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Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in...
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Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Bioinspired Sensorics, Information Processing and Control".

Deadline for manuscript submissions: closed (20 August 2025) | Viewed by 21514

Special Issue Editors

Dr. Constantinos Halkiopoulos

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Guest Editor
Department of Management Science and Technology, University of Patras, 26504 Patras, Greece
Interests: artificial intelligence; data mining; big data; expert systems; computational cognitive science; psychometrics; decision making
Special Issues, Collections and Topics in MDPI journals
Dr. Evgenia Gkintoni

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Guest Editor
Department of Psychiatry, University General Hospital of Patras, 26504 Patras, Greece
Interests: clinical psychology; neuropsychology; cognitive neuroscience; psychopathology; neuroeducation; psychotherapeutic processes; applied psychology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue, "Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research", focuses on the intersection of biomimetics with such vital areas as machine learning, neuropsychology, cognitive neuroscience, cultural psychology, and clinical psychology. The aim of this proposal issue is to concentrate on new approaches using bio-inspired designs and computational models to enhance human cognition, behavior, and therapeutic interventions.

Such fundamental topics will include biomimetic applications in cognitive neuroscience to model brain functions and neuroprosthetics. At the same time, the integration with machine learning aims to improve neuropsychological diagnostics and therapy. Cultural influences in psychology will be highlighted to use biomimetic frameworks. This issue will focus on bio-inspired cognitive models, aiming at a better understanding of psychological phenomena, exploring biomimetic approaches for mental health assessment, and reviewing new therapies that imitate the process of natural healing.

Furthermore, the current Special Issue examines the integration of machine learning algorithms with biomimetic models in neuropsychological diagnostics, focusing on developing adaptive therapeutic interventions using artificial intelligence to predict cognitive decline and tailor such treatments. Such innovations are bound to fundamentally alter conventional diagnostic techniques and therapies.

Additionally, in cultural and clinical psychology domains, biomimetic approaches are proposed to deepen the understanding of cultural influences on cognitive and psychological processes, leading to the development of culturally sensitive therapeutic tools. Moreover, the potential of biomimetic designs for advancing psychological assessments and interventions is explored by developing bio-inspired cognitive models that address complex psychological phenomena such as emotion and memory.

Finally, this Special Issue provides a platform for interdisciplinary collaboration. Therefore, it encourages researchers to share their recent findings, discuss challenges, and present innovative solutions that link biological inspiration with psychological practice. One of the major goals of this Special Issue is to stimulate new directions in research that can further integrate biomimetics into psychology and neuroscience.

Dr. Constantinos Halkiopoulos
Dr. Evgenia Gkintoni
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. Biomimetics is an international peer-reviewed open access monthly 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 2200 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

  • biomimetics
  • machine learning
  • neuropsychology
  • cognitive neuroscience
  • cultural psychology
  • clinical psychology
  • bio-inspired models
  • neuroprosthetics
  • brain–computer interfaces
  • cognitive therapy
  • adaptive therapies
  • real-time monitoring
  • cognitive modeling
  • applied psychology
  • mental health assessment
  • AI in psychology
  • neurorehabilitation
  • personalized medicine
  • culturally sensitive therapies

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Related Special Issue

Published Papers (8 papers)

