  Brain-computer interface uses brain signals to control external devices
without actual control behavior. Recently, speech imagery has been studied for
direct communication using language. Speech imagery uses brain signals
generated when the user imagines speech. Unlike motor imagery, speech imagery
still has unknown characteristics. Additionally, electroencephalography has
intricate and non-stationary properties resulting in insufficient decoding
performance. In addition, speech imagery is difficult to utilize spatial
features. In this study, we designed length-wise training that allows the model
to classify a series of a small number of words. In addition, we proposed
hierarchical convolutional neural network structure and loss function to
maximize the training strategy. The proposed method showed competitive
performance in speech imagery classification. Hence, we demonstrated that the
length of the word is a clue at improving classification performance.
