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Multimodal Fusion of EEG and Audio Spectrogram for Major Depressive Disorder Recognition Using Modified DenseNet121
Musyyab Yousufi1, Robertas Damaševičius1, Rytis Maskeliūnas1
1Centre of Real Time Computer Systems, Kaunas University of Technology, 51368 Kaunas, Lithuania.
Brain Sciences
|October 25, 2024
Summary
This study developed a multimodal model using electroencephalography (EEG) and audio data to classify Major Depressive Disorder (MDD). The model achieved high accuracy, showing potential for clinical depression assessment.
Area of Science:
- Neuroscience
- Machine Learning
- Signal Processing
Background:
- Major Depressive Disorder (MDD) diagnosis can be challenging.
- Electroencephalography (EEG) and audio signals offer potential biomarkers for mental health assessment.
Purpose of the Study:
- To develop a multimodal classification model integrating EEG and audio data for MDD identification.
- To evaluate the model's performance in detecting depressive tendencies.
Main Methods:
- Utilized the Multimodal open dataset for Mental Disorder Analysis (MODMA).
- Employed a pre-trained Densenet121 model with transfer learning.
- Extracted and concatenated features from EEG (Short-Time Fourier-Transform spectrograms) and audio (Mel-spectrograms) modalities.
Main Results:
- Achieved high classification performance: 97.53% Accuracy, 98.20% Precision, 97.76% F1 Score, and 97.32% Recall.
- The multimodal approach outperformed existing single-modality methods.
- A confusion matrix analysis confirmed the model's effectiveness.
Conclusions:
- The proposed multimodal classification approach is robust and effective for MDD assessment.
- This method shows significant potential for application in clinical diagnostics.
- Integration of EEG and audio data enhances depression classification accuracy.
