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Hybrid brain-computer interfacing paradigm for assistive robotics

ROBOTICS AND AUTONOMOUS SYSTEMS 2025年 185卷

作者： Ghosh, Ahona Ghosh, Lidia Saha, Sriparna Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata West Bengal India RCC Inst Informat Technol Dept Comp Applicat Kolkata India

Human-computer interfacing, which can be controlled by eye movements and brain signals, is of widespread use in recent times as an intelligent rehabilitation aid. Although some exciting literature exists on robot-assisted physical therapy, enhancing the quality of neurological rehabilitation, most can only focus on a smaller range of control commands required for real-time robot navigation. In this paper, a hybrid brain-computer interfacing system to control a robotic arm has been proposed where an Electrooculography (EOG) and an Electroencephalography (EEG) sensor, respectively, have been used to select the joints of the robot and to control the movement of the selected joint in the required direction. The proposed technique, which applies interquartile range- based data augmentation to extracted time-domain features, can effectively deal with the outliers and prevent overfitting. Afterwards, a novel variant of the Two-Dimensional Convolutional Neural Network is employed for the classification of EOG signals. On the other hand, a Phase sensitive Common Spatial Pattern induced Linear Discriminant Analysis is utilized for classifying the EEG data. The classifiers exhibit satisfactory performance with 98.45 % and 96.61 % accuracy for EOG and EEG, respectively, leading to the implementation of an online robot navigation system in real-time. The system integrates EEG-based signals into the robotic control loop, enabling real-time error detection in the end-effector trajectory of the Robotic arm through Error Related Potential signals and confirming task completion or target attainment via P300 detection. The proposed framework yields an average steady-state error, peak overshoot, and settling time of 0.036, 2.5 %, and 30 s, respectively. Moreover, the average target reaching rate is 95 %, making it a suitable choice for real-time rehabilitative platforms in prosthetics design.

关键词： brain-computer interfacing Electroencephalography Electrooculography Error related potential OpenManipulatorX P300 signal Phase-sensitive common spatial pattern Two-dimensional convolutional neural network

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A novel approach of brain-computer interfacing (BCI) and Grad-CAM based explainable artificial intelligence: Use case scenario for smart healthcare

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JOURNAL OF NEUROSCIENCE METHODS 2024年 408卷 110159页

作者： Lamba, Kamini Rani, Shalli Chitkara Univ Inst Engn & Technol Rajpura 140401 Punjab India

Background: In order to push the frontiers of brain-computer interface (BCI) and neuron-electronics, this research presents a novel framework that combines cutting-edge technologies for improved brain-related diagnostics in smart healthcare. This research offers a ground-breaking application of transparent strategies to BCI, promoting openness and confidence in brain-computer interactions and taking inspiration from Grad-CAM (Gradientweighted Class Activation Mapping) based Explainable Artificial Intelligence (XAI) methodology. The landscape of healthcare diagnostics is about to be redefined by the integration of various technologies, especially when it comes to illnesses related to the brain. New method: A novel approach has been proposed in this study comprising of Xception architecture which is trained on imagenet database following transfer learning process for extraction of significant features from magnetic resonance imaging dataset acquired from publicly available distinct sources as an input and linear support vector machine has been used for distinguishing distinct ***, gradient-weighted class activation mapping has been deployed as the foundation for explainable artificial intelligence (XAI) for generating informative heatmaps, representing spatial localization of features which were focused to achieve model's predictions. Results: Thus, the proposed model not only provides accurate outcomes but also provides transparency for the predictions generated by the Xception network to diagnose presence of abnormal tissues and avoids overfitting issues. Hyperparameters along with performance-metrics are also obtained while validating the proposed network on unseen brain MRI scans to ensure effectiveness of the proposed network. Comparison with existing methods and conclusions: The integration of Grad-CAM based explainable artificial intelligence with deep neural network namely Xception offers a significant impact in diagnosing brain tumor disease wh

关键词： brain tumor Health Automated diagnosis Transparency Explainable Artificial Intelligence Deep Neural Network brain-computer interfacing

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brain-computer interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

BIOSENSORS-BASEL

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BIOSENSORS-BASEL 2021年第10期11卷 389页

