Table 2. Performances of the SVM classifier for the different data types used in this paper.

The best performance is highlighted in bold. The classification of connectivity matrices best captured the changes in the brain due to ayahuasca.

Type of data	Subset	AUC	Acc.	F1 score	Recall	Precision
EEG time series	Train	0.87	0.89	0.88	0.87	0.89
	Test	0.85	0.88	0.86	0.85	0.86
Connectivity matrix	Train	0.92	0.94	0.93	0.92	0.96
	Test	0.88	0.92	0.90	0.88	0.94
Complex measure	Train	0.79	0.81	0.79	0.79	0.78
	Test	0.75	0.83	0.78	0.75	0.90