Department of Psychology, HSE University, Moscow, 101000, Russia.
Institute of Higher Nervous Activity and Neurophysiology of the Russian Academy of Sciences, Moscow, 117485, Russia.
Sci Rep. 2024 Mar 7;14(1):5605. doi: 10.1038/s41598-024-55163-w.
Machine learning (ML) is widely used in classification tasks aimed at detecting various cognitive states or neurological diseases using noninvasive electroencephalogram (EEG) time series. However, successfully detecting specific cognitive skills in a healthy population, independent of subject, remains challenging. This study compared the subject-independent classification performance of three different pipelines: supervised and Riemann projections with logistic regression and handcrafted power spectral features with light gradient boosting machine (LightGBM). 128-channel EEGs were recorded from 26 healthy volunteers while they solved arithmetic, logical, and verbal tasks. The participants were divided into two groups based on their higher education and occupation: specialists in mathematics and humanities. The balanced accuracy of the education type was significantly above chance for all pipelines: 0.84-0.89, 0.85-0.88, and 0.86-0.88 for each type of task, respectively. All three pipelines allowed us to distinguish mathematical proficiency based on learning experience with different trade-offs between performance and explainability. Our results suggest that ML approaches could also be effective for recognizing individual cognitive traits using EEG.
机器学习（ML）广泛应用于分类任务，旨在使用非侵入性脑电图（EEG）时间序列检测各种认知状态或神经疾病。然而，成功地在健康人群中检测特定的认知技能，而不依赖于个体，仍然具有挑战性。本研究比较了三种不同管道的个体独立分类性能：监督和黎曼投影与逻辑回归以及手工制作的功率谱特征与轻梯度提升机（LightGBM）。在解决算术、逻辑和语言任务时，记录了 26 名健康志愿者的 128 通道 EEG。参与者根据其高等教育和职业分为两组：数学和人文专业人士。对于所有管道，教育类型的平衡准确性均明显高于机会水平：算术、逻辑和语言任务的准确性分别为 0.84-0.89、0.85-0.88 和 0.86-0.88。这三种管道都允许我们根据学习经验区分数学能力，同时在性能和可解释性之间进行权衡。我们的结果表明，ML 方法也可以使用 EEG 有效识别个体认知特征。
