1
|
Klein F. Optimizing spatial specificity and signal quality in fNIRS: an overview of potential challenges and possible options for improving the reliability of real-time applications. FRONTIERS IN NEUROERGONOMICS 2024; 5:1286586. [PMID: 38903906 PMCID: PMC11188482 DOI: 10.3389/fnrgo.2024.1286586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 04/29/2024] [Indexed: 06/22/2024]
Abstract
The optical brain imaging method functional near-infrared spectroscopy (fNIRS) is a promising tool for real-time applications such as neurofeedback and brain-computer interfaces. Its combination of spatial specificity and mobility makes it particularly attractive for clinical use, both at the bedside and in patients' homes. Despite these advantages, optimizing fNIRS for real-time use requires careful attention to two key aspects: ensuring good spatial specificity and maintaining high signal quality. While fNIRS detects superficial cortical brain regions, consistently and reliably targeting specific regions of interest can be challenging, particularly in studies that require repeated measurements. Variations in cap placement coupled with limited anatomical information may further reduce this accuracy. Furthermore, it is important to maintain good signal quality in real-time contexts to ensure that they reflect the true underlying brain activity. However, fNIRS signals are susceptible to contamination by cerebral and extracerebral systemic noise as well as motion artifacts. Insufficient real-time preprocessing can therefore cause the system to run on noise instead of brain activity. The aim of this review article is to help advance the progress of fNIRS-based real-time applications. It highlights the potential challenges in improving spatial specificity and signal quality, discusses possible options to overcome these challenges, and addresses further considerations relevant to real-time applications. By addressing these topics, the article aims to help improve the planning and execution of future real-time studies, thereby increasing their reliability and repeatability.
Collapse
Affiliation(s)
- Franziska Klein
- Biomedical Devices and Systems Group, R&D Division Health, OFFIS - Institute for Information Technology, Oldenburg, Germany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical School, RWTH Aachen University, Aachen, Germany
- Neurocognition and Functional Neurorehabilitation Group, Department of Psychology, University of Oldenburg, Oldenburg, Germany
| |
Collapse
|
2
|
Wang J, Bi L, Fei W, Xu X, Liu A, Mo L, Genetu Feleke A. Neural Correlate and Movement Decoding of Simultaneous-and-Sequential Bimanual Movements Using EEG Signals. IEEE Trans Neural Syst Rehabil Eng 2024; 32:2087-2095. [PMID: 38805337 DOI: 10.1109/tnsre.2024.3406371] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/30/2024]
Abstract
Bimanual coordination is important for developing a natural motor brain-computer interface (BCI) from electroencephalogram (EEG) signals, covering the aspects of bilateral arm training for rehabilitation, bimanual coordination for daily-life assistance, and also improving the multidimensional control of BCIs. For the same task targets of both hands, simultaneous and sequential bimanual movements are two different bimanual coordination manners. Planning and performing motor sequences are the fundamental abilities of humans, and it is more natural to execute sequential movements compared to simultaneous movements in many complex tasks. However, to date, for these two different manners in which two hands coordinated to reach the same task targets, the differences in the neural correlate and also the feasibility of movement discrimination have not been explored. In this study, we aimed to investigate these two issues based on a bimanual reaching task for the first time. Finally, neural correlates in the view of the movement-related cortical potentials, event-related oscillations, and source imaging showed unique neural encoding patterns of sequential movements. Besides, for the same task targets of both hands, the simultaneous and sequential bimanual movements were successfully discriminated in both pre-movement and movement execution periods. This study revealed the neural encoding patterns of sequential bimanual movements and presented its values in developing a more natural and good-performance motor BCI.
Collapse
|
3
|
Kalani M, Anjankar A. Revolutionizing Neurology: The Role of Artificial Intelligence in Advancing Diagnosis and Treatment. Cureus 2024; 16:e61706. [PMID: 38975469 PMCID: PMC11224934 DOI: 10.7759/cureus.61706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Accepted: 06/04/2024] [Indexed: 07/09/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a powerful tool in the field of neurology, significantly impacting the diagnosis and treatment of neurological disorders. Recent technological breakthroughs have given us access to a plethora of information relevant to many aspects of neurology. Neuroscience and AI share a long history of collaboration. Along with great potential, we encounter obstacles relating to data quality, ethics, and inherent difficulty in applying data science in healthcare. Neurological disorders pose intricate challenges due to their complex manifestations and variability. Automating image interpretation tasks, AI algorithms accurately identify brain structures and detect abnormalities. This accelerates diagnosis and reduces the workload on medical professionals. Treatment optimization benefits from AI simulations that model different scenarios and predict outcomes. These AI systems can currently perform many of the sophisticated perceptual and cognitive capacities of biological systems, such as object identification and decision making. Furthermore, AI is rapidly being used as a tool in neuroscience research, altering our understanding of brain functioning. It has the ability to revolutionize healthcare as we know it into a system in which humans and robots collaborate to deliver better care for our patients. Image analysis activities such as recognizing particular brain regions, calculating changes in brain volume over time, and detecting abnormalities in brain scans can be automated by AI systems. This lessens the strain on radiologists and neurologists while improving diagnostic accuracy and efficiency. It is now obvious that cutting-edge artificial intelligence models combined with high-quality clinical data will lead to enhanced prognostic and diagnostic models in neurological illness, permitting expert-level clinical decision aids across healthcare settings. In conclusion, AI's integration into neurology has revolutionized diagnosis, treatment, and research. As AI technologies advance, they promise to unravel the complexities of neurological disorders further, leading to improved patient care and quality of life. The symbiosis of AI and neurology offers a glimpse into a future where innovation and compassion converge to reshape neurological healthcare. This abstract provides a concise overview of the role of AI in neurology and its transformative potential.
Collapse
Affiliation(s)
- Meetali Kalani
- Biochemistry, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Ashish Anjankar
- Biochemistry, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| |
Collapse
|
4
|
Mondini V, Sburlea AI, Müller-Putz GR. Towards unlocking motor control in spinal cord injured by applying an online EEG-based framework to decode motor intention, trajectory and error processing. Sci Rep 2024; 14:4714. [PMID: 38413782 PMCID: PMC10899181 DOI: 10.1038/s41598-024-55413-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 02/23/2024] [Indexed: 02/29/2024] Open
Abstract
Brain-computer interfaces (BCIs) can translate brain signals directly into commands for external devices. Electroencephalography (EEG)-based BCIs mostly rely on the classification of discrete mental states, leading to unintuitive control. The ERC-funded project "Feel Your Reach" aimed to establish a novel framework based on continuous decoding of hand/arm movement intention, for a more natural and intuitive control. Over the years, we investigated various aspects of natural control, however, the individual components had not yet been integrated. Here, we present a first implementation of the framework in a comprehensive online study, combining (i) goal-directed movement intention, (ii) trajectory decoding, and (iii) error processing in a unique closed-loop control paradigm. Testing involved twelve able-bodied volunteers, performing attempted movements, and one spinal cord injured (SCI) participant. Similar movement-related cortical potentials and error potentials to previous studies were revealed, and the attempted movement trajectories were overall reconstructed. Source analysis confirmed the involvement of sensorimotor and posterior parietal areas for goal-directed movement intention and trajectory decoding. The increased experiment complexity and duration led to a decreased performance than each single BCI. Nevertheless, the study contributes to understanding natural motor control, providing insights for more intuitive strategies for individuals with motor impairments.
Collapse
Affiliation(s)
- Valeria Mondini
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Andreea-Ioana Sburlea
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, The Netherlands
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria.
- BioTechMed, Graz, Austria.
| |
Collapse
|
5
|
Zhu L, Xu M, Zhu J, Huang A, Zhang J. A time segment adaptive optimization method based on separability criterion and correlation analysis for motor imagery BCIs. Comput Methods Biomech Biomed Engin 2024:1-14. [PMID: 38193151 DOI: 10.1080/10255842.2023.2301421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Accepted: 12/24/2023] [Indexed: 01/10/2024]
Abstract
Motor imagery (MI) plays a crucial role in brain-computer interface (BCI), and the classification of MI tasks using electroencephalogram (EEG) is currently under extensive investigation. During MI classification, individual differences among subjects in terms of response and time latency need to be considered. Optimizing the time segment for different subjects can enhance subsequent classification performance. In view of the individual differences of subjects in motor imagery tasks, this article proposes a Time Segment Adaptive Optimization method based on Separability criterion and Correlation analysis (TSAOSC). The fundamental principle of this method involves applying the separability criterion to various sizes of time windows within the training data, identifying the optimal raw reference signal, and adaptively adjusting the time segment position for each trial's data by analyzing its relationship with the optimal reference signal. We evaluated our method on three BCI competition datasets, respectively. The utilization of the TSAOSC method in the experiments resulted in an enhancement of 4.90% in average classification accuracy compared to its absence. Additionally, building upon the TSAOSC approach, this study proposes a Nonlinear-TSAOSC method (N-TSAOSC) for analyzing EEG signals with nonlinearity, which shows improvements in the classification accuracy of certain subjects. The results of the experiments demonstrate that the proposed method is an effective time segment optimization method, and it can be integrated into other algorithms to further improve their accuracy.
Collapse
Affiliation(s)
- Lei Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Mengxuan Xu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jieping Zhu
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Aiai Huang
- School of Automation, Hangzhou Dianzi University, Hangzhou, China
| | - Jianhai Zhang
- School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
- Key Laboratory of Brain Machine Collaborative Intelligence of Zhejiang Province, Hangzhou, China
| |
Collapse
|
6
|
Wirth C, Toth J, Arvaneh M. Bayesian learning from multi-way EEG feedback for robot navigation and target identification. Sci Rep 2023; 13:16925. [PMID: 37805540 PMCID: PMC10560278 DOI: 10.1038/s41598-023-44077-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Accepted: 10/03/2023] [Indexed: 10/09/2023] Open
Abstract
Many brain-computer interfaces require a high mental workload. Recent research has shown that this could be greatly alleviated through machine learning, inferring user intentions via reactive brain responses. These signals are generated spontaneously while users merely observe assistive robots performing tasks. Using reactive brain signals, existing studies have addressed robot navigation tasks with a very limited number of potential target locations. Moreover, they use only binary, error-vs-correct classification of robot actions, leaving more detailed information unutilised. In this study a virtual robot had to navigate towards, and identify, target locations in both small and large grids, wherein any location could be the target. For the first time, we apply a system utilising detailed EEG information: 4-way classification of movements is performed, including specific information regarding when the target is reached. Additionally, we classify whether targets are correctly identified. Our proposed Bayesian strategy infers the most likely target location from the brain's responses. The experimental results show that our novel use of detailed information facilitates a more efficient and robust system than the state-of-the-art. Furthermore, unlike state-of-the-art approaches, we show scalability of our proposed approach: By tuning parameters appropriately, our strategy correctly identifies 98% of targets, even in large search spaces.
