Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.8
2.7
Journals
Brain Sciences
Special Issues
The Application of EEG in Neurorehabilitation
share announcement

The Application of EEG in Neurorehabilitation

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Computational Neuroscience and Neuroinformatics".

Deadline for manuscript submissions: 19 May 2025 | Viewed by 7035

Special Issue Editor

Prof. Dr. Ryouhei Ishii

E-Mail Website
Guest Editor
Occupational Major, Graduate School of Rehabilitation Science, Osaka Metropolitan University, Osaka, Japan
Interests: transcranial magnetic stimulation (TMS); electroencephalography (EEG)

Special Issue Information

Dear Colleagues,

The integration of electroencephalography (EEG) into neurorehabilitation practices has emerged as a promising avenue for enhancing our understanding of brain function and facilitating recovery following neurological injury or disease. This special topic edition of Brain Sciences explores the diverse applications of EEG in neurorehabilitation, exploring its role in assessing neural plasticity, guiding personalized treatment strategies, monitoring therapeutic interventions and developing EEG-based brain–computer interfaces. Through a collection of scholarly articles, this volume aims to illuminate the latest advancements, challenges and future directions in the use of EEG technologies to optimize rehabilitation outcomes and promote neurological recovery.

Prof. Dr. Ryouhei Ishii
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. 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

  • electroencephalography (EEG)
  • neurorehabilitation
  • neural plasticity
  • personalized treat-ment strategies
  • brain–computer interfaces

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 4828 KiB  
Article
Effects of Different Individuals and Verbal Tones on Neural Networks in the Brain of Children with Cerebral Palsy
by Ryosuke Yamauchi, Hiroki Ito, Ken Kitai, Kohei Okuyama, Osamu Katayama, Kiichiro Morita, Shin Murata and Takayuki Kodama
Brain Sci. 2025, 15(4), 397; https://doi.org/10.3390/brainsci15040397 - 15 Apr 2025
Viewed by 217
Abstract
Background/Objectives: Motivation is a key factor for improving motor function and cognitive control in patients. Motivation for rehabilitation is influenced by the relationship between the therapist and patient, wherein appropriate voice encouragement is necessary to increase motivation. Therefore, we examined the differences [...] Read more.
Background/Objectives: Motivation is a key factor for improving motor function and cognitive control in patients. Motivation for rehabilitation is influenced by the relationship between the therapist and patient, wherein appropriate voice encouragement is necessary to increase motivation. Therefore, we examined the differences between mothers and other individuals, such as physical therapists (PTs), in their verbal interactions with children with cerebral palsy who have poor communication abilities, as well as the neurological and physiological effects of variations in the tone of their speech. Methods: The three participants were children with cerebral palsy (Participant A: boy, 3 years; Participant B: girl, 7 years; Participant C: girl, 9 years). Participants’ mothers and the assigned PTs were asked to speak under three conditions. During this, the brain activity of the participants was measured using a 19-channel electroencephalogram. The results were further analyzed using Independent Component Analysis frequency analysis with exact Low-Resolution Brain Electromagnetic Tomography, allowing for the identification and visualization of neural activity in three-dimensional brain functional networks. Results: The results of the ICA frequency analysis for each participant revealed distinct patterns of brain activity in response to verbal encouragement from the mother and PT, with differences observed across the theta, alpha, and beta frequency bands. Conclusions: Our study suggests that the children were attentive to their mothers’ inquiries and focused on their internal experiences. Furthermore, it was indicated that when addressed by the PT, the participants found it easier to grasp the meanings and intentions of the words. Full article
(This article belongs to the Special Issue The Application of EEG in Neurorehabilitation)
►▼ Show Figures

