Decoding Depression with EEG: The Promise and Pitfalls of AI Insight

Recent strides in deep learning have pushed the boundaries of what's possible in diagnosing Major Depressive Disorder (MDD) using electroencephalography (EEG) data. But while the models are more accurate than ever, their decision-making processes remain as opaque as a thick fog. Can we trust these high-capacity models without understanding their inner workings?

Unpacking the Black Box

This study put multiple post-hoc explainability methods under the microscope, all aimed at deciphering the enigmatic InceptionTime architecture trained for MDD detection. The analysis roped in a variety of attribution approaches: DeepSHAP, Integrated Gradients, GradCAM, Occlusion, and Permutation Feature Importance, each bringing its own flavor of insight.

Running this through a subject-level stratified 5-fold cross-validation framework revealed some interesting patterns. The methods converged on certain EEG regions, frontal, temporal, and posterior, particularly in the right hemisphere. Yet, they diverged enough to suggest that the choice of method can drastically shape the picture of what's happening inside these AI models.

Are We Seeing the Full Picture?

Quantitative comparisons showed that gradient- and perturbation-based approaches seemed to nod in agreement more often than not, while DeepSHAP often stood apart with unique attribution distributions. But let's not get too excited. The variability between these methods hints at how much their underlying assumptions impact the results. It's a sharp reminder that explainability can be as much an art as a science.

Should we put our faith in these partially overlapping relevance structures? While these patterns resonate with prior EEG studies of MDD, they remain exploratory. Calling them definitive neurophysiological biomarkers would be premature. It shows the potential of AI in psychiatry, but it also underscores where it falls short. Slapping a model on a GPU rental isn't a convergence thesis.

The Road Ahead

This research underscores the need for caution and further exploration. If we're going to let AI help diagnose something as critical as depression, we need to understand it. Fully. And here's the kicker: If the AI can hold a wallet, who writes the risk model? Until we can answer that, these systems should be seen as an aid, not a replacement for human expertise.

The intersection is real. Ninety percent of the projects aren't. But those that are could redefine how we approach mental health diagnosis. Show me the inference costs. Then we'll talk.