Collapse
Affiliation(s)
- Christopher Wirth
- Automatic Control and Systems Engineering Department, University of Sheffield, Sheffield, S1 4DT, UK.
- School of Medical Sciences, University of Manchester, Manchester, M13 9NT, UK.
| | - Jake Toth
- Automatic Control and Systems Engineering Department, University of Sheffield, Sheffield, S1 4DT, UK
| | - Mahnaz Arvaneh
- Automatic Control and Systems Engineering Department, University of Sheffield, Sheffield, S1 4DT, UK
| |
Collapse
|
7
|
Borra D, Mondini V, Magosso E, Müller-Putz GR. Decoding movement kinematics from EEG using an interpretable convolutional neural network. Comput Biol Med 2023; 165:107323. [PMID: 37619325 DOI: 10.1016/j.compbiomed.2023.107323] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 07/28/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023]
Abstract
Continuous decoding of hand kinematics has been recently explored for the intuitive control of electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs). Deep neural networks (DNNs) are emerging as powerful decoders, for their ability to automatically learn features from lightly pre-processed signals. However, DNNs for kinematics decoding lack in the interpretability of the learned features and are only used to realize within-subject decoders without testing other training approaches potentially beneficial for reducing calibration time, such as transfer learning. Here, we aim to overcome these limitations by using an interpretable convolutional neural network (ICNN) to decode 2-D hand kinematics (position and velocity) from EEG in a pursuit tracking task performed by 13 participants. The ICNN is trained using both within-subject and cross-subject strategies, and also testing the feasibility of transferring the knowledge learned on other subjects on a new one. Moreover, the network eases the interpretation of learned spectral and spatial EEG features. Our ICNN outperformed most of the other state-of-the-art decoders, showing the best trade-off between performance, size, and training time. Furthermore, transfer learning improved kinematics prediction in the low data regime. The network attributed the highest relevance for decoding to the delta-band across all subjects, and to higher frequencies (alpha, beta, low-gamma) for a cluster of them; contralateral central and parieto-occipital sites were the most relevant, reflecting the involvement of sensorimotor, visual and visuo-motor processing. The approach improved the quality of kinematics prediction from the EEG, at the same time allowing interpretation of the most relevant spectral and spatial features.
Collapse
Affiliation(s)
- Davide Borra
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena Campus, Cesena, Italy.
| | - Valeria Mondini
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Elisa Magosso
- Department of Electrical, Electronic and Information Engineering "Guglielmo Marconi" (DEI), University of Bologna, Cesena Campus, Cesena, Italy; Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy; Interdepartmental Center for Industrial Research on Health Sciences & Technologies, University of Bologna, Bologna, Italy
| | - Gernot R Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria; BioTechMed, Graz, Austria
| |
Collapse
|
8
|
Ventura S, Ottoboni G, Lullini G, Chattat R, Simoncini L, Magni E, Piperno R, La Porta F, Tessari A. Co-designing an interactive artificial intelligent system with post-stroke patients and caregivers to augment the lost abilities and improve their quality of life: a human-centric approach. Front Public Health 2023; 11:1227748. [PMID: 37808976 PMCID: PMC10551166 DOI: 10.3389/fpubh.2023.1227748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Accepted: 09/04/2023] [Indexed: 10/10/2023] Open
Abstract
Objectives The motor disability due to stroke compromises the autonomy of patients and caregivers. To support autonomy and other personal and social needs, trustworthy, multifunctional, adaptive, and interactive assistive devices represent optimal solutions. To fulfill this aim, an artificial intelligence system named MAIA would aim to interpret users' intentions and translate them into actions performed by assistive devices. Analyzing their perspectives is essential to develop the MAIA system operating in harmony with patients' and caregivers' needs as much as possible. Methods Post-stroke patients and caregivers were interviewed to explore the impact of motor disability on their lives, previous experiences with assistive technologies, opinions, and attitudes about MAIA and their needs. Interview transcripts were analyzed using inductive thematic analysis. Results Sixteen interviews were conducted with 12 post-stroke patients and four caregivers. Three themes emerged: (1) Needs to be satisfied, (2) MAIA technology acceptance, and (3) Perceived trustfulness. Overall, patients are seeking rehabilitative technology, contrary to caregivers needing assistive technology to help them daily. An easy-to-use and ergonomic technology is preferable. However, a few participants trust a system based on artificial intelligence. Conclusion An interactive artificial intelligence technology could help post-stroke patients and their caregivers to restore motor autonomy. The insights from participants to develop the system depends on their motor ability and the role of patients or caregiver. Although technology grows exponentially, more efforts are needed to strengthen people's trust in advanced technology.
Collapse
Affiliation(s)
- Sara Ventura
- Department of Psychology, University of Bologna, Bologna, Italy
- Instituto Polibienestar, University of Valencia, Valencia, Spain
| | | | - Giada Lullini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Rabih Chattat
- Department of Psychology, University of Bologna, Bologna, Italy
| | | | - Elisabetta Magni
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Roberto Piperno
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Fabio La Porta
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Alessia Tessari
- Department of Psychology, University of Bologna, Bologna, Italy
- Alma Mater Research Institute for Human-Centered Artificial Intelligence, University of Bologna, Bologna, Italy
| |
Collapse
|
9
|
Vidaurre C, Irastorza-Landa N, Sarasola-Sanz A, Insausti-Delgado A, Ray AM, Bibián C, Helmhold F, Mahmoud WJ, Ortego-Isasa I, López-Larraz E, Lozano Peiteado H, Ramos-Murguialday A. Challenges of neural interfaces for stroke motor rehabilitation. Front Hum Neurosci 2023; 17:1070404. [PMID: 37789905 PMCID: PMC10543821 DOI: 10.3389/fnhum.2023.1070404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 08/28/2023] [Indexed: 10/05/2023] Open
Abstract
More than 85% of stroke survivors suffer from different degrees of disability for the rest of their lives. They will require support that can vary from occasional to full time assistance. These conditions are also associated to an enormous economic impact for their families and health care systems. Current rehabilitation treatments have limited efficacy and their long-term effect is controversial. Here we review different challenges related to the design and development of neural interfaces for rehabilitative purposes. We analyze current bibliographic evidence of the effect of neuro-feedback in functional motor rehabilitation of stroke patients. We highlight the potential of these systems to reconnect brain and muscles. We also describe all aspects that should be taken into account to restore motor control. Our aim with this work is to help researchers designing interfaces that demonstrate and validate neuromodulation strategies to enforce a contingent and functional neural linkage between the central and the peripheral nervous system. We thus give clues to design systems that can improve or/and re-activate neuroplastic mechanisms and open a new recovery window for stroke patients.
Collapse
Affiliation(s)
- Carmen Vidaurre
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Ikerbasque Science Foundation, Bilbao, Spain
| | | | | | | | - Andreas M. Ray
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Carlos Bibián
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Florian Helmhold
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Wala J. Mahmoud
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| | - Iñaki Ortego-Isasa
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
| | - Eduardo López-Larraz
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
- Bitbrain, Zaragoza, Spain
| | | | - Ander Ramos-Murguialday
- TECNALIA, Basque Research and Technology Alliance (BRTA), San Sebastian, Spain
- Institute for Medical Psychology and Behavioral Neurobiology, University of Tübingen, Tübingen, Germany
| |
Collapse
|
10
|
Nam H, Kim JM, Choi W, Bak S, Kam TE. The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets. Front Hum Neurosci 2023; 17:1205881. [PMID: 37342822 PMCID: PMC10277566 DOI: 10.3389/fnhum.2023.1205881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 05/17/2023] [Indexed: 06/23/2023] Open
Abstract
Introduction The brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI. Methods In this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario. Results and discussion The results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.
Collapse
Affiliation(s)
| | | | | | | | - Tae-Eui Kam
- Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea
| |
Collapse
|
11
|
López-Larraz E, Escolano C, Robledo-Menéndez A, Morlas L, Alda A, Minguez J. A garment that measures brain activity: proof of concept of an EEG sensor layer fully implemented with smart textiles. Front Hum Neurosci 2023; 17:1135153. [PMID: 37305362 PMCID: PMC10250743 DOI: 10.3389/fnhum.2023.1135153] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 04/20/2023] [Indexed: 06/13/2023] Open
Abstract
This paper presents the first garment capable of measuring brain activity with accuracy comparable to that of state-of-the art dry electroencephalogram (EEG) systems. The main innovation is an EEG sensor layer (i.e., the electrodes, the signal transmission, and the cap support) made entirely of threads, fabrics, and smart textiles, eliminating the need for metal or plastic materials. The garment is connected to a mobile EEG amplifier to complete the measurement system. As a first proof of concept, the new EEG system (Garment-EEG) was characterized with respect to a state-of-the-art Ag/AgCl dry-EEG system (Dry-EEG) over the forehead area of healthy participants in terms of: (1) skin-electrode impedance; (2) EEG activity; (3) artifacts; and (4) user ergonomics and comfort. The results show that the Garment-EEG system provides comparable recordings to Dry-EEG, but it is more susceptible to artifacts under adverse recording conditions due to poorer contact impedances. The textile-based sensor layer offers superior ergonomics and comfort compared to its metal-based counterpart. We provide the datasets recorded with Garment-EEG and Dry-EEG systems, making available the first open-access dataset of an EEG sensor layer built exclusively with textile materials. Achieving user acceptance is an obstacle in the field of neurotechnology. The introduction of EEG systems encapsulated in wearables has the potential to democratize neurotechnology and non-invasive brain-computer interfaces, as they are naturally accepted by people in their daily lives. Furthermore, supporting the EEG implementation in the textile industry may result in lower cost and less-polluting manufacturing processes compared to metal and plastic industries.