Figure 1

21 pages, 4830 KiB  
Article
Neural Oscillatory Mechanisms Underlying Step Accuracy: Integrating Microstate Segmentation with eLORETA-Independent Component Analysis
by Kohei Okuyama, Kota Maeda, Ryosuke Yamauchi, Daichi Harada and Takayuki Kodama
Brain Sci. 2025, 15(4), 356; https://doi.org/10.3390/brainsci15040356 - 29 Mar 2025
Viewed by 205
Abstract
Background/Objectives: Precise stepping control is fundamental to human mobility, and impairments increase fall risk in older adults and individuals with neurological conditions. This study investigated the cortical networks underlying stepping accuracy using mobile brain/body imaging with electroencephalography (EEG)-based exact low-resolution electromagnetic tomography-independent component [...] Read more.
Background/Objectives: Precise stepping control is fundamental to human mobility, and impairments increase fall risk in older adults and individuals with neurological conditions. This study investigated the cortical networks underlying stepping accuracy using mobile brain/body imaging with electroencephalography (EEG)-based exact low-resolution electromagnetic tomography-independent component analysis (eLORETA-ICA) and microstate segmentation analysis (MSA). Methods: Sixteen healthy male participants performed a precision stepping task while wearing a mobile EEG system. Step performance was quantified using error distance, measuring deviation between target and heel contact points. Preprocessed EEG data were analyzed using eLORETA-ICA and MSA, with participants categorized into high- and low-performing groups. Results: Seven microstate clusters were identified, with the anterior cingulate cortex (ACC) showing the highest microstate probability (21.15%). The high-performing group exhibited amplified theta-band activity in the ACC, enhanced activity in the precuneus and postcentral gyrus, and suppressed mu- and beta-band activity in the paracentral lobules. Conclusions: Stepping accuracy relies on a distributed neural network, with the ACC playing a central role in performance monitoring. We propose an integrated framework comprising the following systems: error monitoring (ACC), sensorimotor integration (paracentral lobules), and visuospatial processing (precuneus and occipital regions). These findings highlight the importance of neural oscillatory mechanisms in precise motor control and offer insights for rehabilitation strategies and fall prevention programs. Full article
(This article belongs to the Special Issue The Application of EEG in Neurorehabilitation)
►▼ Show Figures

Figure 1

20 pages, 3795 KiB  
Article
Exploring Machine Learning Classification of Movement Phases in Hemiparetic Stroke Patients: A Controlled EEG-tDCS Study
by Rishishankar E. Suresh, M S Zobaer, Matthew J. Triano, Brian F. Saway, Parneet Grewal and Nathan C. Rowland
Brain Sci. 2025, 15(1), 28; https://doi.org/10.3390/brainsci15010028 - 29 Dec 2024
Viewed by 1281
Abstract
Background/Objectives: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying [...] Read more.
Background/Objectives: Noninvasive brain stimulation (NIBS) can boost motor recovery after a stroke. Certain movement phases are more responsive to NIBS, so a system that auto-detects these phases would optimize stimulation timing. This study assessed the effectiveness of various machine learning models in identifying movement phases in hemiparetic individuals undergoing simultaneous NIBS and EEG recordings. We hypothesized that transcranial direct current stimulation (tDCS), a form of NIBS, would enhance EEG signals related to movement phases and improve classification accuracy compared to sham stimulation. Methods: EEG data from 10 chronic stroke patients and 11 healthy controls were recorded before, during, and after tDCS. Eight machine learning algorithms and five ensemble methods were used to classify two movement phases (hold posture and reaching) during each of these periods. Data preprocessing included z-score normalization and frequency band power binning. Results: In chronic stroke participants who received active tDCS, the classification accuracy for hold vs. reach phases increased from pre-stimulation to the late intra-stimulation period (72.2% to 75.2%, p < 0.0001). Late active tDCS surpassed late sham tDCS classification (75.2% vs. 71.5%, p < 0.0001). Linear discriminant analysis was the most accurate (74.6%) algorithm with the shortest training time (0.9 s). Among ensemble methods, low gamma frequency (30–50 Hz) achieved the highest accuracy (74.5%), although this result did not achieve statistical significance for actively stimulated chronic stroke participants. Conclusions: Machine learning algorithms showed enhanced movement phase classification during active tDCS in chronic stroke participants. These results suggest their feasibility for real-time movement detection in neurorehabilitation, including brain–computer interfaces for stroke recovery. Full article
(This article belongs to the Special Issue The Application of EEG in Neurorehabilitation)
►▼ Show Figures

