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Brain Sciences
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Brain-Computer Interfaces and Their Applications in Rehabilitation, Robotics, and Control...
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Brain-Computer Interfaces and Their Applications in Rehabilitation, Robotics, and Control of Human Brain States

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neural Engineering, Neuroergonomics and Neurorobotics".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 10879

Special Issue Editor

Dr. Amin Hekmatmanesh

E-Mail Website
Guest Editor
Mechanical Engineering, LUT School of Energy Systems, LUT University, Lappeenranta, Finland
Interests: biosignal processing; feature extraction; machine learning; brain–computer interface
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Brain–computer Interface (BCI) is a technology that has been introduced to improve the quality of life for people with disabilities or difficulties in their daily lives. BCI applications such as driver assistance, sleep identification for drivers, and controlling a bionic hand/ankle–foot orthosis are widely used for healthy people as well as paralyzed patients. BCI studies are not limited to the EEG signals, indeed other biosignals such as EMG, ECG, and GSR are beneficial in BCI applications. BCI has the potential to be used in many applications based on biosignals. 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 electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG). Mathematical implementations are mainly divided into five main steps: (1) pre-processing, (2) feature extraction, (3) feature selection, (4) classification, and (5) statistical analysis.

Some challenges in the field are related to the identification of patterns generated in EEG signals due to motion intention or motion imagination, called event-related synchronization and desynchronization (ERD/ERS). Depending on the BCI tasks, other patterns are generated in EEG signals that deserve attention, such as readiness potentials, steady-state visual evoked potentials, P300s, and generated local evoked potential patterns. Some of the most well-known mathematical formulas and techniques for detecting EEG patterns are wavelets, common spatial patterns, and nonlinear calculations such as chaotic features (entropy, Lyapunov exponent, fractal dimensions, and recurrence graph). In addition, it is necessary to use handcrafted features to increase the efficiency of the algorithms. Another challenge is linked to the development of classifiers to automate the procedures, such as support vector machines, deep learning, and neural networks.

BCI and rehabilitation projects have different limitations. This research Topic focuses on mathematical solutions based on signal denoising (filtering), feature extraction, and machine learning algorithms. This collection of articles aims to highlight mathematical innovations as well as new 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:

  • 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 automatically
  • Analysing the patterns generated in a designed task to find out which method is more beneficial, for example, wavelet, chaotic methods, common spatial patterns, or reinforcing methods.
  • The development of classifiers to automate the 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. 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

  • brain computer interface
  • biosignal processing
  • rehabilitation models
  • machine learning algorithms
  • feature computations

Published Papers (6 papers)