Collapse
|
12
|
Kapgate DD, Prasad. K K. Adaptive classification helps hybrid visual brain computer interface systems handle non‐stationary cortical signals. COGNITIVE COMPUTATION AND SYSTEMS 2023. [DOI: 10.1049/ccs2.12077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2023] Open
Affiliation(s)
- Deepak D. Kapgate
- Institute of Engineering and Technology Srinivas University Mangalore India
| | - Krishna Prasad. K
- College of Computer Science and Information Science Srinivas University Mangalore India
| |
Collapse
|
13
|
Siribunyaphat N, Punsawad Y. Brain-Computer Interface Based on Steady-State Visual Evoked Potential Using Quick-Response Code Pattern for Wheelchair Control. SENSORS (BASEL, SWITZERLAND) 2023; 23:2069. [PMID: 36850667 PMCID: PMC9964090 DOI: 10.3390/s23042069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 06/18/2023]
Abstract
Brain-computer interfaces (BCIs) are widely utilized in control applications for people with severe physical disabilities. Several researchers have aimed to develop practical brain-controlled wheelchairs. An existing electroencephalogram (EEG)-based BCI based on steady-state visually evoked potential (SSVEP) was developed for device control. This study utilized a quick-response (QR) code visual stimulus pattern for a robust existing system. Four commands were generated using the proposed visual stimulation pattern with four flickering frequencies. Moreover, we employed a relative power spectrum density (PSD) method for the SSVEP feature extraction and compared it with an absolute PSD method. We designed experiments to verify the efficiency of the proposed system. The results revealed that the proposed SSVEP method and algorithm yielded an average classification accuracy of approximately 92% in real-time processing. For the wheelchair simulated via independent-based control, the proposed BCI control required approximately five-fold more time than the keyboard control for real-time control. The proposed SSVEP method using a QR code pattern can be used for BCI-based wheelchair control. However, it suffers from visual fatigue owing to long-time continuous control. We will verify and enhance the proposed system for wheelchair control in people with severe physical disabilities.
Collapse
Affiliation(s)
| | - Yunyong Punsawad
- School of Informatics, Walailak University, Nakhon Si Thammarat 80160, Thailand
- Informatics Innovative Center of Excellence, Walailak University, Nakhon Si Thammarat 80160, Thailand
| |
Collapse
|
14
|
Li H, Liu M, Yu X, Zhu J, Wang C, Chen X, Feng C, Leng J, Zhang Y, Xu F. Coherence based graph convolution network for motor imagery-induced EEG after spinal cord injury. Front Neurosci 2023; 16:1097660. [PMID: 36711141 PMCID: PMC9880407 DOI: 10.3389/fnins.2022.1097660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Background Spinal cord injury (SCI) may lead to impaired motor function, autonomic nervous system dysfunction, and other dysfunctions. Brain-computer Interface (BCI) system based on motor imagery (MI) can provide more scientific and effective treatment solutions for SCI patients. Methods According to the interaction between brain regions, a coherence-based graph convolutional network (C-GCN) method is proposed to extract the temporal-frequency-spatial features and functional connectivity information of EEG signals. The proposed algorithm constructs multi-channel EEG features based on coherence networks as graphical signals and then classifies MI tasks. Different from the traditional graphical convolutional neural network (GCN), the C-GCN method uses the coherence network of EEG signals to determine MI-related functional connections, which are used to represent the intrinsic connections between EEG channels in different rhythms and different MI tasks. EEG data of SCI patients and healthy subjects have been analyzed, where healthy subjects served as the control group. Results The experimental results show that the C-GCN method can achieve the best classification performance with certain reliability and stability, the highest classification accuracy is 96.85%. Conclusion The proposed framework can provide an effective theoretical basis for the rehabilitation treatment of SCI patients.
Collapse
Affiliation(s)
- Han Li
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Ming Liu
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Xin Yu
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - JianQun Zhu
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Chongfeng Wang
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Xinyi Chen
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Chao Feng
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,*Correspondence: Chao Feng,
| | - Jiancai Leng
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,Jiancai Leng,
| | - Yang Zhang
- Rehabilitation Center, Qilu Hospital of Shandong University, Jinan, China,Yang Zhang,
| | - Fangzhou Xu
- International School for Optoelectronic Engineering, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,Fangzhou Xu,
| |
Collapse
|
15
|
Dimova-Edeleva V, Ehrlich SK, Cheng G. Brain computer interface to distinguish between self and other related errors in human agent collaboration. Sci Rep 2022; 12:20764. [PMID: 36456595 PMCID: PMC9715724 DOI: 10.1038/s41598-022-24899-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 11/22/2022] [Indexed: 12/05/2022] Open
Abstract
When a human and machine collaborate on a shared task, ambiguous events might occur that could be perceived as an error by the human partner. In such events, spontaneous error-related potentials (ErrPs) are evoked in the human brain. Knowing whom the human perceived as responsible for the error would help a machine in co-adaptation and shared control paradigms to better adapt to human preferences. Therefore, we ask whether self- and agent-related errors evoke different ErrPs. Eleven subjects participated in an electroencephalography human-agent collaboration experiment with a collaborative trajectory-following task on two collaboration levels, where movement errors occurred as trajectory deviations. Independently of the collaboration level, we observed a higher amplitude of the responses on the midline central Cz electrode for self-related errors compared to observed errors made by the agent. On average, Support Vector Machines classified self- and agent-related errors with 72.64% accuracy using subject-specific features. These results demonstrate that ErrPs can tell if a person relates an error to themselves or an external autonomous agent during collaboration. Thus, the collaborative machine will receive more informed feedback for the error attribution that allows appropriate error identification, a possibility for correction, and avoidance in future actions.
Collapse
Affiliation(s)
- Viktorija Dimova-Edeleva
- grid.6936.a0000000123222966Munich Institute of Robotics and Machine Intelligence (MIRMI), Technical University of Munich, Munich, Germany
| | - Stefan K. Ehrlich
- grid.6936.a0000000123222966TUM School of Computation, Information and Technology, Department of Computer Engineering, Institute of Cognitive Systems, Technical University of Munich, Munich, Germany
| | - Gordon Cheng
- grid.6936.a0000000123222966TUM School of Computation, Information and Technology, Department of Computer Engineering, Institute of Cognitive Systems, Technical University of Munich, Munich, Germany
| |
Collapse
|
16
|
Ferracuti F, Freddi A, Iarlori S, Monteriù A, Omer KIM, Porcaro C. A human-in-the-loop approach for enhancing mobile robot navigation in presence of obstacles not detected by the sensory set. Front Robot AI 2022; 9:909971. [PMID: 36523445 PMCID: PMC9744805 DOI: 10.3389/frobt.2022.909971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 11/07/2022] [Indexed: 04/20/2024] Open
Abstract
Human-in-the-loop approaches can greatly enhance the human-robot interaction by making the user an active part of the control loop, who can provide a feedback to the robot in order to augment its capabilities. Such feedback becomes even more important in all those situations where safety is of utmost concern, such as in assistive robotics. This study aims to realize a human-in-the-loop approach, where the human can provide a feedback to a specific robot, namely, a smart wheelchair, to augment its artificial sensory set, extending and improving its capabilities to detect and avoid obstacles. The feedback is provided by both a keyboard and a brain-computer interface: with this scope, the work has also included a protocol design phase to elicit and evoke human brain event-related potentials. The whole architecture has been validated within a simulated robotic environment, with electroencephalography signals acquired from different test subjects.
Collapse
Affiliation(s)
- Francesco Ferracuti
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Alessandro Freddi
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Sabrina Iarlori
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | - Andrea Monteriù
- Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy
| | | | - Camillo Porcaro
- Department of Neuroscience and Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
- Institute of Cognitive Sciences and Technologies (ISCT)-National Research Council (CNR), Rome, Italy
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, United Kingdom
| |
Collapse
|
17
|
Mathewson KW, Parker ASR, Sherstan C, Edwards AL, Sutton RS, Pilarski PM. Communicative capital: a key resource for human-machine shared agency and collaborative capacity. Neural Comput Appl 2022; 35:16805-16819. [PMID: 37455836 PMCID: PMC10338399 DOI: 10.1007/s00521-022-07948-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 10/12/2022] [Indexed: 11/16/2022]
Abstract
In this work, we present a perspective on the role machine intelligence can play in supporting human abilities. In particular, we consider research in rehabilitation technologies such as prosthetic devices, as this domain requires tight coupling between human and machine. Taking an agent-based view of such devices, we propose that human-machine collaborations have a capacity to perform tasks which is a result of the combined agency of the human and the machine. We introduce communicative capital as a resource developed by a human and a machine working together in ongoing interactions. Development of this resource enables the partnership to eventually perform tasks at a capacity greater than either individual could achieve alone. We then examine the benefits and challenges of increasing the agency of prostheses by surveying literature which demonstrates that building communicative resources enables more complex, task-directed interactions. The viewpoint developed in this article extends current thinking on how best to support the functional use of increasingly complex prostheses, and establishes insight toward creating more fruitful interactions between humans and supportive, assistive, and augmentative technologies.
Collapse
Affiliation(s)
| | - Adam S. R. Parker
- University of Alberta, Edmonton, Canada
- Alberta Machine Intelligence Institute (Amii), Edmonton, Canada
| | | | | | - Richard S. Sutton
- DeepMind, Montreal, Canada
- University of Alberta, Edmonton, Canada
- Alberta Machine Intelligence Institute (Amii), Edmonton, Canada
- DeepMind, Edmonton, Canada
| | - Patrick M. Pilarski
- DeepMind, Montreal, Canada
- University of Alberta, Edmonton, Canada
- Alberta Machine Intelligence Institute (Amii), Edmonton, Canada
- DeepMind, Edmonton, Canada
| |
Collapse
|
18
|
Cortical Reorganization of Early Somatosensory Processing in Hemiparetic Stroke. J Clin Med 2022; 11:jcm11216449. [PMID: 36362680 PMCID: PMC9654771 DOI: 10.3390/jcm11216449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/27/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022] Open
Abstract
The cortical motor system can be reorganized following a stroke, with increased recruitment of the contralesional hemisphere. However, it is unknown whether a similar hemispheric shift occurs in the somatosensory system to adapt to this motor change, and whether this is related to movement impairments. This proof-of-concept study assessed somatosensory evoked potentials (SEPs), P50 and N100, in hemiparetic stroke participants and age-matched controls using high-density electroencephalograph (EEG) recordings during tactile finger stimulation. The laterality index was calculated to determine the hemispheric dominance of the SEP and re-confirmed with source localization. The study found that latencies of P50 and N100 were significantly delayed in stroke brains when stimulating the paretic hand. The amplitude of P50 in the contralateral (to stimulated hand) hemisphere was negatively correlated with the Fügl-Meyer upper extremity motor score in stroke. Bilateral cortical responses were detected in stroke, while only contralateral cortical responses were shown in controls, resulting in a significant difference in the laterality index. These results suggested that somatosensory reorganization after stroke involves increased recruitment of ipsilateral cortical regions, especially for the N100 SEP component. This reorganization delays the latency of somatosensory processing after a stroke. This research provided new insights related to the somatosensory reorganization after stroke, which could enrich future hypothesis-driven therapeutic rehabilitation strategies from a sensory or sensory-motor perspective.