Figure 1

24 pages, 2578 KiB  
Article
Dynamic Neural Network States During Social and Non-Social Cueing in Virtual Reality Working Memory Tasks: A Leading Eigenvector Dynamics Analysis Approach
by Pinar Ozel
Brain Sci. 2025, 15(1), 4; https://doi.org/10.3390/brainsci15010004 - 24 Dec 2024
Viewed by 823
Abstract
Background/Objectives: This research investigates brain connectivity patterns in reaction to social and non-social stimuli within a virtual reality environment, emphasizing their impact on cognitive functions, specifically working memory. Methods: Employing the LEiDA framework with EEG data from 47 participants, I examined dynamic brain [...] Read more.
Background/Objectives: This research investigates brain connectivity patterns in reaction to social and non-social stimuli within a virtual reality environment, emphasizing their impact on cognitive functions, specifically working memory. Methods: Employing the LEiDA framework with EEG data from 47 participants, I examined dynamic brain network states elicited by social avatars compared to non-social stick cues during a VR memory task. Through the integration of LEiDA with deep learning and graph theory analyses, unique connectivity patterns associated with cue type were discerned, underscoring the substantial influence of social cues on cognitive processes. LEiDA, conventionally utilized with fMRI, was creatively employed in EEG to detect swift alterations in brain network states, offering insights into cognitive processing dynamics. Results: The findings indicate distinct neural states for social and non-social cues; notably, social cues correlated with a unique brain state characterized by increased connectivity within self-referential and memory-processing networks, implying greater cognitive engagement. Moreover, deep learning attained approximately 99% accuracy in differentiating cue contexts, highlighting the efficacy of prominent eigenvectors from LEiDA in EEG analysis. Analysis of graph theory also uncovered structural network disparities, signifying enhanced integration in contexts involving social cues. Conclusions: This multi-method approach elucidates the dynamic influence of social cues on brain connectivity and cognition, establishing a basis for VR-based cognitive rehabilitation and immersive learning, wherein social signals may significantly enhance cognitive function. Full article
(This article belongs to the Special Issue The Application of EEG in Neurorehabilitation)
►▼ Show Figures

Figure 1

19 pages, 1342 KiB  
Article
The Modulatory Effects of Transcranial Alternating Current Stimulation on Brain Oscillatory Patterns in the Beta Band in Healthy Older Adults
by Kenya Morales Fajardo, Xuanteng Yan, George Lungoci, Monserrat Casado Sánchez, Georgios D. Mitsis and Marie-Hélène Boudrias
Brain Sci. 2024, 14(12), 1284; https://doi.org/10.3390/brainsci14121284 - 20 Dec 2024
Viewed by 1318
Abstract
Background: In the last few years, transcranial alternating current stimulation (tACS) has attracted attention as a promising approach to interact with ongoing oscillatory cortical activity and, consequently, to enhance cognitive and motor processes. While tACS findings are limited by high variability in young [...] Read more.
Background: In the last few years, transcranial alternating current stimulation (tACS) has attracted attention as a promising approach to interact with ongoing oscillatory cortical activity and, consequently, to enhance cognitive and motor processes. While tACS findings are limited by high variability in young adults’ responses, its effects on brain oscillations in older adults remain largely unexplored. In fact, the modulatory effects of tACS on cortical oscillations in healthy aging participants have not yet been investigated extensively, particularly during movement. This study aimed to examine the after-effects of 20 Hz and 70 Hz High-Definition tACS on beta oscillations both during rest and movement. Methods: We recorded resting state EEG signals and during a handgrip task in 15 healthy older participants. We applied 10 min of 20 Hz HD-tACS, 70 Hz HD-tACS or Sham stimulation for 10 min. We extracted resting-state beta power and movement-related beta desynchronization (MRBD) values to compare between stimulation frequencies and across time. Results: We found that 20 Hz HD-tACS induced a significant reduction in beta power for electrodes C3 and CP3, while 70 Hz did not have any significant effects. With regards to MRBD, 20 Hz HD-tACS led to more negative values, while 70 Hz HD-tACS resulted in more positive ones for electrodes C3 and FC3. Conclusions: These findings suggest that HD-tACS can modulate beta brain oscillations with frequency specificity. They also highlight the focal impact of HD-tACS, which elicits effects on the cortical region situated directly beneath the stimulation electrode. Full article
(This article belongs to the Special Issue The Application of EEG in Neurorehabilitation)
►▼ Show Figures

