Wuttikorn Ponwitayarat, Peerat Limkonchotiwat, Raymond Ng, Jann Railey Montalan, Thura Aung, Jian Gang Ngui, Yosephine Susanto, William Chandra Tjhi, Panuthep Tasawong, Erik Cambria, Ekapol Chuangsuwanich, Sarana Nutanong. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

High mental workload poses significant challenges across various domains, often impairing performance and decision-making. Recent advancements in Internet of Things (IoT) technologies have enabled continuous monitoring of brain activity via electroencephalography (EEG), facilitating real-time assessment of mental workload during task execution. Early detection of elevated workload levels is essential for mitigating potential adverse outcomes. This paper presents EEGMeNet, an end-to-end, multi-task neural network designed to classify mental workload levels from EEG signals. EEGMeNet consists of four key components: (1) a local feature learner that extracts spectral and spatial characteristics, (2) a global feature learner employing attention-based mechanisms to model temporal dependencies, (3) a feature preserver that enhances representation learning, and (4) a supervised classifier for mental workload classification. The proposed model is evaluated across five mental task EEG datasets—including one acquired in clinical settings—under both within-subject and cross-subject, as well as within-session and cross-session classification paradigms. EEGMeNet outperforms nine state-of-the-art baselines, achieving an accuracy of 87.49±2.66% and an F1 score of 85.26±2.63% in cross-subject evaluations, showing at least a 9% improvement in accuracy and a 12% gain in F1 score. Moreover, EEGMeNet demonstrates low prediction time complexity, making it well-suited for deployment on resource-constrained IoT devices. These results underscore EEGMeNet’s robustness to inter-subject and multi-session EEG variability, validating its effectiveness for real-world EEG-IoT applications. This work establishes EEGMeNet as a promising foundation for future mental workload classification research in EEG-IoT environments.

Abstract Self-supervised sentence representation learning is the task of constructing an embedding space for sentences without relying on human annotation efforts. One straightforward approach is to finetune a pretrained language model (PLM) with a representation learning method such as contrastive learning. While this approach achieves impressive performance on larger PLMs, the performance rapidly degrades as the number of parameters decreases. In this paper, we propose a framework called Self-supervised Cross-View Training (SCT) to narrow the performance gap between large and small PLMs. To evaluate the effectiveness of SCT, we compare it to 5 baseline and state-of-the-art competitors on seven Semantic Textual Similarity (STS) benchmarks using 5 PLMs with the number of parameters ranging from 4M to 340M. The experimental results show that STC outperforms the competitors for PLMs with less than 100M parameters in 18 of 21 cases.1

Can Udomcharoenchaikit, Wuttikorn Ponwitayarat, Patomporn Payoungkhamdee, Kanruethai Masuk, Weerayut Buaphet, Ekapol Chuangsuwanich, Sarana Nutanong. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.