Enhancing Word-Level Imagined Speech BCI Through Heterogeneous Transfer Learning

In this study, we proposed a novel heterogeneous transfer learning approach named Focused Speech Feature Transfer Learning (FSFTL), aimed at enhancing the performance of electroencephalogram (EEG)-based word-level Imagined Speech (IS) Brain-Computer Interface (BCI). In IS BCI, the classification accuracy for imagining specific words is relatively low due to the inherent complexity in high-level feature variations. However, the binary classification accuracy for IS/rest is significantly higher. FSFTL leverages the refined feature focusing capability of the binary IS/Rest classification task to effectively locate relevant features for the word-level task. The feature extractor in the IS/Rest model demonstrates robust decoding ability for low-level IS features in EEG signals. We applied this high-performance yet low-resolution feature extractor to a public dataset for five-word IS task. The classifier was retrained to handle an increased number of classification categories, and the feature extractor was further fine-tuned to accommodate higher-level classification tasks. Before the experiment, we aligned the data from the two datasets to maintain the versatility of the feature extractor. Our proposed FSFTL approach was compared with existing EEG models, showing a significant improvement. The FSFTL approach outperformed the backbone strategy with a 6% increase in mean accuracy across all fifteen subjects. This study highlights the commonality of features in EEG data of IS and their transferability across various datasets and tasks, which is beneficial for improving the decoding ability of word-level IS BCI.