Comparison of Cognitive Workload Assessment Techniques in EMG-based Prosthetic Device Studies

Park, Junho; Zahabi, Maryam

doi:10.1109/smc42975.2020.9283229

Cited by 5 publications

(1 citation statement)

References 37 publications

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“…For real-time cognitive workload classification, various methods have been explored [10], including physiological measurements such as electroencephalography (EEG), which is a test that records the electrical activity of the brain using scalp electrodes [11]; electrocardiography (ECG), a medical test to measure the electrical activity of the heart [12]; electromyography (EMG), which is used to evaluate the electrical activity of muscles [13]; and eye tracking, which is technology that monitors and records the movement of the gaze of a person [14]. Task performance metrics [15], functional Near-Infrared Spectroscopy (fNIRS) [16], and multimodal integration contribute further.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Workload Classification in Industry 5.0 Applications: Electroencephalography-Based Bi-Directional Gated Network Approach

Afzal,

Gu,

Afzal

et al. 2023

Electronics

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In the era of Industry 5.0, effectively managing cognitive workload is crucial for optimizing human performance and ensuring operational efficiency. Using an EEG-based Bi-directional Gated Network (BDGN) approach, this study tries to figure out how to classify cognitive workload in Industry 5.0 applications. The proposed approach incorporates LSTM (Long Short-Term Memory) and GRU (Gated Recurrent Unit) models in a hybrid architecture to leverage their complementary strengths. This research highlights the utilization of the developed model alongside the MQTT (Message Queuing Telemetry Transport) protocol to facilitate real-time end-to-end data transmission. The deployed AI model performs the classification of cognitive workload based on the received data. The main findings of this research reveal an impressive accuracy of 98% in cognitive workload classification, validating the efficacy of the suggested BDGN approach. This study emphasizes the significance of leveraging EEG-based approaches in Industry 5.0 applications for cognitive workload management.

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