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Related Concept Videos

Higher Mental Functions of the Brain: Language01:10

Higher Mental Functions of the Brain: Language

919
Language is a system of communication that allows the expression of thoughts, ideas, and feelings. The brain processes language in both hemispheres.
Language formation and comprehension take place in the dominant hemisphere. The dominant hemisphere is responsible for understanding the meaning of spoken, written, or sign language, as well as the ability to communicate. For most people, the left hemisphere is the dominant one. The right hemisphere, then, gives tone and emotional context to the...
919

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Related Experiment Video

Updated: Jul 22, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
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Artificial intelligence based multimodal language decoding from brain activity: A review.

Yuhao Zhao1, Yu Chen2, Kaiwen Cheng1

  • 1College of Language Intelligence, Sichuan International Studies University, Chongqing 400031, PR China.

Brain Research Bulletin
|July 24, 2023
PubMed
Summary
This summary is machine-generated.

This study reviews brain language decoding using artificial intelligence (AI) across multiple data types. AI advances brain-computer interface (BCI) technology, aiding communication for those with aphasia.

Keywords:
Artificial intelligenceBrain activityDecoderLanguage decodingMultimodality

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Brain-computer interface (BCI) technology relies on decoding brain activity.
  • Artificial intelligence (AI) significantly advances brain language decoding.
  • Existing research often overlooks AI methods and focuses on single modalities.

Purpose of the Study:

  • To provide an overview of brain language decoding research.
  • To examine decoding from the perspective of different modalities and AI methodologies.
  • To highlight the potential of AI-driven BCI for communication restoration.

Main Methods:

  • Reviewing studies on multimodal stimuli (text, speech, image, video).
  • Focusing on AI-built decoders translating brain signals into language.
  • Analyzing factors influencing decoding performance, including models and brain regions.

Main Results:

  • Brain signals can be decoded into language at various levels (words, sentences, discourses).
  • AI effectively translates brain activity from multimodal stimuli into text or vocal language.
  • Decoding effectiveness is influenced by specific AI models and neural correlates.

Conclusions:

  • AI-powered brain language decoding shows significant promise for BCI.
  • Advances can help patients with clinical aphasia regain communication abilities.
  • Further research into decoding models and brain regions is crucial for progress.