  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.
