In the application of brain-computer interface (BCI), being able to accurately decode brain signals is a critical task. For the multi-class classification task of brain signal ECoG, how to improve the classification accuracy is one of the current research hotspots. ECoG acquisition uses a high-density electrode array and a high sampling frequency, which makes ECoG data have a certain high similarity and data redundancy in the temporal domain, and also unique spatial pattern in spatial domain. How to effectively extract features is both exciting and challenging. Previous work found that visual-related ECoG can carry visual information via frequency and spatial domain. Based on this finding, we focused on using deep learning to design frequency and spatial feature extraction modules, and proposed a Bi-Band ECoGNet model based on deep learning. The main contributions of this paper are: 1) The Bi-BCWT (Bi-Band Channel-Wise Transform) neural network module is designed to replace the time-consume method MST, this module greatly improves the model calculation and data storage efficiency, and effectively increases the training speed; 2) The Bi-BCWT module can effectively take into account the information both in low-frequency and high-frequency domain, which is more conducive to ECoG multi-classification tasks; 3) ECoG is acquired using 2D electrode array, the newly designed 2D Spatial-Temporal feature encoder can extract the 2D spatial feature better. Experiments have shown that the unique 2D spatial data structure can effectively improve classification accuracy; 3) Compared with previous work, the Bi-Band ECoGNet model is smaller and has higher performance, with an accuracy increase of 1.24%, and the model training speed is increased by 6 times, which is more suitable for BCI applications.

翻译：在脑机接口（BCI）应用中，能够准确解码脑信号是一项关键任务。对于脑信号ECoG的多类别分类任务，如何提高分类精度是当前的研究热点之一。ECoG采集使用高密度电极阵列和高采样频率，这使得ECoG数据在时域上具有较高的相似性和数据冗余，同时在空域上具有独特的空间模式。如何有效地提取特征既令人兴奋又充满挑战。先前的研究发现，与视觉相关的ECoG可以通过频域和空域承载视觉信息。基于这一发现，我们专注于利用深度学习设计频域和空域特征提取模块，并提出了一种基于深度学习的双频带ECoGNet模型。本文的主要贡献包括：1) 设计了Bi-BCWT（双频带通道变换）神经网络模块以替代耗时的MST方法，该模块极大地提高了模型计算和数据存储效率，并有效提升了训练速度；2) Bi-BCWT模块能够有效兼顾低频和高频域的信息，更有利于ECoG多分类任务；3) ECoG使用二维电极阵列采集，新设计的二维时空特征编码器能够更好地提取二维空间特征。实验表明，独特的二维空间数据结构能有效提高分类精度；4) 与先前工作相比，双频带ECoGNet模型规模更小且性能更高，准确率提升了1.24%，模型训练速度提高了6倍，更适用于BCI应用。