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Applied Sciences
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Modern Advances in Neurolinguistics and EEG Language Processing
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Modern Advances in Neurolinguistics and EEG Language Processing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Applied Neuroscience and Neural Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4094

Special Issue Editors

Prof. Dr. Alexander N. Pisarchik

E-Mail Website
Guest Editor
Center for Biomedical Technology, Technical University of Madrid, Campus Montegancedo, Pozuelo de Alarcón, 28223 Madrid, Spain
Interests: complex systems; bioinformatics; mathematical and computational biology; optics and photonics; biological physics; cognitive neuroscience
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alexander E. Hramov

E-Mail Website
Guest Editor
1. Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, Kaliningrad 236041, Russia
2. Neurosciences Research Institute of Samara State Medical University, Samara 443079, Russia
3. Neuroscience and Cognitive Technology Laboratory, Innopolis University, Kazan 420500, Russia
Interests: neuroscience; nonlinear dynamics; wavelets; intelligent systems; synchronization; biomedical signal processing; neuronal networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce the launch of a new Special Issue of Applied Sciences, entitled “Modern Advances in Neurolinguistics and EEG Language Processing”.

Neurolinguistics is a rapidly developing field of science at the intersection of neuroscience, linguistics, neurobiology, cognitive science, neuropsychology, computer science, pedagogy, and data science. Neurolinguistics studies the neurophysiological brain activity and neural mechanisms associated with the learning, hearing, reading, writing, understanding, and speaking of language. Over the past decade, neurolinguistics methods have become widely used in language research to answer questions related to the mapping of language in the brain. When a person is engaged in a particular linguistic task (such as language comprehension or recall), the underlying electrical or magnetic brain activity can be monitored using electroencephalography (EEG) or magnetoencephalography (MEG). These techniques have advantages over traditional behavioral tests because they allow the detection of event-related potentials (ERPs) or event-related fields (ERFs). The latency and amplitude of these quantities provide important information about how the brain performs cognitive processing. Furthermore, the use of EEG/MEG in studying language processing makes enables the exclusion of the influence of undesirable subjective factors. In addition, hemodynamic methods such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), are used to localize brain areas associated with specific linguistic events take.

This special issue aims to provide the academic community with a forum to present and discuss the latest theoretical and applied research related to recent advances in neurolinguistics. We invite original papers covering new physical and mathematical methods, innovative approaches, cutting-edge technologies, and important new techniques that could lead to significant advances in the analysis of neuroimaging data related to linguistic tasks.

In particular, topics of interest, but are not limited to, the following issues:

  • Neuroimaging methods in linguistics
  • Neurolinguistic modalities
  • Neural mechanisms associated with linguistic tasks
  • Novel algorithms of language-related data processing
  • Language neuroanatomy
  • Experimental paradigms in neurolinguistics
  • Brain connectivity when solving linguistic problems
  • Learning foreign languages and bilingualism
  • Language disorders (aphasia, dyslexia, stuttering, etc.)
  • Lexical semantics
  • Sentence comprehension
  • Neurolinguistic programming
  • Neuropsychology
  • Psycholinguistics

Prof. Dr. Alexander N. Pisarchik
Prof. Dr. Alexander E. Hramov
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • electroencephalography (EEG)
  • magnetoencephalography (MEG)
  • event-related Potential (ERP)
  • event-related Field (ERF)
  • positron emission tomography (PET)
  • functional magnetic resonance imaging (fMRI)
  • MEG/EEG data processing
  • MEG/EEG source reconstruction
  • brain functional connectivity restoration

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Published Papers (2 papers)

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Research

26 pages, 4063 KiB  
Article
Exploring the Cognitive Neural Basis of Factuality in Abstractive Text Summarization Models: Interpretable Insights from EEG Signals
by Zhejun Zhang, Yingqi Zhu, Yubo Zheng, Yingying Luo, Hengyi Shao, Shaoting Guo, Liang Dong, Lin Zhang and Lei Li
Appl. Sci. 2024, 14(2), 875; https://doi.org/10.3390/app14020875 - 19 Jan 2024
Viewed by 1337
Abstract
(1) Background: Information overload challenges decision-making in the Industry 4.0 era. While Natural Language Processing (NLP), especially Automatic Text Summarization (ATS), offers solutions, issues with factual accuracy persist. This research bridges cognitive neuroscience and NLP, aiming to improve model interpretability. (2) Methods: This [...] Read more.
(1) Background: Information overload challenges decision-making in the Industry 4.0 era. While Natural Language Processing (NLP), especially Automatic Text Summarization (ATS), offers solutions, issues with factual accuracy persist. This research bridges cognitive neuroscience and NLP, aiming to improve model interpretability. (2) Methods: This research examined four fact extraction techniques: dependency relation, named entity recognition, part-of-speech tagging, and TF-IDF, in order to explore their correlation with human EEG signals. Representational Similarity Analysis (RSA) was applied to gauge the relationship between language models and brain activity. (3) Results: Named entity recognition showed the highest sensitivity to EEG signals, marking the most significant differentiation between factual and non-factual words with a score of −0.99. The dependency relation followed with −0.90, while part-of-speech tagging and TF-IDF resulted in 0.07 and −0.52, respectively. Deep language models such as GloVe, BERT, and GPT-2 exhibited noticeable influences on RSA scores, highlighting the nuanced interplay between brain activity and these models. (4) Conclusions: Our findings emphasize the crucial role of named entity recognition and dependency relations in fact extraction and demonstrate the independent effects of different models and TOIs on RSA scores. These insights aim to refine algorithms to reflect human text processing better, thereby enhancing ATS models’ factual integrity. Full article
(This article belongs to the Special Issue Modern Advances in Neurolinguistics and EEG Language Processing)
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12 pages, 2154 KiB  
Article
A Novel Computationally Efficient Approach for Exploring Neural Entrainment to Continuous Speech Stimuli Incorporating Cross-Correlation
by Luong Do Anh Quan, Le Thi Trang, Hyosung Joo, Dongseok Kim and Jihwan Woo
Appl. Sci. 2023, 13(17), 9839; https://doi.org/10.3390/app13179839 - 31 Aug 2023
Viewed by 1209
Abstract
A linear system identification technique has been widely used to track neural entrainment in response to continuous speech stimuli. Although the approach of the standard regularization method using ridge regression provides a straightforward solution to estimate and interpret neural responses to continuous speech [...] Read more.
A linear system identification technique has been widely used to track neural entrainment in response to continuous speech stimuli. Although the approach of the standard regularization method using ridge regression provides a straightforward solution to estimate and interpret neural responses to continuous speech stimuli, inconsistent results and costly computational processes can arise due to the need for parameter tuning. We developed a novel approach to the system identification method called the detrended cross-correlation function, which aims to map stimulus features to neural responses using the reverse correlation and derivative of convolution. This non-parametric (i.e., no need for parametric tuning) approach can maintain consistent results. Moreover, it provides a computationally efficient training process compared to the conventional method of ridge regression. The detrended cross-correlation function correctly captures the temporal response function to speech envelope and the spectral–temporal receptive field to speech spectrogram in univariate and multivariate forward models, respectively. The suggested model also provides more efficient computation compared to the ridge regression to process electroencephalography (EEG) signals. In conclusion, we suggest that the detrended cross-correlation function can be comparably used to investigate continuous speech- (or sound-) evoked EEG signals. Full article
(This article belongs to the Special Issue Modern Advances in Neurolinguistics and EEG Language Processing)
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