Collapse
|
19
|
Emsawas T, Morita T, Kimura T, Fukui KI, Numao M. Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification. SENSORS (BASEL, SWITZERLAND) 2022; 22:8250. [PMID: 36365948 PMCID: PMC9654218 DOI: 10.3390/s22218250] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 10/22/2022] [Accepted: 10/23/2022] [Indexed: 06/16/2023]
Abstract
Deep learning using an end-to-end convolutional neural network (ConvNet) has been applied to several electroencephalography (EEG)-based brain-computer interface tasks to extract feature maps and classify the target output. However, the EEG analysis remains challenging since it requires consideration of various architectural design components that influence the representational ability of extracted features. This study proposes an EEG-based emotion classification model called the multi-kernel temporal and spatial convolution network (MultiT-S ConvNet). The multi-scale kernel is used in the model to learn various time resolutions, and separable convolutions are applied to find related spatial patterns. In addition, we enhanced both the temporal and spatial filters with a lightweight gating mechanism. To validate the performance and classification accuracy of MultiT-S ConvNet, we conduct subject-dependent and subject-independent experiments on EEG-based emotion datasets: DEAP and SEED. Compared with existing methods, MultiT-S ConvNet outperforms with higher accuracy results and a few trainable parameters. Moreover, the proposed multi-scale module in temporal filtering enables extracting a wide range of EEG representations, covering short- to long-wavelength components. This module could be further implemented in any model of EEG-based convolution networks, and its ability potentially improves the model's learning capacity.
Collapse
Affiliation(s)
- Taweesak Emsawas
- Graduate School of Information Science and Technology, Osaka University, Osaka 565-0871, Japan
| | - Takashi Morita
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, Osaka 567-0047, Japan
| | - Tsukasa Kimura
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, Osaka 567-0047, Japan
| | - Ken-ichi Fukui
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, Osaka 567-0047, Japan
| | - Masayuki Numao
- The Institute of Scientific and Industrial Research (ISIR), Osaka University, Osaka 567-0047, Japan
| |
Collapse
|
20
|
Behboodi A, Lee WA, Hinchberger VS, Damiano DL. Determining optimal mobile neurofeedback methods for motor neurorehabilitation in children and adults with non-progressive neurological disorders: a scoping review. J Neuroeng Rehabil 2022; 19:104. [PMID: 36171602 PMCID: PMC9516814 DOI: 10.1186/s12984-022-01081-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 09/08/2022] [Indexed: 11/22/2022] Open
Abstract
Background Brain–computer interfaces (BCI), initially designed to bypass the peripheral motor system to externally control movement using brain signals, are additionally being utilized for motor rehabilitation in stroke and other neurological disorders. Also called neurofeedback training, multiple approaches have been developed to link motor-related cortical signals to assistive robotic or electrical stimulation devices during active motor training with variable, but mostly positive, functional outcomes reported. Our specific research question for this scoping review was: for persons with non-progressive neurological injuries who have the potential to improve voluntary motor control, which mobile BCI-based neurofeedback methods demonstrate or are associated with improved motor outcomes for Neurorehabilitation applications? Methods We searched PubMed, Web of Science, and Scopus databases with all steps from study selection to data extraction performed independently by at least 2 individuals. Search terms included: brain machine or computer interfaces, neurofeedback and motor; however, only studies requiring a motor attempt, versus motor imagery, were retained. Data extraction included participant characteristics, study design details and motor outcomes. Results From 5109 papers, 139 full texts were reviewed with 23 unique studies identified. All utilized EEG and, except for one, were on the stroke population. The most commonly reported functional outcomes were the Fugl-Meyer Assessment (FMA; n = 13) and the Action Research Arm Test (ARAT; n = 6) which were then utilized to assess effectiveness, evaluate design features, and correlate with training doses. Statistically and functionally significant pre-to post training changes were seen in FMA, but not ARAT. Results did not differ between robotic and electrical stimulation feedback paradigms. Notably, FMA outcomes were positively correlated with training dose. Conclusion This review on BCI-based neurofeedback training confirms previous findings of effectiveness in improving motor outcomes with some evidence of enhanced neuroplasticity in adults with stroke. Associative learning paradigms have emerged more recently which may be particularly feasible and effective methods for Neurorehabilitation. More clinical trials in pediatric and adult neurorehabilitation to refine methods and doses and to compare to other evidence-based training strategies are warranted.
Collapse
Affiliation(s)
- Ahad Behboodi
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA
| | - Walker A Lee
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA
| | | | - Diane L Damiano
- Rehabilitation Medicine Department, National Institutes of Health, Bethesda, MD, USA.
| |
Collapse
|
21
|
Tibrewal N, Leeuwis N, Alimardani M. Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users. PLoS One 2022; 17:e0268880. [PMID: 35867703 PMCID: PMC9307149 DOI: 10.1371/journal.pone.0268880] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 05/11/2022] [Indexed: 11/19/2022] Open
Abstract
Motor Imagery Brain-Computer Interfaces (MI-BCIs) are AI-driven systems that capture brain activity patterns associated with mental imagination of movement and convert them into commands for external devices. Traditionally, MI-BCIs operate on Machine Learning (ML) algorithms, which require extensive signal processing and feature engineering to extract changes in sensorimotor rhythms (SMR). In recent years, Deep Learning (DL) models have gained popularity for EEG classification as they provide a solution for automatic extraction of spatio-temporal features in the signals. However, past BCI studies that employed DL models, only attempted them with a small group of participants, without investigating the effectiveness of this approach for different user groups such as inefficient users. BCI inefficiency is a known and unsolved problem within BCI literature, generally defined as the inability of the user to produce the desired SMR patterns for the BCI classifier. In this study, we evaluated the effectiveness of DL models in capturing MI features particularly in the inefficient users. EEG signals from 54 subjects who performed a MI task of left- or right-hand grasp were recorded to compare the performance of two classification approaches; a ML approach vs. a DL approach. In the ML approach, Common Spatial Patterns (CSP) was used for feature extraction and then Linear Discriminant Analysis (LDA) model was employed for binary classification of the MI task. In the DL approach, a Convolutional Neural Network (CNN) model was constructed on the raw EEG signals. Additionally, subjects were divided into high vs. low performers based on their online BCI accuracy and the difference between the two classifiers’ performance was compared between groups. Our results showed that the CNN model improved the classification accuracy for all subjects within the range of 2.37 to 28.28%, but more importantly, this improvement was significantly larger for low performers. Our findings show promise for employment of DL models on raw EEG signals in future MI-BCI systems, particularly for BCI inefficient users who are unable to produce desired sensorimotor patterns for conventional ML approaches.
Collapse
Affiliation(s)
- Navneet Tibrewal
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
| | - Nikki Leeuwis
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
- Research Department, Unravel Research, Utrecht, The Netherlands
| | - Maryam Alimardani
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, The Netherlands
- * E-mail:
| |
Collapse
|
22
|
Decoding of Turning Intention during Walking Based on EEG Biomarkers. BIOSENSORS 2022; 12:bios12080555. [PMID: 35892452 PMCID: PMC9330787 DOI: 10.3390/bios12080555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Revised: 07/07/2022] [Accepted: 07/19/2022] [Indexed: 12/11/2022]
Abstract
In the EEG literature, there is a lack of asynchronous intention models that realistically propose interfaces for applications that must operate in real time. In this work, a novel BMI approach to detect in real time the intention to turn is proposed. For this purpose, an offline, pseudo-online and online analysis is presented to validate the EEG as a biomarker for the intention to turn. This article presents a methodology for the creation of a BMI that could differentiate two classes: monotonous walk and intention to turn. A comparison of some of the most popular algorithms in the literature is conducted. To filter the signal, two relevant algorithms are used: H∞ filter and ASR. For processing and classification, the mean of the covariance matrices in the Riemannian space was calculated and then, with various classifiers of different types, the distance of the test samples to each class in the Riemannian space was estimated. This dispenses with power-based models and the necessary baseline correction, which is a problem in realistic scenarios. In the cross-validation for a generic selection (valid for any subject) and a personalized one, the results were, on average, 66.2% and 69.6% with the best filter H∞. For the pseudo-online, the custom configuration for each subject was an average of 40.2% TP and 9.3 FP/min; the best subject obtained 43.9% TP and 2.9 FP/min. In the final validation test, this subject obtained 2.5 FP/min and an accuracy rate of 71.43%, and the turn anticipation was 0.21 s on average.
Collapse
|
23
|
Jeong JH, Cho JH, Lee YE, Lee SH, Shin GH, Kweon YS, Millán JDR, Müller KR, Lee SW. 2020 International brain-computer interface competition: A review. Front Hum Neurosci 2022; 16:898300. [PMID: 35937679 PMCID: PMC9354666 DOI: 10.3389/fnhum.2022.898300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 07/01/2022] [Indexed: 11/16/2022] Open
Abstract
The brain-computer interface (BCI) has been investigated as a form of communication tool between the brain and external devices. BCIs have been extended beyond communication and control over the years. The 2020 international BCI competition aimed to provide high-quality neuroscientific data for open access that could be used to evaluate the current degree of technical advances in BCI. Although there are a variety of remaining challenges for future BCI advances, we discuss some of more recent application directions: (i) few-shot EEG learning, (ii) micro-sleep detection (iii) imagined speech decoding, (iv) cross-session classification, and (v) EEG(+ear-EEG) detection in an ambulatory environment. Not only did scientists from the BCI field compete, but scholars with a broad variety of backgrounds and nationalities participated in the competition to address these challenges. Each dataset was prepared and separated into three data that were released to the competitors in the form of training and validation sets followed by a test set. Remarkable BCI advances were identified through the 2020 competition and indicated some trends of interest to BCI researchers.
Collapse
Affiliation(s)
- Ji-Hoon Jeong
- School of Computer Science, Chungbuk National University, Cheongju, South Korea
| | - Jeong-Hyun Cho
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Young-Eun Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Seo-Hyun Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Gi-Hwan Shin
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Young-Seok Kweon
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - José del R. Millán
- Department of Electrical and Computer Engineering, University of Texas at Austin, Austin, TX, United States
| | - Klaus-Robert Müller
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Machine Learning Group, Department of Computer Science, Berlin Institute of Technology, Berlin, Germany
- Max Planck Institute for Informatics, Saarbrucken, Germany
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
- Department of Artificial Intelligence, Korea University, Seoul, South Korea
| |
Collapse
|
24
|
Gantenbein J, Meyer JT, Jager L, Sigrist R, Gassert R, Lambercy O. An Analysis of Intention Detection Strategies to Control Advanced Assistive Technologies at the CYBATHLON. IEEE Int Conf Rehabil Robot 2022; 2022:1-6. [PMID: 36176133 DOI: 10.1109/icorr55369.2022.9896539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
With the increasing range of functionalities of advanced assistive technologies (AAT), reliable control and initiation of the desired actions become increasingly challenging for users. In this work, we present an analysis of current practices, user preferences, and usability of AAT intention detection strategies based on a survey among participants with disabilities at the CYBATHLON 2020 Global Edition. We collected data from 35 respondents, using devices in various disciplines and levels of technology maturity. We found that conventional, direct inputs such as buttons and joysticks are used by the majority of AAT (71.4%) due to their simplicity and learnability. However, 22 respondents (62.8%) reported a desire for more natural control using muscle or non-invasive brain signals, and 37.1% even reported an openness to invasive strategies for potentially improved control. The usability of the used strategies in terms of the explored attributes (reliability, mental effort, required learning) was mainly perceived positively, whereas no significant difference was observed across intention detection strategies and device types. It can be assumed that the strategies used during the CYBATHLON realistically represent options to control an AAT in a dynamic, physically and mentally demanding environment. Thus, this work underlines the need for carefully considering user needs and preferences for the selection of intention detection strategies in a context of use outside the laboratory.
