What does this research mean for the field?

A multi-modal deep learning model combining EEG data and NEO-FFI personality traits significantly improves the accuracy of distinguishing between multiple specific mental disorders compared to traditional binary classification models. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

October 22, 2024Open Access

Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction

Structured PICO

Does the MENTAL model improve mental disorder prediction accuracy in healthy and psychiatric individuals?

Population

1274 healthy and psychiatric individuals including over 30 different diagnoses from the TDBRAIN dataset

Intervention

MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) model utilizing both EEG and NEO-FFI personality data

Comparator

Baseline models/methods (implied by accuracy improvements)

Outcome

Mental disorder prediction accuracy (e.g., ADHD, MDD)

The MENTAL multi-modal model, utilizing both EEG and NEO-FFI data, significantly improves the predictive accuracy for mental disorders such as ADHD and MDD.

Abstract

Early detection and accurate diagnosis of mental disorders is difficult due to the complexity of the diagnostic process, resulting in conditions being left undiagnosed or misdiagnosed. Previous studies have demonstrated that features of Electroencephalogram (EEG) data, such as Power Spectral Density (PSD), are useful biomarkers for indicating the onset of various mental disorders. Existing models using EEG data are typically trained to distinguish between healthy and afflicted individuals, and they are unable to distinguish between individuals with different disorders. We propose MENTAL (Multi-modal EEG NEO-FFI with Trained Attention Layer) to predict an individual's mental state using both EEG and Neo-Five Factor Inventory (NEO-FFI) personality data. We include an attention layer that captures the interactions between personality traits and PSD features, and emphasizes the important PSD features. MENTAL features a Recurrent Neural Network (RNN) to model the temporal nature of EEG data. We train our model with the Two Decades Brainclinics Research Archive for Insights in Neuroscience (TDBRAIN) dataset, which consists of 1274 healthy and psychiatric individuals including over 30 different diagnoses. MENTAL is able to achieve 93.3% accuracy when trained to classify between healthy individuals and those with ADHD. When trained to identify individuals with ADHD from among 33 disorder classes, MENTAL improves accuracy from 70.5 to 81.7%. MENTAL also demonstrates over 20% improvement in predictive accuracy when trained for MDD prediction. For the multi-class classification task of three classes, MENTAL improves accuracy by 4%, and for five classes, by nearly 8%. MENTAL is the first multi-modal model that utilizes both EEG and NEO-FFI data for the task of mental disorder prediction. We are one of the first groups to utilize TDBRAIN for automated disorder classification. MENTAL is accessible and cost-effective, as EEG is more affordable than other neuroimaging methods such as MRI, and the NEO-FFI is a self- reported survey. Our model can lead to acceptance and support for individuals living with mental health challenges and improve quality of life in our society.

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