Transfer Learning based Efficient Schizophrenia Classification on Electroencephalogram (EEG) Signals: A Cross-Dataset Study

Although EEG classification for schizophrenia has shown promising results on individual datasets, the cross-dataset generalizability of such classification remains unknown. This study aimed to assess this generalizability through transfer learning at segment and individual levels, by employing the spectral convolutional neural network (CNN-S) on two distinct EEG datasets for schizophrenia classification. While direct cross-decoding only obtained baseline transfer accuracies of 54.72% ± 2.77% and 47.78% ± 3.62% at the segment and individual levels, the fine-tuned CNN-S achieved average cross-dataset transfer accuracies of 76.46% ± 1.17% and 56.71% ± 5.76, respectively. To improve the limited generalizability at the individual level, we applied transfer component analysis (TCA), a domain adaptation approach, to the two datasets, leading to an average cross-dataset transfer accuracy of 62.12% ± 4.86%. By combining fine-tuning and TCA, the study obtained an average cross-dataset transfer accuracy of 69.15% ± 4.09% at the individual level. Overall, transfer learning proves useful for cross-dataset EEG schizophrenia classification.