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Research

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21 pages, 2417 KB  
Article
TrailMap: Pheromone-Based Adaptive Peer Matching for Sustainable Online Support Communities
by Harold Ngabo-Woods, Larisa Dunai, Isabel Seguí Verdú and Dinu Turcanu
Biomimetics 2025, 10(10), 658; https://doi.org/10.3390/biomimetics10100658 - 1 Oct 2025
Viewed by 427
Abstract
Online peer support platforms are vital, scalable resources for mental health, yet their effectiveness is frequently undermined by inefficient user matching, severe participation inequality, and subsequent “super-helper” burnout. This study introduces TrailMap, a novel peer-matching algorithm inspired by the decentralised foraging strategies of [...] Read more.
Online peer support platforms are vital, scalable resources for mental health, yet their effectiveness is frequently undermined by inefficient user matching, severe participation inequality, and subsequent “super-helper” burnout. This study introduces TrailMap, a novel peer-matching algorithm inspired by the decentralised foraging strategies of ant colonies. By treating user interactions as paths that gain or lose “pheromone” based on helpfulness ratings, the system enables the community to collectively and adaptively identify its most effective helpers. A two-phase validation study was conducted. First, an agent-based simulation demonstrated that TrailMap reduced the mean time to a helpful response by over 70% and improved workload equity compared to random routing. Second, a four-week randomised controlled pilot study with human participants confirmed these gains, showing a 76% reduction in median wait time and significantly higher perceived helpfulness ratings. The findings suggest that by balancing the workload, TrailMap enhances not only the efficiency but also the socio-technical sustainability of online support communities. TrailMap provides a practical, nature-inspired method for building more resilient and equitable online support communities, enhancing access to effective mental health support. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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27 pages, 3660 KB  
Article
Deep Learning-Based Evaluation of Postural Control Impairments Caused by Stroke Under Altered Sensory Conditions
by Armin Najipour, Siamak Khorramymehr, Mehdi Razeghi and Kamran Hassani
Biomimetics 2025, 10(9), 586; https://doi.org/10.3390/biomimetics10090586 - 3 Sep 2025
Viewed by 750
Abstract
Accurate and timely detection of postural control impairments in stroke patients is crucial for effective rehabilitation and fall prevention. Traditional clinical assessments often rely on qualitative observation and handcrafted features, which may fail to capture the nonlinear and uncertain nature of postural deficits. [...] Read more.
Accurate and timely detection of postural control impairments in stroke patients is crucial for effective rehabilitation and fall prevention. Traditional clinical assessments often rely on qualitative observation and handcrafted features, which may fail to capture the nonlinear and uncertain nature of postural deficits. This study addresses these limitations by introducing a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs) with Type-2 fuzzy logic activation to robustly classify sensory dysfunction under altered balance conditions. Using an EquiTest-derived dataset of 8316 labeled samples from 700 participants across six standardized sensory manipulation scenarios, the proposed method achieved 97% accuracy, 96% precision, 97% sensitivity, and 96% specificity, outperforming conventional CNN and other baseline classifiers. The approach demonstrated resilience to measurement noise down to 1 dB SNR, confirming its robustness in realistic clinical environments. These results suggest that the proposed system can serve as a practical, non-invasive tool for clinical diagnosis and personalized rehabilitation planning, supporting data-driven decision-making in stroke care. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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23 pages, 3004 KB  
Article
An Ensemble Learning for Automatic Stroke Lesion Segmentation Using Compressive Sensing and Multi-Resolution U-Net
by Mohammad Emami, Mohammad Ali Tinati, Javad Musevi Niya and Sebelan Danishvar
Biomimetics 2025, 10(8), 509; https://doi.org/10.3390/biomimetics10080509 - 4 Aug 2025
Viewed by 906
Abstract
A stroke is a critical medical condition and one of the leading causes of death among humans. Segmentation of the lesions of the brain in which the blood flow is impeded because of blood coagulation plays a vital role in drug prescription and [...] Read more.
A stroke is a critical medical condition and one of the leading causes of death among humans. Segmentation of the lesions of the brain in which the blood flow is impeded because of blood coagulation plays a vital role in drug prescription and medical diagnosis. Computed tomography (CT) scans play a crucial role in detecting abnormal tissue. There are several methods for segmenting medical images that utilize the main images without considering the patient’s privacy information. In this paper, a deep network is proposed that utilizes compressive sensing and ensemble learning to protect patient privacy and segment the dataset efficiently. The compressed version of the input CT images from the ISLES challenge 2018 dataset is applied to the ensemble part of the proposed network, which consists of two multi-resolution modified U-shaped networks. The evaluation metrics of accuracy, specificity, and dice coefficient are 92.43%, 91.3%, and 91.83%, respectively. The comparison to the state-of-the-art methods confirms the efficiency of the proposed compressive sensing-based ensemble net (CS-Ensemble Net). The compressive sensing part provides information privacy, and the parallel ensemble learning produces better results. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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20 pages, 2680 KB  
Article
Improved Automatic Deep Model for Automatic Detection of Movement Intention from EEG Signals
by Lida Zare Lahijan, Saeed Meshgini, Reza Afrouzian and Sebelan Danishvar
Biomimetics 2025, 10(8), 506; https://doi.org/10.3390/biomimetics10080506 - 4 Aug 2025
Viewed by 927
Abstract
Automated movement intention is crucial for brain–computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This [...] Read more.
Automated movement intention is crucial for brain–computer interface (BCI) applications. The automatic identification of movement intention can assist patients with movement problems in regaining their mobility. This study introduces a novel approach for the automatic identification of movement intention through finger tapping. This work has compiled a database of EEG signals derived from left finger taps, right finger taps, and a resting condition. Following the requisite pre-processing, the captured signals are input into the proposed model, which is constructed based on graph theory and deep convolutional networks. In this study, we introduce a novel architecture based on six deep convolutional graph layers, specifically designed to effectively capture and extract essential features from EEG signals. The proposed model demonstrates a remarkable performance, achieving an accuracy of 98% in a binary classification task when distinguishing between left and right finger tapping. Furthermore, in a more complex three-class classification scenario, which includes left finger tapping, right finger tapping, and an additional class, the model attains an accuracy of 92%. These results highlight the effectiveness of the architecture in decoding motor-related brain activity from EEG data. Furthermore, relative to recent studies, the suggested model exhibits significant resilience in noisy situations, making it suitable for online BCI applications. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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19 pages, 3024 KB  
Article
Feedback-Driven Dynamical Model for Axonal Extension on Parallel Micropatterns
by Kyle Cheng, Udathari Kumarasinghe and Cristian Staii
Biomimetics 2025, 10(7), 456; https://doi.org/10.3390/biomimetics10070456 - 11 Jul 2025
Viewed by 620
Abstract
Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these [...] Read more.
Despite significant advances in understanding neuronal development, a fully quantitative framework that integrates intracellular mechanisms with environmental cues during axonal growth remains incomplete. Here, we present a unified biophysical model that captures key mechanochemical processes governing axonal extension on micropatterned substrates. In these environments, axons preferentially align with the pattern direction, form bundles, and advance at constant speed. The model integrates four core components: (i) actin–adhesion traction coupling, (ii) lateral inhibition between neighboring axons, (iii) tubulin transport from soma to growth cone, and (iv) orientation dynamics guided by substrate anisotropy. Dynamical systems analysis reveals that a saddle–node bifurcation in the actin adhesion subsystem drives a transition to a high-traction motile state, while traction feedback shifts a pitchfork bifurcation in the signaling loop, promoting symmetry breaking and robust alignment. An exact linear solution in the tubulin transport subsystem functions as a built-in speed regulator, ensuring stable elongation rates. Simulations using experimentally inferred parameters accurately reproduce elongation speed, alignment variance, and bundle spacing. The model provides explicit design rules for enhancing axonal alignment through modulation of substrate stiffness and adhesion dynamics. By identifying key control parameters, this work enables rational design of biomaterials for neural repair and engineered tissue systems. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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Review

