DRDNet: A dual-view representation decoupling network for handwriting imagery EEG classification

Handwriting imagery (HI) based on electroencephalography (EEG) offers a non-invasive route to text input and complex intention expression for brain-computer interfaces (BCIs). However, HI EEG decoding is challenged by low signal-to-noise ratios, non-stationarity, cross-session distribution shifts, and the coexistence of continuous temporal trends and local high-response patterns.

We propose a Dual-View Representation Decoupling Network (DRDNet) for within-subject crosssession HI EEG classification. DRDNet first constructs two complementary temporal views from spatial EEG features using average and max pooling, corresponding to smooth trend-oriented and salient response-oriented representations. These views are then modeled by a bidirectional Mamba encoder and a Transformer encoder, respectively, and adaptively integrated through a time-step-level dynamic fusion mechanism followed by long short-term memory based temporal aggregation. The method is evaluated on two tasks from a public HI EEG dataset: Chinese character stroke handwriting imagery (CCSHI) and pinyin single-vowel handwriting imagery (SVHI).

DRDNet achieved average accuracies of 67.74% and 62.51%, with Cohen's kappa scores of 0.5968 and 0.5502, on CCSHI and SVHI, respectively. It outperformed seven representative EEG decoding baselines under the same crosssession protocol. Confusion matrices, feature visualization, ablation studies, structural variant analysis, and complexity evaluation further showed improved feature separability and a favorable balance between decoding performance and computational efficiency.

The results indicate that decoupling HI EEG into complementary temporal views and matching them with heterogeneous temporal encoders provides an effective representation learning strategy for robust non-invasive handwriting BCI decoding.