Recognition of grammatical class of imagined words from EEG signals using convolutional neural network

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. (c) 2021 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).

Automated summary

This paper proposes a framework using multi-channel convolutional neural network for recognizing the grammatical class of covertly-spoken words from EEG signals. The method achieved good results in challenging experiments, demonstrating potential applications.