arXiv:2407.14936v1 Announce Type: new
Abstract: 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.
Decoding Visual Perception through Brain-Computer Semantic Communication
The field of Brain-Computer Interfaces (BCIs) has made significant strides in facilitating direct communication between the human brain and external systems. This article introduces a novel approach called EidetiCom, which leverages cross-modal brain-computer semantic communication to decode visual perception under limited-bandwidth constraint.
BCIs typically involve the acquisition and analysis of brain signals to interpret the user’s intentions or cognitive activities. However, the continuous generation of brain signals poses challenges in terms of efficient storage and real-time transmission. The authors of this paper recognize that the meaningful information associated with cognitive activities often gets obscured by noise, resulting in redundancy.
EidetiCom addresses this challenge by proposing a three-layer hierarchical framework. Each layer is responsible for compressing the semantic information of brain signals into representative features. These low-dimensional compact features are then transmitted and converted into semantically meaningful representations at the receiving end. This approach enables three distinct tasks for decoding visual perception: brain signal-based visual classification, brain-to-caption translation, and brain-to-image generation.
The multi-disciplinary nature of this concept is evident in its integration of brain signals, visual perception, and semantic communication. By combining knowledge from fields such as neuroscience, computer vision, and data compression, EidetiCom presents a holistic solution for efficient storage and real-time communication of brain recordings.
From a multimedia information systems perspective, EidetiCom bridges the gap between brain signals and visual perception. By decoding and reconstructing visual information from brain signals, it enables the creation of virtual realities and augmented realities that can be experienced by users. This has significant implications for fields such as gaming, virtual reality simulations, and assistive communication for patients.
The utilization of EidetiCom in BCI applications, such as eidetic memory storage, holds promise for personalized memory augmentation and retrieval. Additionally, its potential for assistive communication can empower individuals with speech or motor disabilities to communicate effectively.
In conclusion, the proposed cross-modal brain-computer semantic communication paradigm, EidetiCom, demonstrates its ability to facilitate semantic communication under low bit rate conditions. With its focus on efficient storage and real-time transmission of brain recordings, EidetiCom paves the way for advancements in multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.