Cognitive load classification is the task of automatically determining an
individual's utilization of working memory resources during performance of a
task based on physiologic measures such as electroencephalography (EEG). In
this paper, we follow a cross-disciplinary approach, where tools and
methodologies from speech processing are used to tackle this problem. The
corpus we use was released publicly in 2021 as part of the first passive
brain-computer interface competition on cross-session workload estimation. We
present our approach which used i-vector-based neural network classifiers to
accomplish inter-subject cross-session EEG transfer learning, achieving 18%
relative improvement over equivalent subject-dependent models. We also report
experiments showing how our subject-independent models perform competitively on
held-out subjects and improve with additional subject data, suggesting that
subject-dependent training is not required for effective cognitive load
determination.

认知负荷分类是根据生理学测量（如脑电图）自动确定个体在执行任务过程中使用工作记忆资源的任务，本文采用跨学科方法，利用语音处理工具和方法来解决这个问题，并提出了基于 i - 向量的神经网络分类器的方法，在跨个体、跨会话的脑电图转移学习中取得了 18% 的相对改进，并通过实验证明了我们的个体无关模型在保留的受试者上表现出竞争力，并且在额外受试者数据上获得了改进，这表明对于有效的认知负荷确定并不需要个体相关的训练。