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Biomimetics
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Advances in Brain–Computer Interfaces 2025
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Advances in Brain–Computer Interfaces 2025

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: 30 November 2025 | Viewed by 2261

Special Issue Editor

Dr. Amin Hekmatmanesh

E-Mail Website
Guest Editor
Mechanical Engineering, LUT School of Energy Systems, LUT University, Lappeenranta, Finland
Interests: brain–computer interface; rehabilitation; neuro-engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain–computer interface (BCI) technology has been introduced to improve the quality of life for people with disabilities or difficulties in their daily lives. BCI applications such as driver assistants, sleep identification for drivers, and controlling a bionic hand/ankle–foot orthosis are widely used for healthy people as well as paralyzed patients. Research in the field mainly focuses on the development of mathematical calculations for brain-controlled vehicles, brain-controlled air vehicles, brain-controlled bionic hands, and brain-controlled foot–ankle braces using biosignals from an electroencephalogram (EEG), electrooculogram (EOG), electromyogram (EMG), or photoplethysmography (PPG).

The mathematical solutions include signal denoising (filtering), feature extraction, and machine learning algorithms. This collection of articles aims to highlight mathematical innovations as well as novel ideas for designing tasks to induce the brain to generate distinctive neuronal patterns. The final goal of this research topic is the discovery of new methods for BCI applications. We welcome manuscripts on the following subtopics:

  • Automatically decoding brain neuron activities by developing mathematical methods for identifying patterns within the EEG signals;
  • Automatically identifying EEG patterns relative to human actions and decisions;
  • Analyzing the patterns generated in a designed task to determine which method is more beneficial, e.g., wavelets, chaotic methods, common spatial patterns, or reinforcing methods;
  • Developing classifiers to automate identification procedures.

Dr. Amin Hekmatmanesh
Guest Editor

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

  • biosignal processing
  • pattern recognition
  • machine learning
  • brain–computer interface
  • health monitoring systems

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Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 1867 KB  
Article
NEuroMOrphic Neural-Response Decoding System for Adaptive and Personalized Neuro-Prosthetics’ Control
by Georgi Rusev, Svetlozar Yordanov, Simona Nedelcheva, Alexander Banderov, Hugo Lafaye de Micheaux, Fabien Sauter-Starace, Tetiana Aksenova, Petia Koprinkova-Hristova and Nikola Kasabov
Biomimetics 2025, 10(8), 518; https://doi.org/10.3390/biomimetics10080518 - 7 Aug 2025
Viewed by 372
Abstract
In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no [...] Read more.
In our previous work, we developed a neuromorphic decoder of intended movements of tetraplegic patients using ECoG recordings from the brain motor cortex, called Motor Control Decoder (MCD). Even though the training data are labeled based on the desired movement, there is no guarantee that the patient is satisfied by the action of the effectors. Hence, the need for the classification of brain signals as satisfactory/unsatisfactory is obvious. Based on previous work, we upgrade our neuromorphic MCD with a Neural Response Decoder (NRD) that is intended to predict whether ECoG data are satisfactory or not in order to improve MCD accuracy. The main aim is to design an actor–critic structure able to adapt via reinforcement learning the MCD (actor) based on NRD (critic) predictions. For this aim, NRD was trained using not only an ECoG signal but also the MCD prediction or prescribed intended movement of the patient. The achieved accuracy of the trained NRD is satisfactory and contributes to improved MCD performance. However, further work has to be carried out to fully utilize the NRD for MCD performance optimization in an on-line manner. Possibility to include feedback from the patient would allow for further improvement of MCD-NRD accuracy. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces 2025)
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25 pages, 6826 KB  
Article
Multi-Class Classification Methods for EEG Signals of Lower-Limb Rehabilitation Movements
by Shuangling Ma, Zijie Situ, Xiaobo Peng, Zhangyang Li and Ying Huang
Biomimetics 2025, 10(7), 452; https://doi.org/10.3390/biomimetics10070452 - 9 Jul 2025
Viewed by 488
Abstract
Brain–Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical [...] Read more.
Brain–Computer Interfaces (BCIs) enable direct communication between the brain and external devices by decoding motor intentions from EEG signals. However, the existing multi-class classification methods for motor imagery EEG (MI-EEG) signals are hindered by low signal quality and limited accuracy, restricting their practical application. This study focuses on rehabilitation training scenarios, aiming to capture the motor intentions of patients with partial or complete motor impairments (such as stroke survivors) and provide feedforward control commands for exoskeletons. This study developed an EEG acquisition protocol specifically for use with lower-limb rehabilitation motor imagery (MI). It systematically explored preprocessing techniques, feature extraction strategies, and multi-classification algorithms for multi-task MI-EEG signals. A novel 3D EEG convolutional neural network (3D EEG-CNN) that integrates time/frequency features is proposed. Evaluations on a self-collected dataset demonstrated that the proposed model achieved a peak classification accuracy of 66.32%, substantially outperforming conventional approaches and demonstrating notable progress in the multi-class classification of lower-limb motor imagery tasks. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces 2025)
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12 pages, 2241 KB  
Article
Wordline Input Bias Scheme for Neural Network Implementation in 3D-NAND Flash
by Hwiho Hwang, Gyeonghae Kim, Dayeon Yu and Hyungjin Kim
Biomimetics 2025, 10(5), 318; https://doi.org/10.3390/biomimetics10050318 - 15 May 2025
Viewed by 837
Abstract
In this study, we propose a neuromorphic computing system based on a 3D-NAND flash architecture that utilizes analog input voltages applied through wordlines (WLs). The approach leverages the velocity saturation effect in short-channel MOSFETs, which enables a linear increase in drain current with [...] Read more.
In this study, we propose a neuromorphic computing system based on a 3D-NAND flash architecture that utilizes analog input voltages applied through wordlines (WLs). The approach leverages the velocity saturation effect in short-channel MOSFETs, which enables a linear increase in drain current with respect to gate voltage in the saturation region. A NAND flash array with a TANOS (TiN/Al2O3/Si3N4/SiO2/poly-Si) gate stack was fabricated, and its electrical and reliability characteristics were evaluated. Output characteristics of short-channel (L = 1 µm) and long-channel (L = 50 µm) devices were compared, confirming the linear behavior of short-channel devices due to velocity saturation. In the proposed system, analog WL voltages serve as inputs, and the summed bitline (BL) currents represent the outputs. Each synaptic weight is implemented using two paired devices, and each WL layer corresponds to a fully connected (FC) layer, enabling efficient vector-matrix multiplication (VMM). MNIST pattern recognition is conducted, demonstrated only a 0.32% accuracy drop for the short-channel device compared to the ideal linear case, and 0.95% degradation under 0.5 V threshold variation, while maintaining robustness. These results highlight the strong potential of 3D-NAND flash memory, which offers high integration density and technological maturity, for neuromorphic computing applications. Full article
(This article belongs to the Special Issue Advances in Brain–Computer Interfaces 2025)
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