Collapse
|
25
|
Manual 3D Control of an Assistive Robotic Manipulator Using Alpha Rhythms and an Auditory Menu: A Proof-of-Concept. SIGNALS 2022. [DOI: 10.3390/signals3020024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Brain–Computer Interfaces (BCIs) have been regarded as potential tools for individuals with severe motor disabilities, such as those with amyotrophic lateral sclerosis, that render interfaces that rely on movement unusable. This study aims to develop a dependent BCI system for manual end-point control of a robotic arm. A proof-of-concept system was devised using parieto-occipital alpha wave modulation and a cyclic menu with auditory cues. Users choose a movement to be executed and asynchronously stop said action when necessary. Tolerance intervals allowed users to cancel or confirm actions. Eight able-bodied subjects used the system to perform a pick-and-place task. To investigate the potential learning effects, the experiment was conducted twice over the course of two consecutive days. Subjects obtained satisfactory completion rates (84.0 ± 15.0% and 74.4 ± 34.5% for the first and second day, respectively) and high path efficiency (88.9 ± 11.7% and 92.2 ± 9.6%). Subjects took on average 439.7 ± 203.3 s to complete each task, but the robot was only in motion 10% of the time. There was no significant difference in performance between both days. The developed control scheme provided users with intuitive control, but a considerable amount of time is spent waiting for the right target (auditory cue). Implementing other brain signals may increase its speed.
Collapse
|
26
|
Li H, Bi L, Yi J. Sliding-Mode Nonlinear Predictive Control of Brain-Controlled Mobile Robots. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:5419-5431. [PMID: 33232253 DOI: 10.1109/tcyb.2020.3031667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In this article, we develop a robust sliding-mode nonlinear predictive controller for brain-controlled robots with enhanced performance, safety, and robustness. First, the kinematics and dynamics of a mobile robot are built. After that, the proposed controller is developed by cascading a predictive controller and a smooth sliding-mode controller. The predictive controller integrates the human intention tracking with safety guarantee objectives into an optimization problem to minimize the invasion to human intention while maintaining robot safety. The smooth sliding-mode controller is designed to achieve robust desired velocity tracking. The results of human-in-the-loop simulation and robotic experiments both show the efficacy and robust performance of the proposed controller. This work provides an enabling design to enhance the future research and development of brain-controlled robots.
Collapse
|
27
|
Riccio A, Schettini F, Galiotta V, Giraldi E, Grasso MG, Cincotti F, Mattia D. Usability of a Hybrid System Combining P300-Based Brain-Computer Interface and Commercial Assistive Technologies to Enhance Communication in People With Multiple Sclerosis. Front Hum Neurosci 2022; 16:868419. [PMID: 35721361 PMCID: PMC9204311 DOI: 10.3389/fnhum.2022.868419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/26/2022] [Indexed: 11/25/2022] Open
Abstract
Brain-computer interface (BCI) can provide people with motor disabilities with an alternative channel to access assistive technology (AT) software for communication and environmental interaction. Multiple sclerosis (MS) is a chronic disease of the central nervous system that mostly starts in young adulthood and often leads to a long-term disability, possibly exacerbated by the presence of fatigue. Patients with MS have been rarely considered as potential BCI end-users. In this pilot study, we evaluated the usability of a hybrid BCI (h-BCI) system that enables both a P300-based BCI and conventional input devices (i.e., muscular dependent) to access mainstream applications through the widely used AT software for communication “Grid 3.” The evaluation was performed according to the principles of the user-centered design (UCD) with the aim of providing patients with MS with an alternative control channel (i.e., BCI), potentially less sensitive to fatigue. A total of 13 patients with MS were enrolled. In session I, participants were presented with a widely validated P300-based BCI (P3-speller); in session II, they had to operate Grid 3 to access three mainstream applications with (1) an AT conventional input device and (2) the h-BCI. Eight patients completed the protocol. Five out of eight patients with MS were successfully able to access the Grid 3 via the BCI, with a mean online accuracy of 83.3% (± 14.6). Effectiveness (online accuracy), satisfaction, and workload were comparable between the conventional AT inputs and the BCI channel in controlling the Grid 3. As expected, the efficiency (time for correct selection) resulted to be significantly lower for the BCI with respect to the AT conventional channels (Z = 0.2, p < 0.05). Although cautious due to the limited sample size, these preliminary findings indicated that the BCI control channel did not have a detrimental effect with respect to conventional AT channels on the ability to operate an AT software (Grid 3). Therefore, we inferred that the usability of the two access modalities was comparable. The integration of BCI with commercial AT input devices to access a widely used AT software represents an important step toward the introduction of BCIs into the AT centers’ daily practice.
Collapse
Affiliation(s)
- Angela Riccio
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
- *Correspondence: Angela Riccio,
| | - Francesca Schettini
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Valentina Galiotta
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | - Enrico Giraldi
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
| | | | - Febo Cincotti
- Department of Computer, Control and Management Engineering Antonio Ruberti, Sapienza University of Rome, Rome, Italy
| | - Donatella Mattia
- Neuroelectric Imaging and BCI Lab, Fondazione Santa Lucia (IRCCS), Rome, Italy
- Servizio Ausilioteca per la Riabilitazione Assistita con Tecnologia, Fondazione Santa Lucia (IRCCS), Rome, Italy
| |
Collapse
|
28
|
Shi K, Mu F, Huang R, Huang K, Peng Z, Zou C, Yang X, Cheng H. Multimodal Human-Exoskeleton Interface for Lower Limb Movement Prediction Through a Dense Co-Attention Symmetric Mechanism. Front Neurosci 2022; 16:796290. [PMID: 35546887 PMCID: PMC9082753 DOI: 10.3389/fnins.2022.796290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
A challenging task for the biological neural signal-based human-exoskeleton interface is to achieve accurate lower limb movement prediction of patients with hemiplegia in rehabilitation training scenarios. The human-exoskeleton interface based on single-modal biological signals such as electroencephalogram (EEG) is currently not mature in predicting movements, due to its unreliability. The multimodal human-exoskeleton interface is a very novel solution to this problem. This kind of interface normally combines the EEG signal with surface electromyography (sEMG) signal. However, their use for the lower limb movement prediction is still limited—the connection between sEMG and EEG signals and the deep feature fusion between them are ignored. In this article, a Dense con-attention mechanism-based Multimodal Enhance Fusion Network (DMEFNet) is proposed for predicting lower limb movement of patients with hemiplegia. The DMEFNet introduces the con-attention structure to extract the common attention between sEMG and EEG signal features. To verify the effectiveness of DMEFNet, an sEMG and EEG data acquisition experiment and an incomplete asynchronous data collection paradigm are designed. The experimental results show that DMEFNet has a good movement prediction performance in both within-subject and cross-subject situations, reaching an accuracy of 82.96 and 88.44%, respectively.
Collapse
Affiliation(s)
- Kecheng Shi
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Fengjun Mu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Rui Huang
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Ke Huang
- Glasgow College, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhinan Peng
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Chaobin Zou
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Xiao Yang
- Department of Orthopedics, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Hong Cheng
- School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu, China
| |
Collapse
|
29
|
Jalilpour S, Müller-Putz G. Toward passive BCI: asynchronous decoding of neural responses to direction- and angle-specific perturbations during a simulated cockpit scenario. Sci Rep 2022; 12:6802. [PMID: 35473959 PMCID: PMC9042920 DOI: 10.1038/s41598-022-10906-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2021] [Accepted: 04/01/2022] [Indexed: 11/09/2022] Open
Abstract
Neuroimaging studies have provided proof that loss of balance evokes specific neural transient wave complexes in electroencephalography (EEG), called perturbation evoked potentials (PEPs). Online decoding of balance perturbations from ongoing EEG signals can establish the possibility of implementing passive brain-computer interfaces (pBCIs) as a part of aviation/driving assistant systems. In this study, we investigated the feasibility of identifying the existence and expression of perturbations in four different conditions by using EEG signals. Fifteen healthy participants experienced four various postural changes while they sat in a glider cockpit. Sudden perturbations were exposed by a robot connected to a glider and moved to the right and left directions with tilting angles of 5 and 10 degrees. Perturbations occurred in an oddball paradigm in which participants were not aware of the time and expression of the perturbations. We employed a hierarchical approach to separate the perturbation and rest, and then discriminate the expression of perturbations. The performance of the BCI system was evaluated by using classification accuracy and F1 score. Asynchronously, we achieved average accuracies of 89.83 and 73.64% and average F1 scores of 0.93 and 0.60 for binary and multiclass classification, respectively. These results manifest the practicality of pBCI for the detection of balance disturbances in a realistic situation.
Collapse
Affiliation(s)
- Shayan Jalilpour
- Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/4, 8010, Graz, Austria
| | - Gernot Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/4, 8010, Graz, Austria. .,BioTechMed, Graz, Austria.
| |
Collapse
|
30
|
Värbu K, Muhammad N, Muhammad Y. Past, Present, and Future of EEG-Based BCI Applications. SENSORS (BASEL, SWITZERLAND) 2022; 22:3331. [PMID: 35591021 PMCID: PMC9101004 DOI: 10.3390/s22093331] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Revised: 04/05/2022] [Accepted: 04/25/2022] [Indexed: 06/15/2023]
Abstract
An electroencephalography (EEG)-based brain-computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed.