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16 pages, 1768 KB  
Review
The Next Frontier in Neuroprosthetics: Integration of Biomimetic Somatosensory Feedback
by Yucheng Tian, Giacomo Valle, Paul S. Cederna and Stephen W. P. Kemp
Biomimetics 2025, 10(3), 130; https://doi.org/10.3390/biomimetics10030130 - 21 Feb 2025
Cited by 2 | Viewed by 5932
Abstract
The development of neuroprosthetic limbs—robotic devices designed to restore lost limb functions for individuals with limb loss or impairment—has made significant strides over the past decade, reaching the stage of successful human clinical trials. A current research focus involves providing somatosensory feedback to [...] Read more.
The development of neuroprosthetic limbs—robotic devices designed to restore lost limb functions for individuals with limb loss or impairment—has made significant strides over the past decade, reaching the stage of successful human clinical trials. A current research focus involves providing somatosensory feedback to these devices, which was shown to improve device control performance and embodiment. However, widespread commercialization and clinical adoption of somatosensory neuroprosthetic limbs remain limited. Biomimetic neuroprosthetics, which seeks to resemble the natural sensory processing of tactile information and to deliver biologically relevant inputs to the nervous system, offer a promising path forward. This method could bridge the gap between existing neurotechnology and the future realization of bionic limbs that more closely mimic biological limbs. In this review, we examine the recent key clinical trials that incorporated somatosensory feedback on neuroprosthetic limbs through biomimetic neurostimulation for individuals with missing or paralyzed limbs. Furthermore, we highlight the potential impact of cutting-edge advances in tactile sensing, encoding strategies, neuroelectronic interfaces, and innovative surgical techniques to create a clinically viable human–machine interface that facilitates natural tactile perception and advanced, closed-loop neuroprosthetic control to improve the quality of life of people with sensorimotor impairments. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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Other

