The aim of the study is to investigate the complex mechanisms of speech
perception and ultimately decode the electrical changes in the brain accruing
while listening to speech. We attempt to decode heard speech from intracranial
electroencephalographic (iEEG) data using deep learning methods. The goal is to
aid the advancement of brain-computer interface (BCI) technology for speech
synthesis, and, hopefully, to provide an additional perspective on the
cognitive processes of speech perception. This approach diverges from the
conventional focus on speech production and instead chooses to investigate
neural representations of perceived speech. This angle opened up a complex
perspective, potentially allowing us to study more sophisticated neural
patterns. Leveraging the power of deep learning models, the research aimed to
establish a connection between these intricate neural activities and the
corresponding speech sounds. Despite the approach not having achieved a
breakthrough yet, the research sheds light on the potential of decoding neural
activity during speech perception. Our current efforts can serve as a
foundation, and we are optimistic about the potential of expanding and
improving upon this work to move closer towards more advanced BCIs, better
understanding of processes underlying perceived speech and its relation to
spoken speech.

研究目的是调查言语知觉的复杂机制，并最终解码在听取言语时发生的大脑电生理学变化。我们尝试使用深度学习方法从颅内脑电数据中解码听到的言语，目标是促进言语合成的脑 - 机接口技术的发展，并为言语知觉的认知过程提供额外的视角。这一方法不同于传统的言语产生焦点，而选择调查感知言语的神经表达。通过利用深度学习模型的能力，研究旨在建立这些复杂神经活动与相应言语声音之间的联系。尽管该方法尚未取得突破，但研究揭示了解码言语知觉期间神经活动的潜力。我们目前的努力可以作为基础，并且我们对拓展和改进这项工作以更接近先进的脑 - 机接口、更好地理解言语知觉过程及其与口语之间的关系的潜力持乐观态度。