Bocquelet Florent, Hueber Thomas, Girin Laurent, Chabardès Stéphan, Yvert Blaise
INSERM, BrainTech Laboratory U1205, F-38000 Grenoble, France; Univ. Grenoble Alpes, BrainTech Laboratory U1205, F-38000 Grenoble, France.
Univ. Grenoble Alpes, CNRS, Grenoble INP, GIPSA-lab, 38000 Grenoble, France.
J Physiol Paris. 2016 Nov;110(4 Pt A):392-401. doi: 10.1016/j.jphysparis.2017.07.002. Epub 2017 Aug 7.
Restoring communication in case of aphasia is a key challenge for neurotechnologies. To this end, brain-computer strategies can be envisioned to allow artificial speech synthesis from the continuous decoding of neural signals underlying speech imagination. Such speech brain-computer interfaces do not exist yet and their design should consider three key choices that need to be made: the choice of appropriate brain regions to record neural activity from, the choice of an appropriate recording technique, and the choice of a neural decoding scheme in association with an appropriate speech synthesis method. These key considerations are discussed here in light of (1) the current understanding of the functional neuroanatomy of cortical areas underlying overt and covert speech production, (2) the available literature making use of a variety of brain recording techniques to better characterize and address the challenge of decoding cortical speech signals, and (3) the different speech synthesis approaches that can be considered depending on the level of speech representation (phonetic, acoustic or articulatory) envisioned to be decoded at the core of a speech BCI paradigm.
对于神经技术而言,恢复失语症患者的沟通能力是一项关键挑战。为此,可以设想采用脑机策略,通过对语音想象背后的神经信号进行连续解码来实现人工语音合成。此类语音脑机接口目前尚不存在,其设计应考虑三个关键选择:选择合适的脑区来记录神经活动、选择合适的记录技术,以及结合合适的语音合成方法选择神经解码方案。本文将根据以下几点讨论这些关键因素:(1)目前对公开和隐蔽言语产生所涉及的皮质区域功能神经解剖学的理解;(2)利用各种脑记录技术以更好地表征和应对解码皮质语音信号挑战的现有文献;(3)根据语音脑机接口范式核心处设想解码的语音表征水平(语音、声学或发音)可考虑的不同语音合成方法。