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Bioengineering
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Engineering Neural Motor Control: From Mechanisms to Neural Interfaces
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Engineering Neural Motor Control: From Mechanisms to Neural Interfaces

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biomedical Engineering and Biomaterials".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 6064

Special Issue Editors

Dr. Eizo Miyashita

E-Mail Website
Guest Editor
School of Life Science and Technology, Institute of Science Tokyo, Kanagawa 226-8501, Japan
Interests: systems neuroscience; motor control; motor learning; neural coding; neuromodulation
Dr. Yuki Ueyama

E-Mail Website
Guest Editor
Department of Mechanical Engineering, National Defense Academy of Japan, Yokosuka 239-8686, Japan
Interests: motor control; computational neuroscience; rehabilitation; robotics; cybernetics

Special Issue Information

Dear Colleagues,

Neural control of movements is a key issue in neuroscience. Researchers have adopted concepts and technologies from other disciplines to propose working hypotheses, as well as acquire data and analyze data. In particular, engineering provides powerful tools that can be used to unveil and describe the time dynamics of a system, which is an important characteristic of movement. One of the final goals of neuroscience is to understand how to generate appropriate motor commands by the nervous system so as to control the musculoskeletal system within an environment, in which the nervous system should adapt to changes in the body and the environment. Knowledge of this adaptive control mechanism by the nervous system is growing. Not only has this led to greater insights into the nervous system, but this knowledge has also been applied to therapeutics. Electrical stimulation, for example, is used to modulate the nervous system for the treatment of epilepsy and Parkinson’s disease. The brain–machine interface could be a potential prosthetic device for patients with some spinal cord injuries and diseases.

This Special Issue aims to collect recent studies focused on elucidating neural control mechanisms of the body and developing new methodologies translatable to research on the nervous system, as well as to further expand our knowledge that has potential applicability to therapeutics. We welcome authors to submit original papers, reviews, and other articles.

Dr. Eizo Miyashita
Dr. Yuki Ueyama
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 250 words) can be sent to the Editorial Office for assessment.

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. Bioengineering 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 2700 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

  • systems neuroscience
  • computational neuroscience
  • motor control
  • motor learning
  • motor dysfunction
  • rehabilitation
  • neuromodulation
  • human argumentation

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Published Papers (3 papers)