Figure 1

20 pages, 6888 KiB  
Article
The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems
by András Adolf, Csaba Márton Köllőd, Gergely Márton, Ward Fadel and István Ulbert
Brain Sci. 2024, 14(12), 1272; https://doi.org/10.3390/brainsci14121272 - 18 Dec 2024
Viewed by 1123
Abstract
Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess [...] Read more.
Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures. Full article
(This article belongs to the Special Issue The Application of EEG in Neurorehabilitation)
►▼ Show Figures

Figure 1

13 pages, 253 KiB  
Article
Adaptive Compensatory Neurophysiological Biomarkers of Motor Recovery Post-Stroke: Electroencephalography and Transcranial Magnetic Stimulation Insights from the DEFINE Cohort Study
by Guilherme J. M. Lacerda, Fernanda M. Q. Silva, Kevin Pacheco-Barrios, Linamara Rizzo Battistella and Felipe Fregni
Brain Sci. 2024, 14(12), 1257; https://doi.org/10.3390/brainsci14121257 - 15 Dec 2024
Viewed by 1029
Abstract
Objective: This study aimed to explore longitudinal relationships between neurophysiological biomarkers and upper limb motor function recovery in stroke patients, focusing on electroencephalography (EEG) and transcranial magnetic stimulation (TMS) metrics. Methods: This longitudinal cohort study analyzed neurophysiological, clinical, and demographic data from 102 [...] Read more.
Objective: This study aimed to explore longitudinal relationships between neurophysiological biomarkers and upper limb motor function recovery in stroke patients, focusing on electroencephalography (EEG) and transcranial magnetic stimulation (TMS) metrics. Methods: This longitudinal cohort study analyzed neurophysiological, clinical, and demographic data from 102 stroke patients enrolled in the DEFINE cohort. We investigated the associations between baseline and post-intervention changes in the EEG theta/alpha ratio (TAR) and TMS metrics with upper limb motor functionality, assessed using the outcomes of five tests: the Fugl-Meyer Assessment (FMA), Handgrip Strength Test (HST), Pinch Strength Test (PST), Finger Tapping Test (FTT), and Nine-Hole Peg Test (9HPT). Results: Our multivariate models identified that a higher baseline TAR in the lesioned hemisphere was consistently associated with poorer motor outcomes across all five assessments. Conversely, a higher improvement in the TAR was positively associated with improvements in FMA and 9HPT. Additionally, an increased TMS motor-evoked potential (MEP) amplitude in the non-lesioned hemisphere correlated with greater FMA-diff, while a lower TMS Short Intracortical Inhibition (SICI) in the non-lesioned hemisphere was linked to better PST improvements. These findings suggest the potential of the TAR and TMS metrics as biomarkers for predicting motor recovery in stroke patients. Conclusion: Our findings highlight the significance of the TAR in the lesioned hemisphere as a predictor of motor function recovery post-stroke and also a potential signature for compensatory oscillations. The observed relationships between the TAR and motor improvements, as well as the associations with TMS metrics, underscore the potential of these neurophysiological measures in guiding personalized rehabilitation strategies for stroke patients. Full article
(This article belongs to the Special Issue The Application of EEG in Neurorehabilitation)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop