კოგნიტური მთარგმნელის შესახებ

კოგნიტური მთარგმნელის შესახებ

ავტორები

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DOI:

https://doi.org/10.52340/lac.2026.11.25

საკვანძო სიტყვები:

cognitive translator

ანოტაცია

This is a research for creating a cognitive translator prototype based on brain electromagnetic activity, aiming to develop a cognitive translator prototype that uses brain activity to translate thoughts into different languages. The idea is to analyze linguistic activity data and develop algorithms to recognize and translate brain signals associated with specific concepts.

The main hypothesis is that the concept (or thought /whatever is to say/write) remains the same across languages haveing the different linguistic material for different langugaes. To test this hypothesis, we plan to record and analyze the neural activity of 90 participants speaking three different languages using EmoTV epoc - EEG (electroencephalography) technology.

The research involves several steps, including:

  • Data collection: Recording EEG signals from participants as they think or speak in different languages.
  • Data analysis: Analyzing the EEG signals using techniques such as FFT, PSD, ERP, and machine learning algorithms to identify patterns associated with specific concepts.
  • Algorithm development: Developing algorithms to recognize and translate brain signals into different languages.
  • Prototype development: Creating a software prototype of the cognitive translator.

The research aims to achieve several goals, including:

  • Identifying the correlation between EEG signals and linguistic concepts
  • Elaborating the neurological algorythms, which will connect the langugae concepts with the proper EEG sygnals.
  • Recognizing pre-defined sentences with 70-90% accuracy based on brain activity
  • Developing a software prototype of the cognitive translator
  • Creating a scientifically grounded model for neural linguistic research

The research has the potential to revolutionize language technologies and establish a new research direction in Georgian/Kartvelian studies.

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წყაროები

Gazzaniga, M. S., Ivry, R., & Mangun, G. (2018). Cognitive Neuroscience: The Biology of the Mind. W.W. Norton.

Friederici, A. D. (2017). Language in Our Brain: The Origins of a Uniquely Human Capacity. MIT Press.

Kutas, M., & Federmeier, K. D. (2011). Thirty years and counting: finding meaning in the N400 component of the event‐related brain potential (ERP). Annual Review of Psychology, 62, 621–647.

Niedermeyer, E., & da Silva, F. L. (2004). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields. Lippincott Williams & Wilkins.

Luck, S. J. (2014). An Introduction to the Event-Related Potential Technique. MIT Press.

Cohen, M. X. (2014). Analyzing Neural Time Series Data: Theory and Practice. MIT Press.

Tomasello, M. (2009). The Cultural Origins of Human Cognition. Harvard University Press.

Pulvermüller, F. (2013). How neurons make meaning: brain mechanisms for embodied and abstract-symbolic semantics. Trends in Cognitive Sciences, 17(9), 458–470.

Hickok, G., & Poeppel, D. (2007). The cortical organization of speech processing. Nature Reviews Neuroscience, 8(5), 393–402.

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.

Graves, A., Mohamed, A., & Hinton, G. (2013). Speech recognition with deep recurrent neural networks. ICASSP.

ჩამოტვირთვები

გამოქვეყნებული

2026-04-27
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