Every people has their own voice, likewise, brain signals dis-play distinct neural representations for each individual. Al-though recent studies have revealed the robustness of speech-related paradigms for efficient brain-computer interface, the dis-tinction on their cognitive representations with practical usabil-ity still remains to be discovered. Herein, we investigate the dis-tinct brain patterns from electroencephalography (EEG) duringimagined speech, overt speech, and speech perception in termsof subject variations with its practical use of speaker identifica-tion from single channel EEG. We performed classification ofnine subjects using deep neural network that captures temporal-spectral-spatial features from EEG of imagined speech, overtspeech, and speech perception. Furthermore, we demonstratedthe underlying neural features of individual subjects while per-forming imagined speech by comparing the functional connec-tivity and the EEG envelope features. Our results demonstratethe possibility of subject identification from single channel EEGof imagined speech and overt speech. Also, the comparison ofthe three speech-related paradigms will provide valuable infor-mation for the practical use of speech-related brain signals inthe further studies.
翻译:同样,每个人的大脑都有自己的声音,同样,大脑的信号都显示,每个人的神经表现都不同。虽然最近的研究揭示了语言相关范例对于高效的脑计算机界面的强健性,他们的认知表现与实际的易用性之间的脱节性仍有待发现。在这里,我们通过比较功能共性与EEEG信封的特征,对脑中来自脑电脑造影(EEEG)的脱节性脑模式进行了调查,并用其实际使用单一频道EEEEG的语音辨识和公开演讲对主题的变异性进行了语音认知。此外,我们利用深神经网络对九个主题进行了分类,从EEEG中捕捉到想象的语音、公开的语音和语音感知觉的时谱空间特征。此外,我们展示了个别主题的基本神经特征,同时通过比较功能共性与EEG的表达式和公开演讲的特征来对想象性特征进行校验。我们通过单一频道EGEG的想象式语音和公开演讲进行主题识别的可能性。此外,对三种与语音有关的模式的比较将为进一步使用与大脑有关的语言信号的实际应用提供宝贵的内置。