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Research

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22 pages, 1972 KiB  
Article
Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain–Computer Interface for Decoding Imagined Syllables
by Shizhe Wu, Kinkini Bhadra, Anne-Lise Giraud and Silvia Marchesotti
Brain Sci. 2024, 14(3), 196; https://doi.org/10.3390/brainsci14030196 - 21 Feb 2024
Viewed by 1280
Abstract
Brain-Computer Interfaces (BCIs) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost [...] Read more.
Brain-Computer Interfaces (BCIs) aim to establish a pathway between the brain and an external device without the involvement of the motor system, relying exclusively on neural signals. Such systems have the potential to provide a means of communication for patients who have lost the ability to speak due to a neurological disorder. Traditional methodologies for decoding imagined speech directly from brain signals often deploy static classifiers, that is, decoders that are computed once at the beginning of the experiment and remain unchanged throughout the BCI use. However, this approach might be inadequate to effectively handle the non-stationary nature of electroencephalography (EEG) signals and the learning that accompanies BCI use, as parameters are expected to change, and all the more in a real-time setting. To address this limitation, we developed an adaptive classifier that updates its parameters based on the incoming data in real time. We first identified optimal parameters (the update coefficient, UC) to be used in an adaptive Linear Discriminant Analysis (LDA) classifier, using a previously recorded EEG dataset, acquired while healthy participants controlled a binary BCI based on imagined syllable decoding. We subsequently tested the effectiveness of this optimization in a real-time BCI control setting. Twenty healthy participants performed two BCI control sessions based on the imagery of two syllables, using a static LDA and an adaptive LDA classifier, in randomized order. As hypothesized, the adaptive classifier led to better performances than the static one in this real-time BCI control task. Furthermore, the optimal parameters for the adaptive classifier were closely aligned in both datasets, acquired using the same syllable imagery task. These findings highlight the effectiveness and reliability of adaptive LDA classifiers for real-time imagined speech decoding. Such an improvement can shorten the training time and favor the development of multi-class BCIs, representing a clear interest for non-invasive systems notably characterized by low decoding accuracies. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces and Their Applications in Rehabilitation, Robotics, and Control of Human Brain States)
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15 pages, 3971 KiB  
Article
Real-Time Classification of Motor Imagery Using Dynamic Window-Level Granger Causality Analysis of fMRI Data
by Tianyuan Liu, Bao Li, Chi Zhang, Panpan Chen, Weichen Zhao and Bin Yan
Brain Sci. 2023, 13(10), 1406; https://doi.org/10.3390/brainsci13101406 - 1 Oct 2023
Viewed by 1111
Abstract
This article presents a method for extracting neural signal features to identify the imagination of left- and right-hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery (MI) and the [...] Read more.
This article presents a method for extracting neural signal features to identify the imagination of left- and right-hand grasping movements. A functional magnetic resonance imaging (fMRI) experiment is employed to identify four brain regions with significant activations during motor imagery (MI) and the effective connections between these regions of interest (ROIs) were calculated using Dynamic Window-level Granger Causality (DWGC). Then, a real-time fMRI (rt-fMRI) classification system for left- and right-hand MI is developed using the Open-NFT platform. We conducted data acquisition and processing on three subjects, and all of whom were recruited from a local college. As a result, the maximum accuracy of using Support Vector Machine (SVM) classifier on real-time three-class classification (rest, left hand, and right hand) with effective connections is 69.3%. And it is 3% higher than that of traditional multivoxel pattern classification analysis on average. Moreover, it significantly improves classification accuracy during the initial stage of MI tasks while reducing the latency effects in real-time decoding. The study suggests that the effective connections obtained through the DWGC method serve as valuable features for real-time decoding of MI using fMRI. Moreover, they exhibit higher sensitivity to changes in brain states. This research offers theoretical support and technical guidance for extracting neural signal features in the context of fMRI-based studies. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces and Their Applications in Rehabilitation, Robotics, and Control of Human Brain States)
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18 pages, 2918 KiB  
Article
Tensor Decomposition Analysis of Longitudinal EEG Signals Reveals Differential Oscillatory Dynamics in Eyes-Closed and Eyes-Open Motor Imagery BCI: A Case Report
by Saman Seifpour and Alexander Šatka
Brain Sci. 2023, 13(7), 1013; https://doi.org/10.3390/brainsci13071013 - 30 Jun 2023
Viewed by 1351
Abstract
Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery [...] Read more.
Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery brain-computer interface (MI BCI), we measured neural activity over the motor regions with electroencephalography (EEG) in a stroke survivor during his longitudinal rehabilitation training. We investigated lateralized oscillatory sensorimotor rhythm modulations while the patient imagined moving his hemiplegic hand with closed and open eyes to control an external robotic splint. In order to precisely identify the main profiles of neural activation affected by MI with eyes-open (MIEO) and eyes-closed (MIEC), a data-driven approach based on parallel factor analysis (PARAFAC) tensor decomposition was employed. Using the proposed framework, a set of narrow-band, subject-specific sensorimotor rhythms was identified; each of them had its own spatial and time signature. When MIEC trials were compared with MIEO trials, three key narrow-band rhythms whose peak frequencies centred at ∼8.0 Hz, ∼11.5 Hz, and ∼15.5 Hz, were identified with differently modulated oscillatory dynamics during movement preparation, initiation, and completion time frames. Furthermore, we observed that lower and higher sensorimotor oscillations represent different functional mechanisms within the MI paradigm, reinforcing the hypothesis that rhythmic activity in the human sensorimotor system is dissociated. Leveraging PARAFAC, this study achieves remarkable precision in estimating latent sensorimotor neural substrates, aiding the investigation of the specific functional mechanisms involved in the MI process. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces and Their Applications in Rehabilitation, Robotics, and Control of Human Brain States)
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18 pages, 3156 KiB  
Article
FB-CCNN: A Filter Bank Complex Spectrum Convolutional Neural Network with Artificial Gradient Descent Optimization
by Dongcen Xu, Fengzhen Tang, Yiping Li, Qifeng Zhang and Xisheng Feng
Brain Sci. 2023, 13(5), 780; https://doi.org/10.3390/brainsci13050780 - 10 May 2023
Viewed by 1466
Abstract
The brain–computer interface (BCI) provides direct communication between human brains and machines, including robots, drones and wheelchairs, without the involvement of peripheral systems. BCI based on electroencephalography (EEG) has been applied in many fields, including aiding people with physical disabilities, rehabilitation, education and [...] Read more.
The brain–computer interface (BCI) provides direct communication between human brains and machines, including robots, drones and wheelchairs, without the involvement of peripheral systems. BCI based on electroencephalography (EEG) has been applied in many fields, including aiding people with physical disabilities, rehabilitation, education and entertainment. Among the different EEG-based BCI paradigms, steady-state visual evoked potential (SSVEP)-based BCIs are known for their lower training requirements, high classification accuracy and high information transfer rate (ITR). In this article, a filter bank complex spectrum convolutional neural network (FB-CCNN) was proposed, and it achieved leading classification accuracies of 94.85 ± 6.18% and 80.58 ± 14.43%, respectively, on two open SSVEP datasets. An optimization algorithm named artificial gradient descent (AGD) was also proposed to generate and optimize the hyperparameters of the FB-CCNN. AGD also revealed correlations between different hyperparameters and their corresponding performances. It was experimentally demonstrated that FB-CCNN performed better when the hyperparameters were fixed values rather than channel number-based. In conclusion, a deep learning model named FB-CCNN and a hyperparameter-optimizing algorithm named AGD were proposed and demonstrated to be effective in classifying SSVEP through experiments. The hyperparameter design process and analysis were carried out using AGD, and advice on choosing hyperparameters for deep learning models in classifying SSVEP was provided. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces and Their Applications in Rehabilitation, Robotics, and Control of Human Brain States)
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Review

