## 1 Introduction  
Cognitive workload is a global issue, alongside the ongoing advancements of the industrial revolution, where new technologies are continually emerging [1]. Industry 4.0 marked a transition from traditional manufacturing methods to digital manufacturing [2], driven by innovations like advanced robot arms [3], industrial Internet of Things (IIoT) [4], artificial intelligence (AI) [5], and cloud computing [6]. These innovations generate a significant amount of data [7] and contribute to a disruptive workplace process, which has become a new source of stress [8]. Industry 5.0, in contrast to its predecessor Industry 4.0, heralds a new era of smooth integration between cutting-edge technology and human-machine interactions [9]. This integration aims to enhance production and efficiency, emphasizing the significance of improving human-machine interactions to boost overall performance [10, 11]. In this context, implementing a new cognitive workload monitoring system (CWMS) incorporating deep gated neural network (DGNN) and industrial Internet of Things (IIoT) technologies is essential. This system tracks and classifies cognitive workloads in real time, thereby improving the performance and safety of human-machine interactions. The CWMS holds significance in multiple industrial contexts where human-robot collaboration is pervasive [12; 13; 14; 15]. Cognitive workload, encompassing mental exertion [16] across various functions such as memory [17], decision-making [18], attention [19], perception [20], problem-solving [21], and information processing [22], serves as a foundational element. Continuous monitoring and real-time analysis of workload levels are essential for improving efficiency [23], reducing errors [24], and addressing safety concerns [25], especially in critical industrial environments. A spectrum of cutting-edge technologies contributes to the assessment of cognitive workload. Electrocardiography (ECG) monitors the heart's electrical activity [26], providing valuable data on physiological stress levels. Eye-tracking captures gaze patterns, offering insights into visual attention and cognitive processing [27]. Facial expression analysis deciphers real-time emotional states [28], and task performance analysis evaluates metrics related to specific activities [29]. Functional near-infrared spectroscopy (fNIRS), measures changes in blood oxygen levels [30], revealing neural activity associated with cognitive workload. Among all these methods, electroencephalography (EEG) stands out for its effectiveness [31], providing a detailed understanding of cognitive processes with real-time monitoring, non-invasiveness, and portability. Also, these methods leverage machine learning [32], and deep learning [33] algorithms for autonomous identification and classification of cognitive states. A critical aspect that requires attention is the underexplored intersection of cognitive workload assessment, IIoT, and advanced neural network methodologies for real-time workload monitoring and analysis. There is a lack of comprehensive studies harnessing the combined potential of EEG, IIoT, and refined neural network methods. This creates a gap in developing efficient and accurate cognitive workload monitoring systems that are capable of handling the challenges posed by industrial interactions [1]. In our current study, we aim to bridge this identified gap by focusing on EEG signals for real-time cognitive workload classification and monitoring. Our approach introduces the DGNN model, comprising a dual-layer architecture that incorporates both Bi-LSTM [34] and GRU [35] networks, providing a state-of-the-art framework for cognitive workload classification. The DGNN methodology harnesses the STEW dataset [36], including data from both low and high cognitive workload modes for training and evaluation. The model achieves a remarkable accuracy of 99.45%.