Transfer Learning for Covert Speech Classification Using EEG Hilbert Envelope and Temporal Fine Structure
This addresses the issue of mental fatigue and training burden for users of brain-computer interfaces, though it is incremental as it builds on existing transfer learning and feature extraction methods.
The paper tackled the problem of extensive training and difficulty in identifying word onset in brain-computer interfaces for covert speech by transferring a classifier trained on overt speech data, achieving state-of-the-art classification accuracies of 86.44% for overt speech and 79.82% for covert speech.
Brain-Computer Interfaces (BCIs) can decode imagined speech from neural activity. However, these systems typically require extensive training sessions where participants imaginedly repeat words, leading to mental fatigue and difficulties identifying the onset of words, especially when imagining sequences of words. This paper addresses these challenges by transferring a classifier trained in overt speech data to covert speech classification. We used electroencephalogram (EEG) features derived from the Hilbert envelope and temporal fine structure, and used them to train a bidirectional long-short-term memory (BiLSTM) model for classification. Our method reduces the burden of extensive training and achieves state-of-the-art classification accuracy: 86.44% for overt speech and 79.82% for covert speech using the overt speech classifier.