Imaginary handwriting is an important research paradigm in the field of brain-controlled typing. Neural signals exhibit high complexity, low signal-to-noise ratio, and strong temporal and environmental variability, leading to significant inter-trial differences in the temporal dynamics of character-related signals. These factors pose significant challenges for segmenting character-related signals and accurately decoding imaginary handwriting. To address these issues, this study proposes a Dynamic Time Warping Independent Component Analysis (DTWICA) framework. This framework employs FastDTW to construct individualized warping functions for each trial, followed by FastICA-based decomposition to separate the signal into distinct temporal and neuronal factors. The decomposed temporal factors are then mapped and transformed using the warping function and subsequently merged with the neuronal factors to reconstruct the signal. A sliding time window is then applied for adaptive processing, yielding the transformed signal. Finally, the transformed signals from multiple trials are averaged to generate a template for each character. Results based on a publicly available neural signals dataset for imaginary handwriting indicate that, compared with mainstream time warping models such as Shift, Linear, Piecewise, and TWPCA, the proposed model improves the character decoding accuracy for 31 characters by 14%, 13%, 7%, and 2%, respectively. This study not only constructs effective character signal templates but also facilitates accurate character segmentation during unlabeled imagined typing in an offline setting, providing a promising methodological basis for future real-time imagined typing decoding systems.
Nan et al. (Fri,) studied this question.