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23 pages, 1476 KiB  
Review
An Analysis of Deep Learning Models in SSVEP-Based BCI: A Survey
by Dongcen Xu, Fengzhen Tang, Yiping Li, Qifeng Zhang and Xisheng Feng
Brain Sci. 2023, 13(3), 483; https://doi.org/10.3390/brainsci13030483 - 13 Mar 2023
Cited by 7 | Viewed by 3632
Abstract
The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have [...] Read more.
The brain–computer interface (BCI), which provides a new way for humans to directly communicate with robots without the involvement of the peripheral nervous system, has recently attracted much attention. Among all the BCI paradigms, BCIs based on steady-state visual evoked potentials (SSVEPs) have the highest information transfer rate (ITR) and the shortest training time. Meanwhile, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, and many researchers have started to apply deep learning to classify SSVEP signals. However, the designs of deep learning models vary drastically. There are many hyper-parameters that influence the performance of the model in an unpredictable way. This study surveyed 31 deep learning models (2011–2023) that were used to classify SSVEP signals and analyzed their design aspects including model input, model structure, performance measure, etc. Most of the studies that were surveyed in this paper were published in 2021 and 2022. This survey is an up-to-date design guide for researchers who are interested in using deep learning models to classify SSVEP signals. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces and Their Applications in Rehabilitation, Robotics, and Control of Human Brain States)
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Other

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19 pages, 1774 KiB  
Systematic Review
A Review on Motor Imagery with Transcranial Alternating Current Stimulation: Bridging Motor and Cognitive Welfare for Patient Rehabilitation
by Rosary Yuting Lim, Kai Keng Ang, Effie Chew and Cuntai Guan
Brain Sci. 2023, 13(11), 1584; https://doi.org/10.3390/brainsci13111584 - 12 Nov 2023
Viewed by 1134
Abstract
Research has shown the effectiveness of motor imagery in patient motor rehabilitation. Transcranial electrical stimulation has also demonstrated to improve patient motor and non-motor performance. However, mixed findings from motor imagery studies that involved transcranial electrical stimulation suggest that current experimental protocols can [...] Read more.
Research has shown the effectiveness of motor imagery in patient motor rehabilitation. Transcranial electrical stimulation has also demonstrated to improve patient motor and non-motor performance. However, mixed findings from motor imagery studies that involved transcranial electrical stimulation suggest that current experimental protocols can be further improved towards a unified design for consistent and effective results. This paper aims to review, with some clinical and neuroscientific findings from literature as support, studies of motor imagery coupled with different types of transcranial electrical stimulation and their experiments onhealthy and patient subjects. This review also includes the cognitive domains of working memory, attention, and fatigue, which are important for designing consistent and effective therapy protocols. Finally, we propose a theoretical all-inclusive framework that synergizes the three cognitive domains with motor imagery and transcranial electrical stimulation for patient rehabilitation, which holds promise of benefiting patients suffering from neuromuscular and cognitive disorders. Full article
(This article belongs to the Special Issue Brain-Computer Interfaces and Their Applications in Rehabilitation, Robotics, and Control of Human Brain States)
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