In this paper we propose a framework using multi-channel convolutional neural network (MC–CNN) for recognizing the grammatical class (verb or noun) of covertly-spoken words from electroencephalogram (EEG) signals. Our proposed network extracts features by taking into account spatial, temporal, and spectral properties of the EEG signal. Further, sets of signals acquired from different regions of the brain are processed separately within the proposed framework and are subsequently combined at the classification stage. This approach enables the network to effectively learn discriminative features from the locations of the brain where imagined speech is processed. Our network was tested using challenging experiments, including cases where the test subject did not take part in system training. In our main application scenario, where no instance of a specific noun or verb was used during training, our method achieved 85.7% recognition. Further, our proposed method was evaluated on a publicly available EEG dataset and achieved recognition rate of 93.8% in binary classification. These results demonstrate the potential of our method.

在本文中，我们提出了一个使用多通道卷积神经网络 (MC-CNN) 的框架，用于从脑电图 (EEG) 信号中识别隐蔽说出的词的语法类别（动词或名词）。我们提出的网络通过考虑 EEG 信号的空间、时间和频谱特性来提取特征。此外，从大脑不同区域获取的信号集在所提出的框架内分别进行处理，然后在分类阶段进行组合。这种方法使网络能够从处理想象语音的大脑位置有效地学习判别特征。我们的网络使用具有挑战性的实验进行了测试，包括测试对象没有参加系统训练的情况。在我们的主要应用场景中，在训练期间没有使用特定名词或动词的实例的情况下，我们的方法实现了 85.7% 的识别。此外，我们提出的方法在公开可用的 EEG 数据集上进行了评估，并在二元分类中实现了 93.8% 的识别率。这些结果证明了我们方法的潜力。