Fairness in EEG-Based Depression Detection
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@courseproject{nurfirdausi2425-ethicsofai20242025,
abstract = {As artificial intelligence becomes increasingly integrated into mental health diagnostics, ensuring fairness and transparency is critical—particularly in sensitive applications such as depression detection. This project explores fairness-aware deep learning using raw EEG signals from the MODMA (Multi- modal Open Dataset for Mental-disorder Analysis) dataset [1]. Three deep neural architectures are developed and evaluated: a Convolutional Neural Network (CNN) as a baseline model, a CNN combined with Long Short-Term Memory (CNN-LSTM), and a CNN combined with Gated Recurrent Units and Attention mechanism (CNN-GRU-Attention). To address potential biases, five mitigation techniques are applied across three stages: preprocessing, in-processing, and post-processing. The models are evaluated using both performance metrics (accuracy, precision, recall, F1-score) and fairness metrics (disparate impact, statistical parity, equal opportunity, average odds, and equalized accuracy).},
apice = {Nurfirdausi2425},
author = {Annisaa Fitri Nurfirdausi},
howpublished = {Ethics of AI, A.Y. 2024/2025},
sort = {classic},
supervisor = {Calegari, Roberta},
title = {Fairness in EEG-Based Depression Detection},
type = {Standard Project},
url = {https://dvcs.apice.unibo.it/pika-lab/courses/ai-ethics/projects/nurfirdausi2425}
}
abstract = {As artificial intelligence becomes increasingly integrated into mental health diagnostics, ensuring fairness and transparency is critical—particularly in sensitive applications such as depression detection. This project explores fairness-aware deep learning using raw EEG signals from the MODMA (Multi- modal Open Dataset for Mental-disorder Analysis) dataset [1]. Three deep neural architectures are developed and evaluated: a Convolutional Neural Network (CNN) as a baseline model, a CNN combined with Long Short-Term Memory (CNN-LSTM), and a CNN combined with Gated Recurrent Units and Attention mechanism (CNN-GRU-Attention). To address potential biases, five mitigation techniques are applied across three stages: preprocessing, in-processing, and post-processing. The models are evaluated using both performance metrics (accuracy, precision, recall, F1-score) and fairness metrics (disparate impact, statistical parity, equal opportunity, average odds, and equalized accuracy).},
apice = {Nurfirdausi2425},
author = {Annisaa Fitri Nurfirdausi},
howpublished = {Ethics of AI, A.Y. 2024/2025},
sort = {classic},
supervisor = {Calegari, Roberta},
title = {Fairness in EEG-Based Depression Detection},
type = {Standard Project},
url = {https://dvcs.apice.unibo.it/pika-lab/courses/ai-ethics/projects/nurfirdausi2425}
}