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The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From...
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The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurorehabilitation".

Deadline for manuscript submissions: closed (15 December 2025) | Viewed by 26459

Special Issue Editors

Prof. Dr. Rocco Salvatore Calabrò

E-Mail Website
Guest Editor
Neurology Unit, IRCCS Centro Neurolesi “Bonino-Pulejo”, 98124 Messina, Italy
Interests: stroke; neuropsychology; neurological diseases; clinical neurology; stroke rehabilitation; multiple sclerosis; brain injury; traumatic brain injury; clinical neurophysiology
Special Issues, Collections and Topics in MDPI journals
Dr. David Perpetuini

E-Mail Website
Guest Editor
Department InGeo, University "G. d'Annunzio" Chieti-Pescara, 65127 Pescara, Italy
Interests: artificial intelligence; bioengineering; signal processing; human–computer interaction
Special Issues, Collections and Topics in MDPI journals
Dr. Irene Aprile

E-Mail Website
Guest Editor
IRCCS Fondazione Don Carlo Gnocchi, Via di Scandicci 269, 50143 Firenze, Italy
Interests: robotic rehabilitation; movement analysis; gait analysis; neurorehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Neurorehabilitation is a specialized healthcare field aiding individuals with neurological injuries, employing various therapies to restore motor skills and cognitive function, enhancing their quality of life. In recent years, the use of innovative technologies, such as robotics, has proven effective in the treatment of different neurological disorders, including stroke, traumatic brain and spinal cord injury, multiple sclerosis and Parkinson’s disease. Indeed, better outcomes in terms of gait, balance and upper limb function have been described after robotic-aided training, as compared to conventional rehabilitation.

Artificial intelligence (AI) encompasses computer systems that mimic human cognitive functions, performing tasks like learning, reasoning, problem solving and decision making. The integration of AI in neurorehabilitation holds great promise, but it is crucial to approach this technology with a clear understanding of its capabilities and limitations. AI can enhance assessment, diagnosis and personalized treatment plans, but it should complement, rather than replace, human healthcare providers.

The aim of this Special Issue is to provide researchers and clinicians with indications on the current use of robotic devices as both assessment and rehabilitation tools. Moreover, we aim at demonstrating the growing role of AI in the rehabilitation field.

Papers dealing with robotic-assisted motion analysis and rehabilitation in different neurological disorders are welcomed. Combined advanced approaches and use of AI in predicting outcomes (such as deep and machine learning) or to improve assistive robotics are particularly welcomed.   

Dr. Rocco Salvatore Calabrò
Dr. David Perpetuini
Dr. Irene Aprile
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. Brain Sciences 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

  • robotic-assisted rehabilitation
  • machine learning
  • neurological disorders
  • artificial intelligence

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Published Papers (7 papers)