Collapse
Affiliation(s)
- Kaido Värbu
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia;
| | - Naveed Muhammad
- Institute of Computer Science, University of Tartu, 51009 Tartu, Estonia;
| | - Yar Muhammad
- Department of Computing & Games, School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough TS1 3BX, UK;
| |
Collapse
|
31
|
Müller-Putz GR, Kobler RJ, Pereira J, Lopes-Dias C, Hehenberger L, Mondini V, Martínez-Cagigal V, Srisrisawang N, Pulferer H, Batistić L, Sburlea AI. Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control. Front Hum Neurosci 2022; 16:841312. [PMID: 35360289 PMCID: PMC8961864 DOI: 10.3389/fnhum.2022.841312] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 02/16/2022] [Indexed: 11/13/2022] Open
Abstract
Establishing the basic knowledge, methodology, and technology for a framework for the continuous decoding of hand/arm movement intention was the aim of the ERC-funded project “Feel Your Reach”. In this work, we review the studies and methods we performed and implemented in the last 6 years, which build the basis for enabling severely paralyzed people to non-invasively control a robotic arm in real-time from electroencephalogram (EEG). In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback. Although we have tested some of our approaches already with the target populations, we still need to transfer the “Feel Your Reach” framework to people with cervical spinal cord injury and evaluate the decoders’ performance while participants attempt to perform upper-limb movements. While on the one hand, we made major progress towards this ambitious goal, we also critically discuss current limitations.
Collapse
Affiliation(s)
- Gernot R. Müller-Putz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- BioTechMed, Graz, Austria
- *Correspondence: Gernot R. Müller-Putz
| | - Reinmar J. Kobler
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- RIKEN Center for Advanced Intelligence Project, Kyoto, Japan
| | - Joana Pereira
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
- Brain-State Decoding Lab, Albert-Ludwigs-Universität Freiburg, Freiburg, Germany
- Stereotaxy and Functional Neurosurgery Department, Uniklinik Freiburg, Freiburg, Germany
| | - Catarina Lopes-Dias
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Lea Hehenberger
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Valeria Mondini
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Víctor Martínez-Cagigal
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Valladolid, Spain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Valladolid, Spain
| | | | - Hannah Pulferer
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Luka Batistić
- Faculty of Engineering, Department of Computer Engineering, University of Rijeka, Rijeka, Croatia
| | - Andreea I. Sburlea
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| |
Collapse
|
32
|
Khalil K, Asgher U, Ayaz Y. Novel fNIRS study on homogeneous symmetric feature-based transfer learning for brain-computer interface. Sci Rep 2022; 12:3198. [PMID: 35210460 PMCID: PMC8873341 DOI: 10.1038/s41598-022-06805-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 01/04/2022] [Indexed: 01/23/2023] Open
Abstract
The brain-computer interface (BCI) provides an alternate means of communication between the brain and external devices by recognizing the brain activities and translating them into external commands. The functional Near-Infrared Spectroscopy (fNIRS) is becoming popular as a non-invasive modality for brain activity detection. The recent trends show that deep learning has significantly enhanced the performance of the BCI systems. But the inherent bottleneck for deep learning (in the domain of BCI) is the requirement of the vast amount of training data, lengthy recalibrating time, and expensive computational resources for training deep networks. Building a high-quality, large-scale annotated dataset for deep learning-based BCI systems is exceptionally tedious, complex, and expensive. This study investigates the novel application of transfer learning for fNIRS-based BCI to solve three objective functions (concerns), i.e., the problem of insufficient training data, reduced training time, and increased accuracy. We applied symmetric homogeneous feature-based transfer learning on convolutional neural network (CNN) designed explicitly for fNIRS data collected from twenty-six (26) participants performing the n-back task. The results suggested that the proposed method achieves the maximum saturated accuracy sooner and outperformed the traditional CNN model on averaged accuracy by 25.58% in the exact duration of training time, reducing the training time, recalibrating time, and computational resources.
Collapse
Affiliation(s)
- Khurram Khalil
- National Center of Artificial Intelligence (NCAI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan
| | - Umer Asgher
- National Center of Artificial Intelligence (NCAI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.,Department of Mechatronics Engineering, College of Electrical and Mechanical Engineering, National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan
| | - Yasar Ayaz
- National Center of Artificial Intelligence (NCAI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST), Islamabad, 44000, Pakistan.
| |
Collapse
|
33
|
Gantenbein J, Dittli J, Meyer JT, Gassert R, Lambercy O. Intention Detection Strategies for Robotic Upper-Limb Orthoses: A Scoping Review Considering Usability, Daily Life Application, and User Evaluation. Front Neurorobot 2022; 16:815693. [PMID: 35264940 PMCID: PMC8900616 DOI: 10.3389/fnbot.2022.815693] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/24/2022] [Indexed: 11/13/2022] Open
Abstract
Wearable robotic upper limb orthoses (ULO) are promising tools to assist or enhance the upper-limb function of their users. While the functionality of these devices has continuously increased, the robust and reliable detection of the user's intention to control the available degrees of freedom remains a major challenge and a barrier for acceptance. As the information interface between device and user, the intention detection strategy (IDS) has a crucial impact on the usability of the overall device. Yet, this aspect and the impact it has on the device usability is only rarely evaluated with respect to the context of use of ULO. A scoping literature review was conducted to identify non-invasive IDS applied to ULO that have been evaluated with human participants, with a specific focus on evaluation methods and findings related to functionality and usability and their appropriateness for specific contexts of use in daily life. A total of 93 studies were identified, describing 29 different IDS that are summarized and classified according to a four-level classification scheme. The predominant user input signal associated with the described IDS was electromyography (35.6%), followed by manual triggers such as buttons, touchscreens or joysticks (16.7%), as well as isometric force generated by residual movement in upper-limb segments (15.1%). We identify and discuss the strengths and weaknesses of IDS with respect to specific contexts of use and highlight a trade-off between performance and complexity in selecting an optimal IDS. Investigating evaluation practices to study the usability of IDS, the included studies revealed that, primarily, objective and quantitative usability attributes related to effectiveness or efficiency were assessed. Further, it underlined the lack of a systematic way to determine whether the usability of an IDS is sufficiently high to be appropriate for use in daily life applications. This work highlights the importance of a user- and application-specific selection and evaluation of non-invasive IDS for ULO. For technology developers in the field, it further provides recommendations on the selection process of IDS as well as to the design of corresponding evaluation protocols.
Collapse
Affiliation(s)
- Jessica Gantenbein
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Jan Dittli
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Jan Thomas Meyer
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
| | - Roger Gassert
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| | - Olivier Lambercy
- Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, ETH Zurich, Zurich, Switzerland
- Future Health Technologies, Singapore-ETH Centre, Campus for Research Excellence and Technological Enterprise (CREATE), Singapore, Singapore
| |
Collapse
|
34
|
Single-Trial Classification of Error-Related Potentials in People with Motor Disabilities: A Study in Cerebral Palsy, Stroke, and Amputees. SENSORS 2022; 22:s22041676. [PMID: 35214576 PMCID: PMC8879227 DOI: 10.3390/s22041676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/16/2022]
Abstract
Brain-computer interface performance may be reduced over time, but adapting the classifier could reduce this problem. Error-related potentials (ErrPs) could label data for continuous adaptation. However, this has scarcely been investigated in populations with severe motor impairments. The aim of this study was to detect ErrPs from single-trial EEG in offline analysis in participants with cerebral palsy, an amputation, or stroke, and determine how much discriminative information different brain regions hold. Ten participants with cerebral palsy, eight with an amputation, and 25 with a stroke attempted to perform 300-400 wrist and ankle movements while a sham BCI provided feedback on their performance for eliciting ErrPs. Pre-processed EEG epochs were inputted in a multi-layer perceptron artificial neural network. Each brain region was used as input individually (Frontal, Central, Temporal Right, Temporal Left, Parietal, and Occipital), the combination of the Central region with each of the adjacent regions, and all regions combined. The Frontal and Central regions were most important, and adding additional regions only improved performance slightly. The average classification accuracies were 84 ± 4%, 87± 4%, and 85 ± 3% for cerebral palsy, amputation, and stroke participants. In conclusion, ErrPs can be detected in participants with motor impairments; this may have implications for developing adaptive BCIs or automatic error correction.
Collapse
|
35
|
Review of tDCS Configurations for Stimulation of the Lower-Limb Area of Motor Cortex and Cerebellum. Brain Sci 2022; 12:brainsci12020248. [PMID: 35204011 PMCID: PMC8870282 DOI: 10.3390/brainsci12020248] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 01/28/2022] [Accepted: 02/01/2022] [Indexed: 11/17/2022] Open
Abstract
This article presents an exhaustive analysis of the works present in the literature pertaining to transcranial direct current stimulation(tDCS) applications. The aim of this work is to analyze the specific characteristics of lower-limb stimulation, identifying the strengths and weaknesses of these works and framing them with the current knowledge of tDCS. The ultimate goal of this work is to propose areas of improvement to create more effective stimulation therapies with less variability.
Collapse
|
36
|
Chang Y, He C, Tsai BY, Ko LW. Multi-Parameter Physiological State Monitoring in Target Detection Under Real-World Settings. Front Hum Neurosci 2021; 15:785562. [PMID: 35002658 PMCID: PMC8727696 DOI: 10.3389/fnhum.2021.785562] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 12/03/2021] [Indexed: 11/13/2022] Open
Abstract
Mental state changes induced by stimuli under experimental settings or by daily events in real life affect task performance and are entwined with physical and mental health. In this study, we developed a physiological state indicator with five parameters that reflect the subject's real-time physiological states based on online EEG signal processing. These five parameters are attention, fatigue, stress, and the brain activity shifts of the left and right hemispheres. We designed a target detection experiment modified by a cognitive attention network test for validating the effectiveness of the proposed indicator, as such conditions would better approximate a real chaotic environment. Results demonstrated that attention levels while performing the target detection task were significantly higher than during rest periods, but also exhibited a decay over time. In contrast, the fatigue level increased gradually and plateaued by the third rest period. Similar to attention levels, the stress level decreased as the experiment proceeded. These parameters are therefore shown to be highly correlated to different stages of the experiment, suggesting their usage as primary factors in passive brain-computer interfaces (BCI). In addition, the left and right brain activity indexes reveal the EEG neural modulations of the corresponding hemispheres, which set a feasible reference of activation for an active BCI control system, such as one executing motor imagery tasks. The proposed indicator is applicable to potential passive and active BCI applications for monitoring the subject's physiological state change in real-time, along with providing a means of evaluating the associated signal quality to enhance the BCI performance.
