Analyzing Mental Workload among Deep-sea Oceanauts during Driving Operation Tasks Using EEG Data

Guang Cong^*
*Correspondence: Guang Cong, Department of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, China, Email:

Keywords

Mental workload • Deep-sea oceanauts • Driving operation tasks

Introduction

The exploration of the deep-sea environment is an endeavor that demands the highest levels of human expertise and operational precision. Deep-sea oceanauts, tasked with navigating submersible vehicles through the uncharted depths of the ocean, face an array of challenges that extend far beyond those encountered by their terrestrial counterparts. Operating in extreme pressure, darkness, and isolation, these professionals are not only responsible for their own safety but also for conducting critical scientific research, engineering tasks, and environmental monitoring in one of Earth's least understood ecosystems [1].

Central to the effectiveness and safety of deep-sea operations is the cognitive workload borne by oceanauts. Mental workload, defined as the cognitive resources required to perform a specific task, plays a pivotal role in determining the success of deep-sea missions. High mental workload can lead to fatigue, reduced decision-making capabilities, and compromised safety, all of which are particularly concerning in the unforgiving environment of the deep ocean. Thus, understanding and managing mental workload among deepsea oceanauts is of paramount importance for the success and safety of their missions [2].

Traditionally, mental workload assessments have relied on subjective selfreports or external observations, both of which have limitations in capturing the nuanced cognitive demands experienced by oceanauts during complex tasks. In recent years, advancements in neuroimaging techniques, specifically Electroencephalography (EEG), have provided a means to directly measure cognitive workload through neural markers. EEG allows for the non-invasive recording of electrical brain activity, providing insights into the mental states and cognitive processes of individuals.

This study aims to leverage EEG data to analyze mental workload among deep-sea oceanauts during driving operation tasks in submersible vehicles. These tasks are characterized by a high level of complexity and real-time decision-making, making them ideal for assessing mental workload. By monitoring neural markers, we seek to uncover distinct patterns of mental workload during different phases of the driving operation. Additionally, we aim to identify potential stressors and cognitive challenges faced by oceanauts during these tasks [3]. The insights gained from this research hold significant implications for deep-sea exploration and safety. Understanding the cognitive demands placed on oceanauts during driving operations can inform the design of training programs, task allocation strategies, and the development of human-machine interfaces tailored to the unique demands of deep-sea missions. Ultimately, this research contributes to the enhancement of safety and performance optimization for oceanauts operating in the challenging and high-stress deep-sea environment.

Description

The analysis of mental workload among deep-sea oceanauts during driving operation tasks using EEG data has yielded valuable insights into the cognitive demands and stressors associated with their unique work environment. In this discussion, we will examine the key findings of the study, their implications, and potential avenues for future research. Our study revealed distinct patterns of mental workload among deep-sea oceanauts during different phases of driving operations. These patterns offer crucial insights into how cognitive demands fluctuate during various stages of their tasks. For example, we observed higher mental workload during navigation and maneuvering phases compared to routine cruising. This finding suggests that oceanauts experience heightened cognitive demands when making critical decisions, adjusting to environmental factors, or responding to unexpected challenges. Understanding these variations in mental workload can inform task planning and allocation strategies. For instance, allocating additional resources or support systems during high-load phases could enhance performance and safety [4].

EEG data provided neural markers of cognitive load, allowing us to objectively assess the mental state of oceanauts. Changes in brainwave patterns, such as increased theta activity, were associated with higher cognitive workload. These markers can serve as real-time indicators of mental strain and fatigue. Implication: Integrating real-time EEG monitoring into deepsea missions could enable timely interventions to mitigate cognitive overload and prevent safety hazards.

By analyzing EEG data, we identified specific stressors that contribute to increased mental workload. Factors such as unexpected environmental conditions, equipment malfunctions, and time pressure were linked to elevated cognitive demands. Recognizing these stressors allows for targeted interventions, including improved training protocols, stress management techniques, and the development of resilient task execution strategies [5].

Optimizing the performance of deep-sea oceanauts is paramount to mission success. This study's insights into mental workload can inform the design of training programs tailored to the cognitive demands of driving operations. Additionally, the development of adaptive human-machine interfaces that respond to real-time EEG data could enhance task performance.

Implication: Enhancing performance not only ensures mission success but also contributes to the safety and well-being of oceanauts working in extreme conditions. Conducting longitudinal studies to track changes in mental workload and cognitive adaptation over extended missions would provide a deeper understanding of how oceanauts acclimate to their environment. Leveraging machine learning algorithms to predict cognitive load based on EEG data could lead to more advanced and proactive support systems for oceanauts. Collaboration between neuroscientists, engineers, and oceanographers can further refine the integration of EEG technology into deep-sea missions, creating a multidisciplinary approach to addressing cognitive workload challenges [6].

Conclusion

This study offers valuable insights into the mental workload experienced by deep-sea oceanauts during driving operation tasks. The findings have implications for safety, training, and the development of advanced humanmachine interfaces in deep-sea exploration. By addressing the cognitive demands of this unique work environment, we can contribute to the success and well-being of those who venture into the depths of our planet's oceans.

Acknowledgment

None.

Conflict of Interest

None.

References

1. Recarte, Miguel A and Luis M Nunes. "Mental workload while driving: Effects on visual search, discrimination, and decision making." J Experim Psychol: Appl 9 (2003): 119.

Google Scholar Cross Ref Indexed at

2. Mouzé-Amady, Marc, Eric Raufaste, Henri Prade and Jean-Pierre Meyer. "Fuzzy-TLX: using fuzzy integrals for evaluating human mental workload with NASA-Task Load indeX in laboratory and field studies." Ergonom 56 (2013): 752-763.

Google Scholar Cross Ref Indexed at

3. Klein, Martina I., Joel S Warm, Michael A Riley and Gerald Matthews, et al. "Mental workload and stress perceived by novice operators in the laparoscopic and robotic minimally invasive surgical interfaces." J Endourol 26 (2012): 1089-1094.

Google Scholar Cross Ref Indexed at

4. Fallahi, Majid, Majid Motamedzade, Rashid Heidarimoghadam and Ali Reza Soltanian, et al. "Effects of mental workload on physiological and subjective responses during traffic density monitoring: A field study." Appl Ergonom 52 (2016): 95-103.

Google Scholar Cross Ref Indexed at

5. Kramer, Arthur F., Leonard J Trejo and Darryl Humphrey. "Assessment of mental workload with task-irrelevant auditory probes." Biolog Psychol 40 (1995): 83-100.

Google Scholar Cross Ref Indexed at

6. Ke, Yufeng, Hongzhi Qi, Lixin Zhang and Shanguang Chen, et al. "Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression." Internat J Psychophys 98 (2015): 157-166.

Google Scholar Cross Ref Indexed at