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Research

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45 pages, 7613 KB  
Article
BrainTwin-AI: A Multimodal MRI-EEG-Based Cognitive Digital Twin for Real-Time Brain Health Intelligence
by Himadri Nath Saha, Utsho Banerjee, Rajarshi Karmakar, Saptarshi Banerjee and Jon Turdiev
Brain Sci. 2026, 16(4), 411; https://doi.org/10.3390/brainsci16040411 - 13 Apr 2026
Viewed by 1286
Abstract
Background/Objectives: Brain health monitoring is increasingly essential as modern cognitive load, stress, and lifestyle pressures contribute to widespread neural instability. The paper presents BrainTwin, a next-generation cognitive digital twin, as a patient-specific, constantly updating computer model that combines state-of-the-art MRI analytics for [...] Read more.
Background/Objectives: Brain health monitoring is increasingly essential as modern cognitive load, stress, and lifestyle pressures contribute to widespread neural instability. The paper presents BrainTwin, a next-generation cognitive digital twin, as a patient-specific, constantly updating computer model that combines state-of-the-art MRI analytics for neuro-oncological assessment related to clinical study and management of tumors affecting the central nervous system (including their detection, progression, and monitoring) with real-time EEG-based brain health intelligence. Methods: Structural analysis is driven by an Enhanced Vision Transformer (ViT++), which improves spatial representation and boundary localization, achieving more accurate tumor prediction than conventional models. The extracted tumor volume forms the baseline for short-horizon tumor progression modeling. Parallel to MRI analysis, continuous EEG signals are captured through an in-house wearable skullcap, preprocessed using Edge AI on a Hailo Toolkit-enabled Raspberry Pi 5 for low-latency denoising and secure cloud transmission. Pre-processed EEG packets are authenticated at the fog layer, ensuring secure and reliable cloud transfer, enabling significant load reduction in the edge and cloud nodes. In the digital twin, EEG characteristics offer real-time functional monitoring through dynamic brainwave analysis, while a BiLSTM classifier distinguishes relaxed, stress, and fatigue states, which are probabilistically inferred cognitive conditions derived from EEG spectral patterns. Unlike static MRI imaging, EEG provides real-time brain health monitoring. The BrainTwin performs EEG–MRI fusion, correlating functional EEG metrics with ViT++ structural embeddings to produce a single risk score that can be interpreted by clinicians to determine brain vulnerability to future diseases. Explainable artificial intelligence (XAI) provides clinical interpretability through gradient-weighted class activation mapping (Grad-CAM) heatmaps, which are used to interpret ViT++ decisions and are visualized on a 3D interactive brain model to allow more in-depth inspection of spatial details. Results: The evaluation metrics demonstrate a BiLSTM macro-F1 of 0.94 (Precision/Recall/F1: Relaxed 0.96, Stress 0.93, Fatigue 0.92) and a ViT++ MRI accuracy of 96%, outperforming baseline architectures. Conclusions: These results demonstrate BrainTwin’s reliability, interpretability, and clinical utility as an integrated digital companion for tumor assessment and real-time functional brain monitoring. Full article
(This article belongs to the Special Issue The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment)
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23 pages, 2156 KB  
Article
Toward Multi-Dimensional Depression Assessment: EEG-Based Machine Learning and Neurophysiological Interpretation for Diagnosis, Severity, and Cognitive Decline
by Farhad Nassehi, Asuhan Zupan, Aykut Eken, Sinan Yetkin and Osman Erogul
Brain Sci. 2026, 16(2), 139; https://doi.org/10.3390/brainsci16020139 - 28 Jan 2026
Viewed by 823
Abstract
Background/Objectives: Depressive disorder (DD) is a prevalent psychiatric condition often diagnosed through subjective self-reports, which can be time-consuming and lead to inaccurate assessments. To enhance diagnostic precision, integrating Electroencephalography (EEG) with machine learning (ML) has gained attention as an objective approach for DD [...] Read more.
Background/Objectives: Depressive disorder (DD) is a prevalent psychiatric condition often diagnosed through subjective self-reports, which can be time-consuming and lead to inaccurate assessments. To enhance diagnostic precision, integrating Electroencephalography (EEG) with machine learning (ML) has gained attention as an objective approach for DD diagnosis and severity assessment. Methods: We propose an interpretable EEG-based ML framework that integrates optimized functional connectivity features, including Coherence, Phase Lag Index (PLI), and Granger causality, to explore EEG-based functional connectivity patterns in individuals clinically diagnosed with depressive DD and to model symptom severity and cognitive vulnerability. The identified biomarkers provide a promising foundation for developing objective, clinically actionable decision-support tools in psychiatric care. Feature selection was performed using the Neighborhood Component Analysis (NCA) method, and biomarkers were identified through statistical tests. Results: The highest classification performance (97.66% ± 2.05%accuracy, 99.20% ± 1.10% sensitivity, 95.91% ± 4.66% specificity, 98.00% ± 1.02% f1-score, and 0.95 ± 0.48 MCC) was achieved using 21 NCA-selected features with a KNN (K = 9) classifier. The best severity assessment (r2 = 0.89 ± 0.10, MSE = 3.96 ± 17.05) and cognitive impairment prediction (r2 = 0.89 ± 0.06, MSE = 0.23 ± 0.45) were obtained using an ANN regressor with 20 and 17 NCA-selected features, respectively. Conclusions: Our approach outperforms previous EEG-based ML models in DD classification and severity prediction using fewer features. Notably, this is the first study to use EEG connectivity features to predict patients’ severity and cognitive impairment in DD. Coherence and PLI values from frontal and temporal pathways across the alpha, beta, and gamma sub-bands may serve as critical biomarkers for DD diagnosis, severity assessment, and prediction of cognitive impairment. Full article
(This article belongs to the Special Issue The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment)
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12 pages, 262 KB  
Article
Effects of Digital Neurohabilitation on Attention and Memory in Patients with a Diagnosis of Pediatric Obesity: Case Series
by Noemí Cárdenas-Rodríguez, Claudia Andrea Chávez-Mejía, Vania Sofía Gardida-Álvarez, Norma Angélica Labra-Ruíz, Julieta Griselda Mendoza-Torreblanca and Eduardo Espinosa-Garamendi
Brain Sci. 2025, 15(4), 353; https://doi.org/10.3390/brainsci15040353 - 28 Mar 2025
Cited by 1 | Viewed by 1258
Abstract
Objective: Obesity represents a health risk and several studies have linked this clinical entity to cognitive deficits. Among the neuropsychological rehabilitation tools, Peak, a digital application, has shown positive results as a therapeutic method. The aim of this work was to measure, for [...] Read more.
Objective: Obesity represents a health risk and several studies have linked this clinical entity to cognitive deficits. Among the neuropsychological rehabilitation tools, Peak, a digital application, has shown positive results as a therapeutic method. The aim of this work was to measure, for the first time, cognitive deficits and the effects of Peak digital cognitive neurohabilitation therapy in patients diagnosed with obesity. Methods: Peak treatment was offered to the parents who agreed and lasted 6 months, including the neurocognitive evaluation. The patients used Peak five times a day for 20 min. The Neuropsychological Attention and Memory Battery (NEUROPSI) was applied before and after the intervention. Results: The results revealed posttest changes in attention and executive function, memory, and total attention and memory. Significant clinical changes were observed, and the diagnostic range increased from severe to moderate. Conclusions: We concluded that, through an intervention with the Peak app, it is possible to enable attention and memory, which represent the main cognitive deficits in obese pediatric patients. Full article
(This article belongs to the Special Issue The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment)
13 pages, 1269 KB  
Article
Improving Neuroplasticity through Robotic Verticalization Training in Patients with Minimally Conscious State: A Retrospective Study
by Rosaria De Luca, Antonio Gangemi, Mirjam Bonanno, Rosa Angela Fabio, Davide Cardile, Maria Grazia Maggio, Carmela Rifici, Giuliana Vermiglio, Daniela Di Ciuccio, Angela Messina, Angelo Quartarone and Rocco Salvatore Calabrò
Brain Sci. 2024, 14(4), 319; https://doi.org/10.3390/brainsci14040319 - 27 Mar 2024
Cited by 9 | Viewed by 3961
Abstract
In disorders of consciousness, verticalization is considered an effective type of treatment to improve motor and cognitive recovery. Our purpose is to investigate neurophysiological effects of robotic verticalization training (RVT) in patients with minimally conscious state (MCS). Thirty subjects affected by MCS due [...] Read more.
In disorders of consciousness, verticalization is considered an effective type of treatment to improve motor and cognitive recovery. Our purpose is to investigate neurophysiological effects of robotic verticalization training (RVT) in patients with minimally conscious state (MCS). Thirty subjects affected by MCS due to traumatic or vascular brain injury, attending the intensive Neurorehabilitation Unit of the IRCCS Neurolesi (Messina, Italy), were included in this retrospective study. They were equally divided into two groups: the control group (CG) received traditional verticalization with a static bed and the experimental group (EG) received advanced robotic verticalization using the Erigo device. Each patient was evaluated using both clinical scales, including Levels of Cognitive Functioning (LCF) and Functional Independence Measure (FIM), and quantitative EEG pre (T0) and post each treatment (T1). The treatment lasted for eight consecutive weeks, and sessions were held three times a week, in addition to standard neurorehabilitation. In addition to a notable improvement in clinical parameters, such as functional (FIM) (p < 0.01) and cognitive (LCF) (p < 0.01) outcomes, our findings showed a significant modification in alpha and beta bands post-intervention, underscoring the promising effect of the Erigo device to influence neural plasticity and indicating a noteworthy difference between pre-post intervention. This was not observed in the CG. The observed changes in alpha and beta bands underscore the potential of the Erigo device to induce neural plasticity. The device’s custom features and programming, tailored to individual patient needs, may contribute to its unique impact on brain responses. Full article
(This article belongs to the Special Issue The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment)
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18 pages, 1062 KB  
Article
Improving Outcomes in People with Spinal Cord Injury: Encouraging Results from a Multidisciplinary Advanced Rehabilitation Pathway
by Maria Grazia Maggio, Mirjam Bonanno, Alfredo Manuli and Rocco Salvatore Calabrò
Brain Sci. 2024, 14(2), 140; https://doi.org/10.3390/brainsci14020140 - 28 Jan 2024
Cited by 26 | Viewed by 9439
Abstract
Spinal cord injury (SCI) consists of damage to any segment of the spinal cord extending to potential harm to nerves in the cauda equina. Rehabilitative efforts for SCI can involve conventional physiotherapy, innovative technologies, as well as cognitive treatment and psychological support. The [...] Read more.
Spinal cord injury (SCI) consists of damage to any segment of the spinal cord extending to potential harm to nerves in the cauda equina. Rehabilitative efforts for SCI can involve conventional physiotherapy, innovative technologies, as well as cognitive treatment and psychological support. The aim of this study is to evaluate the feasibility of a dedicated, multidisciplinary, and integrated intervention path for SCI, encompassing both conventional and technological interventions, while observing their impact on cognitive, motor, and behavioral outcomes and the overall quality of life for individuals with SCI. Forty-two patients with SCI were included in the analysis utilizing electronic recovery system data. The treatment regimen included multidisciplinary rehabilitation approaches, such as traditional physiotherapy sessions, speech therapy, psychological support, robotic devices, advanced cognitive rehabilitation, and other interventions. Pre–post comparisons showed a significant improvement in lower limb function (Fugl Meyer Assessment-FMA < 0.001), global cognitive functioning (Montreal Cognitive Assessment-MoCA p < 0.001), and perceived quality of life at both a physical and mental level (Short Form-12-SF-12 p < 0.001). Furthermore, we found a significant reduction in depressive state (Beck Depression Inventory-BDI p < 0.001). In addition, we assessed patient satisfaction using the Short Form of the Patient Satisfaction Questionnaire (PSQ), offering insights into the subjective evaluation of the intervention. In conclusion, this retrospective study provides positive results in terms of improvements in motor function, cognitive functions, and quality of life, highlighting the importance of exploring multidisciplinary approaches. Full article
(This article belongs to the Special Issue The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment)
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Review