作者： Paulmurugan, Kogulan Vijayaragavan, Vimalan Ghosh, Sayantan Padmanabhan, Parasuraman Gulyas, Balazs Nanyang Technol Univ Cognit Neuroimaging Ctr 59 Nanyang Dr Singapore 636921 Singapore Vellore Inst Technol Dept Integrat Biol Vellore 632014 Tamil Nadu India Nanyang Technol Univ Imaging Probe Dev Platform 59 Nanyang Dr Singapore 636921 Singapore Karolinska Inst Dept Clin Neurosci S-17176 Stockholm Sweden

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain-computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain-computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

关键词： functional near-infrared spectroscopy (fNIRS) non-invasive monitoring brain function neuron function blood oxygen concentration cognitive function brain-computer interfacing current advancement

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Augmenting Motor Imagery Learning for brain-computer interfacing Using Electrical Stimulation as Feedback

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2019年第4期1卷 247-255页

作者： Bhattacharyya, Saugat Clerc, Maureen Hayashibe, Mitsuhiro Univ Essex BCI NE Lab Colchester CO4 3SQ Essex England Inria Sophia Antipolis Mediterranee Athena Project Team F-06902 Valbonne France Tohoku Univ Dept Robot Sendai Miyagi 9808579 Japan Tohoku Univ Grad Sch Engn Dept Biomed Engn Sendai Miyagi 9808579 Japan

brain-computer Interfaces (BCI) and Functional electrical stimulation (FES) contribute significantly to induce cortical learning and to elicit peripheral neuronal activation processes and thus, are highly effective to promote motor recovery. This study aims at understanding the effect of FES as a neural feedback and its influence on the learning process for motor imagery tasks while comparing its performance with a classical visual feedback protocol. The participants were randomly separated into two groups: one group was provided with visual feedback (VIS) while the other received electrical stimulation (FES) as feedback. Both groups performed various motor imagery tasks while feedback was provided in form of a bidirectional bar for VIS group and targeted electrical stimulation on the upper and lower limbs for FES group. The results shown in this paper suggest that the FES based feedback is more intuitive to the participants, hence, the superior results as compared to the visual feedback. The results suggest that the convergence of BCI with FES modality could improve the learning of the patients both in terms of accuracy and speed and provide a practical solution to the BCI learning process in rehabilitation.

关键词： brain-computer interfacing common spatial patterns functional electrical stimulation motor imagery feedback

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Electrophysiological correlates of neurodegeneration in motor and non-motor brain regions in amyotrophic lateral sclerosis-implications for brain-computer interfacing

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JOURNAL OF NEURAL ENGINEERING 2018年第4期15卷 041003-041003页

作者： Kellmeyer, Philipp Grosse-Wentrup, Moritz Schulze-Bonhage, Andreas Ziemann, Ulf Ball, Tonio Univ Freiburg Med Ctr Dept Neurosurg Translat Neurotechnol Lab Freiburg Germany Univ Freiburg Cluster Excellence BrainLinks BrainTools Freiburg Germany Max Planck Inst Intelligent Syst Empir Inference Dept Tubingen Germany Ludwig Maximilians Univ Munchen Dept Stat Munich Germany Univ Freiburg Med Ctr Dept Neurosurg Epilepsy Ctr Freiburg Germany Univ Tubingen Dept Neurol & Stroke Tubingen Germany Univ Tubingen Hertie Inst Clin Brain Res Tubingen Germany Univ Freiburg Med Ctr Epilepsy Ctr Dept NeurosurgTranslat Neurotechnol Lab AG Ball Engelbergerstr 21 D-79106 Freiburg Germany

