Silent speech BCI

提出了一种新的无声言语脑机接口，使用脑电图头皮记录和在公开和隐蔽地说日语“janken”和“season”时测量的语音信号。该框架由两个阶段组成。第一阶段使用语音信号的频谱图和实际语音任务中记录的脑电图的独立分量指定卡尔曼滤波器（KF）。在“季节”任务中，语音信号被转换成元音和/或辅音序列，这些关系由HMM学习。第二个模型预测了KF在无声语音任务中使用脑电图预测的无声“janken”和“season”的频谱图。对于静止的“季节”，将预测的谱图输入HMM。目前的结果表明，当将卡尔曼滤波与偶极子解主要定位于布洛卡区的集成电路相结合时，对10个健康受试者的沉默“jankens”都能正确预测，并且“春季”和“夏季”hmm都能很好地预测。

A new silent speech BCI was proposed using both EEGs scalp-recorded and speech signals measured during overtly and covertly speaking "janken" and "season" in Japanese. The framework consists of two phases. The first phase specifies a Kalman filter (KF) using both spectrograms of the speech signals and independent components of the EEGs recorded during the actual speech task. In case of the "season" task, speech signals were transformed into vowel and/or consonant sequences, and these relations were learned by HMM. The second one predicts spectrograms for the silent "janken" and "season" by the KF with the EEGs during the silent speech tasks. For the silent "season", the predicted spectrograms were inputted to the HMM. The present results demonstrate that when using the Kalman filter with the ICs whose dipole solutions are localized to mainly the Broca's area, the silent "jankens" are all correctly predicted for ten healthy subjects, and the "spring"- and "summer"-HMMs worked well.