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14 pages, 283 KB  
Review
Revealing the Complexity of Fatigue: A Review of the Persistent Challenges and Promises of Artificial Intelligence
by Thorsten Rudroff
Brain Sci. 2024, 14(2), 186; https://doi.org/10.3390/brainsci14020186 - 19 Feb 2024
Cited by 19 | Viewed by 6142
Abstract
Part I reviews persistent challenges obstructing progress in understanding complex fatigue’s biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and [...] Read more.
Part I reviews persistent challenges obstructing progress in understanding complex fatigue’s biology. Difficulties quantifying subjective symptoms, mapping multi-factorial mechanisms, accounting for individual variation, enabling invasive sensing, overcoming research/funding insularity, and more are discussed. Part II explores how emerging artificial intelligence and machine and deep learning techniques can help address limitations through pattern recognition of complex physiological signatures as more objective biomarkers, predictive modeling to capture individual differences, consolidation of disjointed findings via data mining, and simulation to explore interventions. Conversational agents like Claude and ChatGPT also have potential to accelerate human fatigue research, but they currently lack capacities for robust autonomous contributions. Envisioned is an innovation timeline where synergistic application of enhanced neuroimaging, biosensors, closed-loop systems, and other advances combined with AI analytics could catalyze transformative progress in elucidating fatigue neural circuitry and treating associated conditions over the coming decades. Full article
(This article belongs to the Special Issue The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment)