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72 pages, 4170 KB  
Systematic Review
Digital Twin Cognition: AI-Biomarker Integration in Biomimetic Neuropsychology
by Evgenia Gkintoni and Constantinos Halkiopoulos
Biomimetics 2025, 10(10), 640; https://doi.org/10.3390/biomimetics10100640 - 23 Sep 2025
Cited by 1 | Viewed by 2220
Abstract
(1) Background: The convergence of digital twin technology, artificial intelligence, and multimodal biomarkers heralds a transformative era in neuropsychological assessment and intervention. Digital twin cognition represents an emerging paradigm that creates dynamic, personalized virtual models of individual cognitive systems, enabling continuous monitoring, predictive [...] Read more.
(1) Background: The convergence of digital twin technology, artificial intelligence, and multimodal biomarkers heralds a transformative era in neuropsychological assessment and intervention. Digital twin cognition represents an emerging paradigm that creates dynamic, personalized virtual models of individual cognitive systems, enabling continuous monitoring, predictive modeling, and precision interventions. This systematic review comprehensively examines the integration of AI-driven biomarkers within biomimetic neuropsychological frameworks to advance personalized cognitive health. (2) Methods: Following PRISMA 2020 guidelines, we conducted a systematic search across six major databases spanning medical, neuroscience, and computer science disciplines for literature published between 2014 and 2024. The review synthesized evidence addressing five research questions examining framework integration, predictive accuracy, clinical translation, algorithm effectiveness, and neuropsychological validity. (3) Results: Analysis revealed that multimodal integration approaches combining neuroimaging, physiological, behavioral, and digital phenotyping data substantially outperformed single-modality assessments. Deep learning architectures demonstrated superior pattern recognition capabilities, while traditional machine learning maintained advantages in interpretability and clinical implementation. Successful frameworks, particularly for neurodegenerative diseases and multiple sclerosis, achieved earlier detection, improved treatment personalization, and enhanced patient outcomes. However, significant challenges persist in algorithm interpretability, population generalizability, and the integration of healthcare systems. Critical analysis reveals that high-accuracy claims (85–95%) predominantly derive from small, homogeneous cohorts with limited external validation. Real-world performance in diverse clinical settings likely ranges 10–15% lower, emphasizing the need for large-scale, multi-site validation studies before clinical deployment. (4) Conclusions: Digital twin cognition establishes a new frontier in personalized neuropsychology, offering unprecedented opportunities for early detection, continuous monitoring, and adaptive interventions while requiring continued advancement in standardization, validation, and ethical frameworks. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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65 pages, 2739 KB  
Systematic Review
Brain-Inspired Multisensory Learning: A Systematic Review of Neuroplasticity and Cognitive Outcomes in Adult Multicultural and Second Language Acquisition
by Evgenia Gkintoni, Stephanos P. Vassilopoulos and Georgios Nikolaou
Biomimetics 2025, 10(6), 397; https://doi.org/10.3390/biomimetics10060397 - 12 Jun 2025
Cited by 4 | Viewed by 7998
Abstract
Background: Multicultural education and second-language acquisition engaged neural networks, supporting executive function, memory, and social cognition in adulthood, represent powerful forms of brain-inspired multisensory learning. The neuroeducational framework integrates neuroscience with pedagogical practice to understand how linguistically and culturally rich environments drive neuroplasticity [...] Read more.
Background: Multicultural education and second-language acquisition engaged neural networks, supporting executive function, memory, and social cognition in adulthood, represent powerful forms of brain-inspired multisensory learning. The neuroeducational framework integrates neuroscience with pedagogical practice to understand how linguistically and culturally rich environments drive neuroplasticity and cognitive adaptation in adult learners. Objective: This systematic review synthesizes findings from 80 studies examining neuroplasticity and cognitive outcomes in adults undergoing multicultural and second-language acquisition, focusing on underlying neural mechanisms and educational effectiveness. Methods: The analysis included randomized controlled trials and longitudinal studies employing diverse neuroimaging techniques (fMRI, MEG, DTI) to assess structural and functional brain network changes. Interventions varied in terms of immersion intensity (ranging from limited classroom contact to complete environmental immersion), multimodal approaches (integrating visual, auditory, and kinesthetic elements), feedback mechanisms (immediate vs. delayed, social vs. automated), and learning contexts (formal instruction, naturalistic acquisition, and technology-enhanced environments). Outcomes encompassed cognitive domains (executive function, working memory, attention) and socio-emotional processes (empathy, cultural adaptation). Results: Strong evidence demonstrates that multicultural and second-language acquisition induce specific neuroplastic adaptations, including enhanced connectivity between language and executive networks, increased cortical thickness in frontal–temporal regions, and white matter reorganization supporting processing efficiency. These neural changes are correlated with significant improvements in working memory, attentional control, and cognitive flexibility. Immersion intensity, multimodal design features, learning context, and individual differences, including age and sociocultural background, moderate the effectiveness of interventions across adult populations. Conclusions: Adult multicultural and second-language acquisition represents a biologically aligned educational approach that leverages natural neuroplastic mechanisms to enhance cognitive resilience. Findings support the design of interventions that engage integrated neural networks through rich, culturally relevant environments, with significant implications for cognitive health across the adult lifespan and for evidence-based educational practice. Full article
(This article belongs to the Special Issue Innovative Biomimetics: Integrating Machine Learning, Neuropsychology, and Cognitive Neuroscience in Applied Psychological Research)
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