Collapse
Affiliation(s)
- Yang Chang
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Congying He
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Bo-Yu Tsai
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Li-Wei Ko
- Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B), College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Drug Development and Value Creation Research Center, Kaohsiung Medical University, Kaohsiung City, Taiwan
| |
Collapse
|
37
|
Customizing skills for assistive robotic manipulators, an inverse reinforcement learning approach with error-related potentials. Commun Biol 2021; 4:1406. [PMID: 34916587 PMCID: PMC8677775 DOI: 10.1038/s42003-021-02891-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 11/10/2021] [Indexed: 11/09/2022] Open
Abstract
Robotic assistance via motorized robotic arm manipulators can be of valuable assistance to individuals with upper-limb motor disabilities. Brain-computer interfaces (BCI) offer an intuitive means to control such assistive robotic manipulators. However, BCI performance may vary due to the non-stationary nature of the electroencephalogram (EEG) signals. It, hence, cannot be used safely for controlling tasks where errors may be detrimental to the user. Avoiding obstacles is one such task. As there exist many techniques to avoid obstacles in robotics, we propose to give the control to the robot to avoid obstacles and to leave to the user the choice of the robot behavior to do so a matter of personal preference as some users may be more daring while others more careful. We enable the users to train the robot controller to adapt its way to approach obstacles relying on BCI that detects error-related potentials (ErrP), indicative of the user’s error expectation of the robot’s current strategy to meet their preferences. Gaussian process-based inverse reinforcement learning, in combination with the ErrP-BCI, infers the user’s preference and updates the obstacle avoidance controller so as to generate personalized robot trajectories. We validate the approach in experiments with thirteen able-bodied subjects using a robotic arm that picks up, places and avoids real-life objects. Results show that the algorithm can learn user’s preference and adapt the robot behavior rapidly using less than five demonstrations not necessarily optimal. Teaching an assistive robotic manipulator to move objects in a cluttered table requires demonstrations from expert operators, but what if the experts are individuals with motor disabilities? Batzianoulis et al. propose a learning approach which combines robot autonomy and a brain-computer interfacing that decodes whether the generated trajectories meet the user’s criteria, and show how their system enables the robot to learn individual user’s preferred behaviors using less than five demonstrations that are not necessarily optimal.
Collapse
|
38
|
Nur Chowdhury MS, Dutta A, Robison MK, Blais C, Brewer G, Bliss DW. 3D CNN to Estimate Reaction Time from Multi-Channel EEG. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:5932-5935. [PMID: 34892469 DOI: 10.1109/embc46164.2021.9630748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The study of human reaction time (RT) is invaluable not only to understand the sensory-motor functions but also to translate brain signals into machine comprehensible commands that can facilitate augmentative and alternative communication using brain-computer interfaces (BCI). Recent developments in sensor technologies, hardware computational capabilities, and neural network models have significantly helped advance biomedical signal processing research. This study is an attempt to utilize state-of-the-art resources to explore the relationship between human behavioral responses during perceptual decision-making and corresponding brain signals in the form of electroencephalograms (EEG). In this paper, a generalized 3D convolutional neural network (CNN) architecture is introduced to estimate RT for a simple visual task using single-trial multi-channel EEG. Earlier comparable studies have also employed a number of machine learning and deep learning-based models, but none of them considered inter-channel relationships while estimating RT. On the contrary, the use of 3D convolutional layers enabled us to consider the spatial relationship among adjacent channels while simultaneously utilizing spectral information from individual channels. Our model can predict RT with a root mean square error of 91.5 ms and a correlation coefficient of 0.83. These results surpass all the previous results attained from different studies.Clinical relevance Novel approaches to decode brain signals can facilitate research on brain-computer interfaces (BCIs), psychology, and neuroscience, enabling people to utilize assistive devices by root-causing psychological or neuromuscular disorders.
Collapse
|
39
|
A Review of the Role of Machine Learning Techniques towards Brain–Computer Interface Applications. MACHINE LEARNING AND KNOWLEDGE EXTRACTION 2021. [DOI: 10.3390/make3040042] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
This review article provides a deep insight into the Brain–Computer Interface (BCI) and the application of Machine Learning (ML) technology in BCIs. It investigates the various types of research undertaken in this realm and discusses the role played by ML in performing different BCI tasks. It also reviews the ML methods used for mental state detection, mental task categorization, emotion classification, electroencephalogram (EEG) signal classification, event-related potential (ERP) signal classification, motor imagery categorization, and limb movement classification. This work explores the various methods employed in BCI mechanisms for feature extraction, selection, and classification and provides a comparative study of reviewed methods. This paper assists the readers to gain information regarding the developments made in BCI and ML domains and future improvements needed for improving and designing better BCI applications.
Collapse
|
40
|
Olsen S, Alder G, Williams M, Chambers S, Jochumsen M, Signal N, Rashid U, Niazi IK, Taylor D. Electroencephalographic Recording of the Movement-Related Cortical Potential in Ecologically Valid Movements: A Scoping Review. Front Neurosci 2021; 15:721387. [PMID: 34650399 PMCID: PMC8505671 DOI: 10.3389/fnins.2021.721387] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Accepted: 08/27/2021] [Indexed: 12/05/2022] Open
Abstract
The movement-related cortical potential (MRCP) is a brain signal that can be recorded using surface electroencephalography (EEG) and represents the cortical processes involved in movement preparation. The MRCP has been widely researched in simple, single-joint movements, however, these movements often lack ecological validity. Ecological validity refers to the generalizability of the findings to real-world situations, such as neurological rehabilitation. This scoping review aimed to synthesize the research evidence investigating the MRCP in ecologically valid movement tasks. A search of six electronic databases identified 102 studies that investigated the MRCP during multi-joint movements; 59 of these studies investigated ecologically valid movement tasks and were included in the review. The included studies investigated 15 different movement tasks that were applicable to everyday situations, but these were largely carried out in healthy populations. The synthesized findings suggest that the recording and analysis of MRCP signals is possible in ecologically valid movements, however the characteristics of the signal appear to vary across different movement tasks (i.e., those with greater complexity, increased cognitive load, or a secondary motor task) and different populations (i.e., expert performers, people with Parkinson’s Disease, and older adults). The scarcity of research in clinical populations highlights the need for further research in people with neurological and age-related conditions to progress our understanding of the MRCPs characteristics and to determine its potential as a measure of neurological recovery and intervention efficacy. MRCP-based neuromodulatory interventions applied during ecologically valid movements were only represented in one study in this review as these have been largely delivered during simple joint movements. No studies were identified that used ecologically valid movements to control BCI-driven external devices; this may reflect the technical challenges associated with accurately classifying functional movements from MRCPs. Future research investigating MRCP-based interventions should use movement tasks that are functionally relevant to everyday situations. This will facilitate the application of this knowledge into the rehabilitation setting.
Collapse
Affiliation(s)
- Sharon Olsen
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Gemma Alder
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Mitra Williams
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Seth Chambers
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Mads Jochumsen
- Department of Health Science and Technology, Aalborg University, Aalborg, Denmark
| | - Nada Signal
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| | - Usman Rashid
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand.,Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
| | - Imran Khan Niazi
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand.,Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.,Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland, New Zealand
| | - Denise Taylor
- Rehabilitation Innovation Centre, Health and Rehabilitation Research Institute, Auckland University of Technology, Auckland, New Zealand
| |
Collapse
|
41
|
Usama N, Niazi IK, Dremstrup K, Jochumsen M. Detection of Error-Related Potentials in Stroke Patients from EEG Using an Artificial Neural Network. SENSORS 2021; 21:s21186274. [PMID: 34577481 PMCID: PMC8472485 DOI: 10.3390/s21186274] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/07/2021] [Revised: 09/14/2021] [Accepted: 09/17/2021] [Indexed: 11/26/2022]
Abstract
Error-related potentials (ErrPs) have been proposed as a means for improving brain-computer interface (BCI) performance by either correcting an incorrect action performed by the BCI or label data for continuous adaptation of the BCI to improve the performance. The latter approach could be relevant within stroke rehabilitation where BCI calibration time could be minimized by using a generalized classifier that is continuously being individualized throughout the rehabilitation session. This may be achieved if data are correctly labelled. Therefore, the aims of this study were: (1) classify single-trial ErrPs produced by individuals with stroke, (2) investigate test-retest reliability, and (3) compare different classifier calibration schemes with different classification methods (artificial neural network, ANN, and linear discriminant analysis, LDA) with waveform features as input for meaningful physiological interpretability. Twenty-five individuals with stroke operated a sham BCI on two separate days where they attempted to perform a movement after which they received feedback (error/correct) while continuous EEG was recorded. The EEG was divided into epochs: ErrPs and NonErrPs. The epochs were classified with a multi-layer perceptron ANN based on temporal features or the entire epoch. Additionally, the features were classified with shrinkage LDA. The features were waveforms of the ErrPs and NonErrPs from the sensorimotor cortex to improve the explainability and interpretation of the output of the classifiers. Three calibration schemes were tested: within-day, between-day, and across-participant. Using within-day calibration, 90% of the data were correctly classified with the entire epoch as input to the ANN; it decreased to 86% and 69% when using temporal features as input to ANN and LDA, respectively. There was poor test-retest reliability between the two days, and the other calibration schemes led to accuracies in the range of 63-72% with LDA performing the best. There was no association between the individuals' impairment level and classification accuracies. The results show that ErrPs can be classified in individuals with stroke, but that user- and session-specific calibration is needed for optimal ErrP decoding with this approach. The use of ErrP/NonErrP waveform features makes it possible to have a physiological meaningful interpretation of the output of the classifiers. The results may have implications for labelling data continuously in BCIs for stroke rehabilitation and thus potentially improve the BCI performance.
Collapse
Affiliation(s)
- Nayab Usama
- Department of Health Science and Technology, Aalborg University, 9000 Aalborg, Denmark; (N.U.); (K.D.); (M.J.)
| | - Imran Khan Niazi
- Department of Health Science and Technology, Aalborg University, 9000 Aalborg, Denmark; (N.U.); (K.D.); (M.J.)
