A review of epileptic seizure detection using EEG signals analysis in the time and frequency domain

Guerrero, L. Fernando; Romero, Leandro Dorado; López, Manuel Gómez

doi:10.1109/icct52962.2021.9657835

Cited by 2 publications

(1 citation statement)

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“…Time domain analysis serves as a critical component of our study, involving an indepth examination of EEG data acquired during both low-mode and high-mode conditions [40]. This analytical approach delves into the temporal intricacies of the EEG data, facilitating an exploration of distinctive patterns and fluctuations over time.…”

Section: Time Domain Analysismentioning

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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Section: Time Domain Analysismentioning

confidence: 99%