Objective. For patients with amyotrophic lateral sclerosis (ALS) who are suffering from severe communication or motor problems, brain-computer interfaces (BCIs) can improve the quality of life and patient autonomy. However, current BCI systems are not as widely used as their potential and patient demand would let assume. This underutilization is a result of technological as well as user-based limitations but also of the comparatively poor performance of currently existing BCIs in patients with late-stage ALS, particularly in the locked-in state. Approach. Here we review a broad range of electrophysiological studies in ALS patients with the aim to identify electrophysiological correlates of ALS-related neurodegeneration in motor and non-motor brain regions in to better understand potential neurophysiological limitations of current BCI systems for ALS patients. To this end we analyze studies in ALS patients that investigated basic sensory evoked potentials, resting-state and task-based paradigms using electroencephalography or electrocorticography for basic research purposes as well as for brain-computer interfacing. Main results and significance. Our review underscores that, similarly to mounting evidence from neuroimaging and neuropathology, electrophysiological measures too indicate neurodegeneration in non-motor areas in ALS. Furthermore, we identify an unexpected gap of basic and advanced electrophysiological studies in late-stage ALS patients, particularly in the locked-in state. We propose a research strategy on how to fill this gap in order to improve the design and performance of future BCI systems for this patient group.

关键词： electroencephalography electrocorticography brain-computer interfacing event-related potentials neurodegeneration evoked potentials amyotrophic lateral sclerosis

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Using real-time fMRI brain-computer interfacing to treat eating disorders

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JOURNAL OF THE NEUROLOGICAL SCIENCES 2018年第0期388卷 109-114页

作者： Sokunbi, Moses O. De Montfort Univ Fac Hlth & Life Sci Sch Allied Hlth Sci Room 2-25dHawthorn Bldg Leicester LE1 9BH Leics England

Real-time functional magnetic resonance imaging based brain-computer interfacing (fMRI neurofeedback) has shown encouraging outcomes in the treatment of psychiatric and behavioural disorders. However, its use in the treatment of eating disorders is very limited. Here, we give a brief overview of how to design and implement fMRI neurofeedback intervention for the treatment of eating disorders, considering the basic and essential components. We also attempt to develop potential adaptations of fMRI neurofeedback intervention for the treatment of anorexia nervosa, bulimia nervosa and binge eating disorder.

关键词： Anorexia nervosa Binge eating disorder brain-computer interfacing Bulimia nervosa Eating disorders Neurofeedback Real-time fMRI Self-regulation

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Decoding implicit information from the electroencephalogram with methods from brain-computer interfacing

Decoding implicit information from the electroencephalogram ...

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作者： Markus Wenzel Technical University Berlin

学位级别：博士

Background. Research on brain-computer interfacing (BCI) has demonstrated that specific brain activity patterns can be detected in the electroencephalogram (EEG) with multivariate methods from machine learning and signal processing in real-time. Objective. This direct access to the neural processes potentially provides an opportunity to learn about the users of technical applications in a novel way. In this dissertation, it was explored how implicit, user-related information can be decoded from the EEG. Approach and results. First, it was demonstrated that BCI methods can uncover an imperceptible usability flaw of a technical device. A neural workload, which is potentially uncomfortable on the long-term, was imposed by the device on the brain of the user, but could not be noticed by test persons due to the limits of human perception. The findings suggest a remedy, which may improve the ease of use of the assessed device. In a second line of research, it was investigated how the subjective relevance of viewed items can be estimated based on EEG and eye tracking signals. This information renders it possible to map the relevance of the visual surrounding, and to infer the current interest of the individual user in real-time. Significance. Signals originating in the brain contain valuable information about the users of technical devices and applications. It was demonstrated that the direct observation of the neural processes offers particular benefits in comparison to standard methods for obtaining user-related information, such as questionnaires. Multivariate methods proved to be essential for extracting information about the complex neural activity from the recorded signals. The methods recognised patterns that were distributed over the numerous dimensions of the EEG data and obscured by irrelevant activity and noise.

关键词： brain-computer interfacing electroencephalography eye tracking neuroergonomics neurotechnology pattern recognition relevance detection

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On robust parameter estimation in brain-computer interfacing

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JOURNAL OF NEURAL ENGINEERING 2017年第6期14卷 061001-061001页

作者： Samek, Wojciech Nakajima, Shinichi Kawanabe, Motoaki Mueller, Klaus-Robert Fraunhofer Heinrich Hertz Inst Machine Learning Grp D-10587 Berlin Germany Tech Univ Berlin Machine Learning Grp D-10587 Berlin Germany Adv Telecommun Res Inst Int Kyoto 6190288 Japan Korea Univ Dept Brain & Cognit Engn Seoul 136713 South Korea Max Planck Inst Informat D-66123 Saarbrucken Germany

