Abstract
Neural decoding, a critical component of Brain-Computer Interface (BCI), has recently attracted increasing research interest. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, the in-depth exploration of model architectures remains underexplored, despite its proven effectiveness in other tasks such as energy forecasting and image classification. In this study, we propose NeuroSketch, an effective framework for neural decoding via systematic architecture optimization. Starting with the basic architecture study, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this, we optimize the architecture from macro- to micro-level, achieving improvements in performance at each step. The exploration process and model validations take over 5,000 experiments spanning three distinct modalities (visual, auditory, and speech), three types of brain signals (EEG, SEEG, and ECoG), and eight diverse decoding tasks. Experimental results indicate that NeuroSketch achieves state-of-the-art (SOTA) performance across all evaluated datasets, positioning it as a powerful tool for neural decoding. Our code and scripts are available at this https URL.
Abstract (translated)
神经解码是脑机接口（BCI）的关键组成部分，近年来吸引了越来越多的研究兴趣。以往的研究主要集中在利用信号处理和深度学习方法来提升神经解码性能上。然而，尽管在诸如能源预测和图像分类等其他任务中已经证明了模型架构的重要性及其有效性，对模型架构的深入探索仍然相对较少。在这项研究中，我们提出了NeuroSketch框架，这是一个通过系统化架构优化实现有效神经解码的有效框架。 从基本架构的研究开始，我们发现CNN-2D在神经解码任务中的表现优于其他架构，并从时间和空间的角度探讨其有效性。在此基础上，我们将架构优化从宏观层面细化到微观层面，在每个步骤中都实现了性能的提升。探索过程和模型验证跨越了超过5,000次实验，涵盖了三种不同的模态（视觉、听觉和语音）、三种脑信号类型（EEG、SEEG 和 ECoG），以及八个多样化的解码任务。 实验结果表明，NeuroSketch在所有评估的数据集上都达到了最先进的性能水平，这使其成为神经解码的强大工具。我们的代码和脚本可以在提供的网址获取。
URL
https://arxiv.org/abs/2512.09524
