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Brain-computer interface uses brain signals to control external deviceswithout actual control behavior. Recently, speech imagery has been studied fordirect communication using language. Speech imagery uses brain signalsgenerated when the user imagines speech. Unlike motor imagery, speech imagerystill has unknown characteristics. Additionally, electroencephalography hasintricate and non-stationary properties resulting in insufficient decodingperformance. In addition, speech imagery is difficult to utilize spatialfeatures. In this study, we designed length-wise training that allows the modelto classify a series of a small number of words. In addition, we proposedhierarchical convolutional neural network structure and loss function tomaximize the training strategy. The proposed method showed competitiveperformance in speech imagery classification. Hence, we demonstrated that thelength of the word is a clue at improving classification performance.

Speech Imagery Classification using Length-Wise Training based on Deep Learning