This article focuses on the design of a non-invasive brain computer interface (BCI) system for handwritten text based on L2 regularization and attention supervision paradigm, and the optimization of EEG signal decoding. In response to the challenges of non-invasive BCI in terms of signal acquisition accuracy and classification accuracy, this paper innovatively proposes a segmentation recombination preprocessing method, and designs an MLSTM algorithm combining a mapping module and LSTM to achieve accurate output from EEG signals to text. In terms of system implementation, attention supervision paradigm was introduced, combined with L2 regularization technology to optimize the model, significantly improving the quality of signal acquisition and decoding accuracy. The research results not only promote the further development of BCI technology, but also provide a new self-control method for patients with physical disabilities. The research in this article has important theoretical and practical value, and is of great significance for the widespread application of BCI technology in the future.

Jun Ye. Design of a Non-Invasive Brain Computer Interface System for Handwritten Text Based on L2 Regularization and Attention Supervision Paradigm, and Optimization of EEG Signal Decoding. International Journal of Big Data Intelligent Technology (2025), Vol. 6, Issue 1: 126-134. https://doi.org/10.38007/IJBDIT.2025.060113.

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