Objective. The reliable estimation of parameters such as mean or covariance matrix from noisy and high-dimensional observations is a prerequisite for successful application of signal processing and machine learning algorithms in brain-computer interfacing (BCI). This challenging task becomes significantly more difficult if the data set contains outliers, e.g. due to subject movements, eye blinks or loose electrodes, as they may heavily bias the estimation and the subsequent statistical analysis. Although various robust estimators have been developed to tackle the outlier problem, they ignore important structural information in the data and thus may not be optimal. Typical structural elements in BCI data are the trials consisting of a few hundred EEG samples and indicating the start and end of a task. Approach. This work discusses the parameter estimation problem in BCI and introduces a novel hierarchical view on robustness which naturally comprises different types of outlierness occurring in structured data. Furthermore, the class of minimum divergence estimators is reviewed and a robust mean and covariance estimator for structured data is derived and evaluated with simulations and on a benchmark data set. Main results. The results show that state-of-the-art BCI algorithms benefit from robustly estimated parameters. Significance. Since parameter estimation is an integral part of various machine learning algorithms, the presented techniques are applicable to many problems beyond BCI.

关键词： brain-computer interfacing parameter estimation common spatial patterns robustness

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Hacking the brain: brain-computer interfacing technology and the ethics of neurosecurity

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ETHICS AND INFORMATION TECHNOLOGY 2016年第2期18卷 117-129页

作者： Ienca, Marcello Haselager, Pim Univ Basel Inst Biomed Eth Bernoullistr 28 CH-4056 Basel Switzerland Radboud Univ Nijmegen Donders Inst Brain Cognit & Behav B-01-13 Spinoza BldgMontessorilaan 3 NL-6525 HR Nijmegen Netherlands

brain-computer interfacing technologies are used as assistive technologies for patients as well as healthy subjects to control devices solely by brain activity. Yet the risks associated with the misuse of these technologies remain largely unexplored. Recent findings have shown that BCIs are potentially vulnerable to cybercriminality. This opens the prospect of "neurocrime": extending the range of computer-crime to neural devices. This paper explores a type of neurocrime that we call brain-hacking as it aims at the illicit access to and manipulation of neural information and computation. As neural computation underlies cognition, behavior and our self-determination as persons, a careful analysis of the emerging risks of malicious brain-hacking is paramount, and ethical safeguards against these risks should be considered early in design and regulation. This contribution is aimed at raising awareness of the emerging risk of malicious brain-hacking and takes a first step in developing an ethical and legal reflection on those risks.

关键词： brain-computer interfacing Neurosecurity Privacy Neurocrime brain-hacking Autonomy Agency

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brain-computer interfacing under distraction: an evaluation study

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JOURNAL OF NEURAL ENGINEERING 2016年第5期13卷 056012-056012页

作者： Brandl, Stephanie Frolich, Laura Hoehne, Johannes Mueller, Klaus-Robert Samek, Wojciech Berlin Inst Technol Marchstr 23 D-10587 Berlin Germany Tech Univ Denmark DK-2800 Lyngby Denmark Korea Univ Dept Brain & Cognit Engn Seoul 136713 South Korea Fraunhofer Heinrich Hertz Inst Einsteinufer 37 D-10587 Berlin Germany

Objective. While motor-imagery based brain-computer interfaces (BCIs) have been studied over many years by now, most of these studies have taken place in controlled lab settings. Bringing BCI technology into everyday life is still one of the main challenges in this field of research. Approach. This paper systematically investigates BCI performance under 6 types of distractions that mimic out-of-lab environments. Main results. We report results of 16 participants and show that the performance of the standard common spatial patterns (CSP) + regularized linear discriminant analysis classification pipeline drops significantly in this 'simulated' out-of-lab setting. We then investigate three methods for improving the performance: (1) artifact removal, (2) ensemble classification, and (3) a 2-step classification approach. While artifact removal does not enhance the BCI performance significantly, both ensemble classification and the 2-step classification combined with CSP significantly improve the performance compared to the standard procedure. Significance. Systematically analyzing out-of-lab scenarios is crucial when bringing BCI into everyday life. Algorithms must be adapted to overcome nonstationary environments in order to tackle real-world challenges.

关键词： brain-computer interfacing common spatial patterns nonstationarity robustness

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