Other

Jump to: Research, Review

14 pages, 874 KB  
Case Report
Robotic-Assisted Gait Training Combined with Multimodal Rehabilitation for Functional Recovery in Acute Dermatomyositis: A Case Report
by Wilmer Esparza, Rebeca Benalcazar-Aguilar, Gabriela Moreno-Andrade and Israel Vinueza-Fernández
Brain Sci. 2025, 15(6), 650; https://doi.org/10.3390/brainsci15060650 - 17 Jun 2025
Viewed by 1564
Abstract
This case report examines the impact of robotic-assisted therapy (Lokomat) on functional recovery in a 28-year-old male patient with acute dermatomyositis (DM), an autoimmune inflammatory myopathy causing progressive muscle weakness and disability. The patient underwent 21 sessions of robotic therapy combined with physical [...] Read more.
This case report examines the impact of robotic-assisted therapy (Lokomat) on functional recovery in a 28-year-old male patient with acute dermatomyositis (DM), an autoimmune inflammatory myopathy causing progressive muscle weakness and disability. The patient underwent 21 sessions of robotic therapy combined with physical therapy, and occupational therapy over seven weeks. Assessments were conducted at baseline, week 10, and week 21 using standardized measures for balance, muscle strength, and functionality. Results demonstrated significant improvements across all domains: balance scores progressed from severe impairment (4/56 Berg, 0/28 Tinetti) to near-normal function (55/56, 24/28, respectively); muscle strength increased from grade 1/5 to 4/5 (MMT-8) in all tested muscle groups; and functionality improved from moderate dependence (59/126 FIM) to complete independence (126/126). The trunk functionality scores showed remarkable recovery from 12/100 to 100/100 (TCT), indicating restored trunk control. Lokomat-assisted therapy combined with conventional rehabilitation effectively improves proximal weakness and postural instability in DM. Robotic therapy enhances motor learning via repetitive movements and reduces therapist workload. Though limited by a single-case design, this study offers preliminary evidence for robotic rehabilitation in DM, previously unexplored. Controlled studies are needed to standardize protocols and validate results in larger cohorts. Advanced technologies show promise for functional recovery in inflammatory myopathies. Full article
(This article belongs to the Special Issue The Use of Robotics and Artificial Intelligence in Neurorehabilitation: From Diagnosis to Treatment)
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