To address the limitations in the accuracy of current cerebral cortex structure segmentation methods, this study proposes an automatic segmentation network based on dynamic recalibration and region awareness. The network is an improved version of the classic U-shaped architecture, incorporating a Dynamic Recalibration Block (DRB) and a Region-Aware Block (RAB). The DRB enhances important feature channels by extracting global feature information across channels, computing the significance weights via a two-layer fully connected network, and applying these weights to the original feature maps for dynamic feature reweighting. Meanwhile, the RAB integrates spatial positional information and captures both global and local context across multiple dimensions. It recalibrates features using dimension-specific weights, enabling region-aware feature association and complementing the DRB’s function. Together, these components enable efficient and accurate segmentation of brain structures. The proposed DRA-Net model effectively overcomes the accuracy–efficiency trade-off in cortical segmentation through multi-scale feature fusion, dual attention mechanisms, and deep feature extraction strategies. Experimental results demonstrate that DRA-Net achieves an average Dice score of 91.35% across multiple datasets, outperforming segmentation atlases based on methods such as U-Net, QuickNAT, and FastSurfer.

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.