  Brain-computer interface (BCI) facilitates direct communication between the
human brain and external systems by utilizing brain signals, eliminating the
need for conventional communication methods such as speaking, writing, or
typing. Nevertheless, the continuous generation of brain signals in BCI
frameworks poses challenges for efficient storage and real-time transmission.
While considering the human brain as a semantic source, the meaningful
information associated with cognitive activities often gets obscured by
substantial noise present in acquired brain signals, resulting in abundant
redundancy. In this paper, we propose a cross-modal brain-computer semantic
communication paradigm, named EidetiCom, for decoding visual perception under
limited-bandwidth constraint. The framework consists of three hierarchical
layers, each responsible for compressing the semantic information of brain
signals into representative features. These low-dimensional compact features
are transmitted and converted into semantically meaningful representations at
the receiver side, serving three distinct tasks for decoding visual perception:
brain signal-based visual classification, brain-to-caption translation, and
brain-to-image generation, in a scalable manner. Through extensive qualitative
and quantitative experiments, we demonstrate that the proposed paradigm
facilitates the semantic communication under low bit rate conditions ranging
from 0.017 to 0.192 bits-per-sample, achieving high-quality semantic
reconstruction and highlighting its potential for efficient storage and
real-time communication of brain recordings in BCI applications, such as
eidetic memory storage and assistive communication for patients.
