June 1, 2023Frontiers in PsychologyOpen Access

Multi-Input CNN-LSTM deep learning model for fear level classification based on EEG and peripheral physiological signals

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Why the study?

Objective and accurate classification of fear levels is important for developing treatments for anxiety disorder, obsessive-compulsive disorder, PTSD, and phobia.

Population

Multichannel EEG signals and multimodal peripheral physiological signals in the DEAP dataset

Design

Deep learning model evaluation with 10-fold cross-validation

Authors

NMNagisa Masuda IYIkuko Eguchi YairiSophia University

Discussion

Member takes

Implication

May support EEG-based fear monitoring in anxiety care; leaves open clinical translation pending prospective validation.

Structured PICO

Population

Multichannel EEG signals and multimodal peripheral physiological signals from the DEAP dataset

Intervention

Multi-Input CNN-LSTM classification model

Outcome

Accuracy of fear level classification

A Multi-Input CNN-LSTM deep learning model can accurately classify fear levels using EEG and peripheral physiological signals, which may aid in developing treatments for anxiety and trauma-related disorders.

Cite This Study

Masuda et al. (2023) studied this question.

synapsesocial.com/papers/6a76980aa308cedcc6530cc4 https://doi.org/10.3389/fpsyg.2023.1141801

View Full Paper

Ask AI

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Multi-Input CNN-LSTM deep learning model for fear level classification based on EEG and peripheral physiological signals

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Multi-Input CNN-LSTM deep learning model for fear level classification based on EEG and peripheral physiological signals

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider