A Review of Brain–Computer Interface-Based Language Decoding: From Signal Interpretation to Intelligent Communication
Brain–computer interface (BCI) technologies for language decoding have emerged as a transformative bridge between neuroscience and artificial intelligence (AI), enabling direct neural–computational communication. The current literature provides detailed insights into individual components of BCI sys...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-01-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/1/392 |
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| Summary: | Brain–computer interface (BCI) technologies for language decoding have emerged as a transformative bridge between neuroscience and artificial intelligence (AI), enabling direct neural–computational communication. The current literature provides detailed insights into individual components of BCI systems, from neural encoding mechanisms to language decoding paradigms and clinical applications. However, a comprehensive perspective that captures the parallel evolution of cognitive understanding and technological advancement in BCI-based language decoding remains notably absent. Here, we propose the Interpretation–Communication–Interaction (ICI) architecture, a novel three-stage perspective that provides an analytical lens for examining BCI-based language decoding development. Our analysis reveals the field’s evolution from basic signal interpretation through dynamic communication to intelligent interaction, marked by three key transitions: from single-channel to multimodal processing, from traditional pattern recognition to deep learning architectures, and from generic systems to personalized platforms. This review establishes that BCI-based language decoding has achieved substantial improvements in regard to system accuracy, latency reduction, stability, and user adaptability. The proposed ICI architecture bridges the gap between cognitive neuroscience and computational methodologies, providing a unified perspective for understanding BCI evolution. These insights offer valuable guidance for future innovations in regard to neural language decoding technologies and their practical application in clinical and assistive contexts. |
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| ISSN: | 2076-3417 |