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Research

22 pages, 4772 KB  
Article
Neuroscience-Inspired Deep Learning Brain–Machine Interface Decoder
by Hong-Yun Ou, Takahiro Hasegawa, Osamu Fukayama and Eizo Miyashita
Bioengineering 2026, 13(4), 440; https://doi.org/10.3390/bioengineering13040440 - 10 Apr 2026
Abstract
Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In [...] Read more.
Brain–machine interfaces (BMIs) aim to decode motor intentions from neural activity to enable direct control of external devices. However, most existing decoders rely on monolithic architectures that fail to capture the distinct neural representations of different joint movement directions, limiting their generalizability. In this work, we propose a Single-Direction CNN-LSTM decoder inspired by motor cortex encoding mechanisms, which separately models extension and flexion dynamics through parallel CNN-LSTM branches. Each branch extracts spatial–temporal features from neural spike data and predicts directional joint variables, which are then combined by subtraction to yield the net angular velocity and torque of upper-limb joints. Using invasive recordings from a macaque during a 2D center-out reaching task, we demonstrate that our decoder achieves comparable performance to a conventional CNN-LSTM when trained on all tasks, while significantly outperforming both CNN-LSTM and linear regression baselines in cross-target generalization scenarios. Moreover, the model can capture physiologically meaningful co-contraction patterns, providing richer insights into motor control. These results suggest that incorporating neuroscience-inspired modular decoding into deep neural architectures enhances robustness and adaptability across tasks, offering a promising pathway for BMI applications in prosthetics and rehabilitation. Full article
(This article belongs to the Special Issue Engineering Neural Motor Control: From Mechanisms to Neural Interfaces)
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14 pages, 849 KB  
Article
Short-Term Facility-Based Functional Electrical Stimulation for Chronic Post-Stroke Foot Drop: A Pilot Study
by Diana-Lidia Tache-Codreanu, Ioana Angela Rotaru, Mihai-Andrei Butum-Cristea, Georgeta Stefan, Andrei Tache-Codreanu, Corina Sporea and Ana-Maria Tache-Codreanu
Bioengineering 2026, 13(2), 238; https://doi.org/10.3390/bioengineering13020238 - 18 Feb 2026
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Abstract
Background: Functional Electrical Stimulation (FES) for post-stroke drop foot is commonly applied in acute and subacute stroke rehabilitation or as part of long-term home-based programs in chronic patients. Evidence supporting short facility-based rehabilitation programs incorporating FES in chronic populations remains limited. The aim [...] Read more.
Background: Functional Electrical Stimulation (FES) for post-stroke drop foot is commonly applied in acute and subacute stroke rehabilitation or as part of long-term home-based programs in chronic patients. Evidence supporting short facility-based rehabilitation programs incorporating FES in chronic populations remains limited. The aim of this study was to explore functional outcomes associated with such a program in a chronic population. Materials and methods: A 10-day facility-based rehabilitation program incorporating FES therapy followed by 3-month follow-up was delivered to 14 chronic post-stroke patients with foot drop (8 women; aged 62.6 ± 12.2 years). FES was applied during walking with stimulation synchronized to the swing phase of gait (35 Hz, 300 μs, 15 min per session). Activities of daily living and mobility were assessed using clinical outcome measures. Statistical significance (p < 0.05), effect sizes, and minimal clinically important difference (MCID) responder rates were evaluated. Results: Statistically significant improvements were observed across all outcome measures post-treatment and at follow-up, with MCID responder rates exceeding 50%. Conclusions: A short facility-based multimodal rehabilitation program incorporating FES was associated with functional improvements in chronic post-stroke patients. Given the multimodal design, these findings cannot be attributed to FES alone and should be interpreted as exploratory. Full article
(This article belongs to the Special Issue Engineering Neural Motor Control: From Mechanisms to Neural Interfaces)
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33 pages, 10768 KB  
Article
Analysis of Connectivity in Electromyography Signals to Examine Neural Correlations in the Activation of Lower Leg Muscles for Postural Stability: A Pilot Study
by Gordon Alderink, Diana McCrumb, David Zeitler and Samhita Rhodes
Bioengineering 2025, 12(1), 84; https://doi.org/10.3390/bioengineering12010084 - 17 Jan 2025
Cited by 1 | Viewed by 4672
Abstract
In quiet standing, the central nervous system implements a pre-programmed ankle strategy of postural control to maintain upright balance and stability. This strategy comprises a synchronized common neural drive delivered to synergistically grouped muscles. This study evaluated connectivity between EMG signals of the [...] Read more.
In quiet standing, the central nervous system implements a pre-programmed ankle strategy of postural control to maintain upright balance and stability. This strategy comprises a synchronized common neural drive delivered to synergistically grouped muscles. This study evaluated connectivity between EMG signals of the unilateral and bilateral homologous muscle pairs of the lower legs during various standing balance conditions using magnitude-squared coherence (MSC). The leg muscles examined included the right and left tibialis anterior (TA), medial gastrocnemius (MG), and soleus (S). MSC is a frequency domain measure that quantifies the linear phase relation between two signals and was analyzed in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–100 Hz) neural frequency bands for feet together and feet tandem, with eyes open and eyes closed conditions. Results showed that connectivity in the beta and lower and upper gamma bands (30–100 Hz) was influenced by standing balance conditions and was indicative of a neural drive originating from the motor cortex. Instability was evaluated by comparing less stable standing conditions with a baseline—eyes open feet together stance. Changes in connectivity in the beta and gamma bands were found to be most significant in the muscle pairs of the back leg during a tandem stance regardless of dominant foot placement. MSC identified the MG:S muscle pair as significant for the right and left leg. The results of this study provided insight into the neural mechanism of postural control. Full article
(This article belongs to the Special Issue Engineering Neural Motor Control: From Mechanisms to Neural Interfaces)
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