Innovations and approaches in depression detection via functional near-infrared spectroscopy

Table 1 Summary of machine learning-based detection methods

Ref.	Method	Task	Sample size	Number of channels	Evaluation criterion
Enneking et al[27], 2022	SVR	Resting	64 (34 P + 30 HC)	53	-
Yu et al[50], 2022	GNN	VFT	96	53	ACC (0.8775)
Shao et al[51], 2024	2D-CNN	Vision	96 (17 HC + 79 P)	53	ACC (0.905)
Huang et al[52], 2024	SVM	VFT	140	52	ACC (0.928)
Kim et al[53], 2023	SVM	Stroop	34	15	ACC (0.703)
Wang et al[54], 2021	AlexNet	Vison	96 (17 HC + 79 P)	53	ACC (0.90)
Lin et al[55], 2025	RF	VFT	143 (73 HC + 70 P)	44	ACC (0.77)
Mou et al[56], 2025	1D-CNN	VFT	172 (132 P + 40 HC)	22	ACC (0.7957)

P: Patients; HC: Healthy control; ACC: Accuracy; SVR: Support vector regression; GNN: Graph neural network; 2D-CNN: 2-dimensional convolution neural network; SVM: Support vector machine; RF: Random forest; 1D-CNN: 1-dimensional convolutional neural network; VFT: Verbal fluency tasks.