力。相比之下，在颞部头皮位置测量的θ-α与β比率（TA2BR）成为评估提高警觉状态的最有效描述符，特别是在需要外部注意力和快速反应的任务中，如VR驾驶任务。任务持续时间对描述符可靠性的影响因参与者的表现水平而异。
