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36 reviewsAbstract—Speech recognition can be widely applied to supportare deaf-mute [1]. In addition, nearly one-third of the world’speople with language disabilities by enabling them topopulation speaks tonal languages. Current languagecommunicate through brain-computer interfaces (BCIs), thusrecognition-oriented BCI systems ignore the recognition ofimproving their quality of life. Despite the essential role of tonal tones, despite the essential role of tones for the semanticvariations in conveying semantic meaning, there have beenexpression of languages. For those patients who cannotlimited studies focusing on the neural signatures of tones andpronounce normally, solely recognizing the words they saytheir decoding. This paper systematically investigates the neural without recognizing tones would affect their communicationsignatures of the four tones of Mandarin. It explores thewith others, especially when devices like an electronic larynxfeasibility of tone decoding in both silent and audible speechare used for synthesizing pronunciations. To solve this issue, itusing a multimodal BCI based on electroencephalography(EEG) and electromyography (EMG). The time-frequencywould be beneficial to understand from bothanalysis of EEG has revealed significant variations in neural electroencephalogram (EEG) and electromyogram (EMG)activation patterns across various tones and speech modes. Forsignals how they correlate with tonal speaking silently andexample, in the silent speech condition, temporal-domain analysisvocally, and how their combination would improve theshows significant tone-dependent activation in the frontal lobedecoding performance. (ANOVA p = 0.000, Tone1 vs Tone2: p = 0.000, Tone1 vs Tone4: From the perspective of EEG signals, few studies havep = 0.000, Tone2 vs Tone3: p = 0.000, Tone3 vs Tone4: p = 0.001) studied the signature of tones and tone decoding. Sereshkeh