- Centre for Chiropractic Research, New Zealand College of Chiropractic, Auckland 1060, New Zealand
- Health and Rehabilitation Research Institute, AUT University, Auckland 0627, New Zealand
- Correspondence: ; Tel.: +64-9-526-6789
| | - Kim Dremstrup
- Department of Health Science and Technology, Aalborg University, 9000 Aalborg, Denmark; (N.U.); (K.D.); (M.J.)
| | - Mads Jochumsen
- Department of Health Science and Technology, Aalborg University, 9000 Aalborg, Denmark; (N.U.); (K.D.); (M.J.)
| |
Collapse
|
42
|
BioPyC, an Open-Source Python Toolbox for Offline Electroencephalographic and Physiological Signals Classification. SENSORS 2021; 21:s21175740. [PMID: 34502629 PMCID: PMC8433891 DOI: 10.3390/s21175740] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/09/2021] [Accepted: 08/20/2021] [Indexed: 11/17/2022]
Abstract
Research on brain–computer interfaces (BCIs) has become more democratic in recent decades, and experiments using electroencephalography (EEG)-based BCIs has dramatically increased. The variety of protocol designs and the growing interest in physiological computing require parallel improvements in processing and classification of both EEG signals and bio signals, such as electrodermal activity (EDA), heart rate (HR) or breathing. If some EEG-based analysis tools are already available for online BCIs with a number of online BCI platforms (e.g., BCI2000 or OpenViBE), it remains crucial to perform offline analyses in order to design, select, tune, validate and test algorithms before using them online. Moreover, studying and comparing those algorithms usually requires expertise in programming, signal processing and machine learning, whereas numerous BCI researchers come from other backgrounds with limited or no training in such skills. Finally, existing BCI toolboxes are focused on EEG and other brain signals but usually do not include processing tools for other bio signals. Therefore, in this paper, we describe BioPyC, a free, open-source and easy-to-use Python platform for offline EEG and biosignal processing and classification. Based on an intuitive and well-guided graphical interface, four main modules allow the user to follow the standard steps of the BCI process without any programming skills: (1) reading different neurophysiological signal data formats, (2) filtering and representing EEG and bio signals, (3) classifying them, and (4) visualizing and performing statistical tests on the results. We illustrate BioPyC use on four studies, namely classifying mental tasks, the cognitive workload, emotions and attention states from EEG signals.
Collapse
|
43
|
Min BK, Kim HS, Ko W, Ahn MH, Suk HI, Pantazis D, Knight RT. Electrophysiological Decoding of Spatial and Color Processing in Human Prefrontal Cortex. Neuroimage 2021; 237:118165. [PMID: 34000400 PMCID: PMC8344402 DOI: 10.1016/j.neuroimage.2021.118165] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/30/2021] [Accepted: 05/11/2021] [Indexed: 11/16/2022] Open
Abstract
The prefrontal cortex (PFC) plays a pivotal role in goal-directed cognition, yet its representational code remains an open problem with decoding techniques ineffective in disentangling task-relevant variables from PFC. Here we applied regularized linear discriminant analysis to human scalp EEG data and were able to distinguish a mental-rotation task versus a color-perception task with 87% decoding accuracy. Dorsal and ventral areas in lateral PFC provided the dominant features dissociating the two tasks. Our findings show that EEG can reliably decode two independent task states from PFC and emphasize the PFC dorsal/ventral functional specificity in processing the where rotation task versus the what color task.
Collapse
Affiliation(s)
- Byoung-Kyong Min
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea; Department of Artificial Intelligence, Korea University, Seoul 02841, Korea.
| | - Hyun-Seok Kim
- Biomedical Engineering Research Center, Asan Institute of Life Science, Asan Medical Center, Seoul 05505, Korea
| | - Wonjun Ko
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea
| | - Min-Hee Ahn
- Laboratory of Brain and Cognitive Science for Convergence Medicine, College of Medicine, Hallym University, Anyang 14068, Korea
| | - Heung-Il Suk
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Korea; Department of Artificial Intelligence, Korea University, Seoul 02841, Korea
| | - Dimitrios Pantazis
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Robert T Knight
- Department of Psychology, Helen Wills Neuroscience Institute, UC Berkeley, Berkeley, CA 94720, USA
| |
Collapse
|
44
|
Barban F, Chiappalone M, Bonassi G, Mantini D, Semprini M. Yet another artefact rejection study: an exploration of cleaning methods for biological and neuromodulatory noise. J Neural Eng 2021; 18. [PMID: 34342270 DOI: 10.1088/1741-2552/ac01fe] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 05/17/2021] [Indexed: 02/02/2023]
Abstract
Objective. Electroencephalography (EEG) cleaning has been a longstanding issue in the research community. In recent times, huge leaps have been made in the field, resulting in very promising techniques to address the issue. The most widespread ones rely on a family of mathematical methods known as blind source separation (BSS), ideally capable of separating artefactual signals from the brain originated ones. However, corruption of EEG data still remains a problem, especially in real life scenario where a mixture of artefact components affects the signal and thus correctly choosing the correct cleaning procedure can be non trivial. Our aim is here to evaluate and score the plethora of available BSS-based cleaning methods, providing an overview of their advantages and downsides and of their best field of application.Approach. To address this, we here first characterized and modeled different types of artefact, i.e. arising from muscular or blinking activity as well as from transcranial alternate current stimulation. We then tested and scored several BSS-based cleaning procedures on semi-synthetic datasets corrupted by the previously modeled noise sources. Finally, we built a lifelike dataset affected by many artefactual components. We tested an iterative multistep approach combining different BSS steps, aimed at sequentially removing each specific artefactual component.Main results. We did not find an overall best method, as different scenarios require different approaches. We therefore provided an overview of the performance in terms of both reconstruction accuracy and computational burden of each method in different use cases.Significance. Our work provides insightful guidelines for signal cleaning procedures in the EEG related field.
Collapse
Affiliation(s)
- Federico Barban
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genoa, Italy
| | - Michela Chiappalone
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genoa, Italy.,Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, 16145 Genoa, Italy
| | - Gaia Bonassi
- S.C. Medicina Fisica e Riabilitazione Ospedaliera, ASL 4, Azienda Sanitaria Locale Chiavarese, 16034 Chiavari Genoa, Italy
| | - Dante Mantini
- Research Center for Motor Control and Neuroplasticity, KU Leuven, 3001 Leuven, Belgium.,Brain Imaging and Neural Dynamics Research Group, IRCCS San Camillo Hospital, 30126 Venice, Italy
| | - Marianna Semprini
- Rehab Technologies, Istituto Italiano di Tecnologia, 16163 Genoa, Italy
| |
Collapse
|
45
|
Vidaurre C, Jorajuría T, Ramos-Murguialday A, Müller KR, Gómez M, Nikulin VV. Improving motor imagery classification during induced motor perturbations. J Neural Eng 2021; 18. [PMID: 34233305 DOI: 10.1088/1741-2552/ac123f] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 07/07/2021] [Indexed: 11/11/2022]
Abstract
Objective.Motor imagery is the mental simulation of movements. It is a common paradigm to design brain-computer interfaces (BCIs) that elicits the modulation of brain oscillatory activity similar to real, passive and induced movements. In this study, we used peripheral stimulation to provoke movements of one limb during the performance of motor imagery tasks. Unlike other works, in which induced movements are used to support the BCI operation, our goal was to test and improve the robustness of motor imagery based BCI systems to perturbations caused by artificially generated movements.Approach.We performed a BCI session with ten participants who carried out motor imagery of three limbs. In some of the trials, one of the arms was moved by neuromuscular stimulation. We analysed 2-class motor imagery classifications with and without movement perturbations. We investigated the performance decrease produced by these disturbances and designed different computational strategies to attenuate the observed classification accuracy drop.Main results.When the movement was induced in a limb not coincident with the motor imagery classes, extracting oscillatory sources of the movement imagination tasks resulted in BCI performance being similar to the control (undisturbed) condition; when the movement was induced in a limb also involved in the motor imagery tasks, the performance drop was significantly alleviated by spatially filtering out the neural noise caused by the stimulation. We also show that the loss of BCI accuracy was accompanied by weaker power of the sensorimotor rhythm. Importantly, this residual power could be used to predict whether a BCI user will perform with sufficient accuracy under the movement disturbances.Significance.We provide methods to ameliorate and even eliminate motor related afferent disturbances during the performance of motor imagery tasks. This can help improving the reliability of current motor imagery based BCI systems.
Collapse
Affiliation(s)
- C Vidaurre
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.,Machine Learning Group, Computer Science Faculty, Berlin Institute of Technology, Berlin, Germany.,Both authors contributed equally
| | - T Jorajuría
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.,Both authors contributed equally
| | - A Ramos-Murguialday
- Institute for Medical Psychology and Behavioral Neurobiology (IMP), University of Tübingen, 72076 Tübingen, Germany.,Neurotechnology Laboratory, TECNALIA, Basque Research and Technology Alliance (BRTA), Donostia-San Sebastián, Spain
| | - K-R Müller
- Machine Learning Group, Computer Science Faculty, Berlin Institute of Technology, Berlin, Germany.,BIFOLD Berlin Institute for the Foundations of Learning and Data, Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany.,Department of Artificial Intelligence, Korea University, Seoul, Republic of Korea.,Max Planck Institute for Informatics, Saarbrücken, Germany
| | - M Gómez
- Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain
| | - V V Nikulin
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Centre for Cognition and Decision Making, Institute for Cognitive Neuroscience, National Research University Higher School of Economics, Moscow, Russia
| |
Collapse
|
46
|
A Bibliometric Analysis of Human-Machine Interaction Methodology for Electric-Powered Wheelchairs Driving from 1998 to 2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18147567. [PMID: 34300017 PMCID: PMC8304937 DOI: 10.3390/ijerph18147567] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Revised: 05/17/2021] [Accepted: 07/10/2021] [Indexed: 11/17/2022]
Abstract
Electric power wheelchairs (EPWs) enhance the mobility capability of the elderly and the disabled, while the human-machine interaction (HMI) determines how well the human intention will be precisely delivered and how human-machine system cooperation will be efficiently conducted. A bibliometric quantitative analysis of 1154 publications related to this research field, published between 1998 and 2020, was conducted. We identified the development status, contributors, hot topics, and potential future research directions of this field. We believe that the combination of intelligence and humanization of an EPW HMI system based on human-machine collaboration is an emerging trend in EPW HMI methodology research. Particular attention should be paid to evaluating the applicability and benefits of the EPW HMI methodology for the users, as well as how much it contributes to society. This study offers researchers a comprehensive understanding of EPW HMI studies in the past 22 years and latest trends from the evolutionary footprints and forward-thinking insights regarding future research.
Collapse
|
47
|
Pereira J, Kobler R, Ofner P, Schwarz A, Müller-Putz GR. Online detection of movement during natural and self-initiated reach-and-grasp actions from EEG signals. J Neural Eng 2021; 18. [PMID: 34130267 DOI: 10.1088/1741-2552/ac0b52] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 06/15/2021] [Indexed: 11/11/2022]
Abstract
Movement intention detection using electroencephalography (EEG) is a challenging but essential component of brain-computer interfaces (BCIs) for people with motor disabilities.Objective.The goal of this study is to develop a new experimental paradigm to perform asynchronous online detection of movement based on low-frequency time-domain EEG features, concretely on movement-related cortical potentials. The paradigm must be easily transferable to people without any residual upper-limb movement function and the BCI must be independent of upper-limb movement onset measurements and external cues.Approach. In a study with non-disabled participants, we evaluated a novel BCI paradigm to detect self-initiated reach-and-grasp movements. Two experimental conditions were involved. In one condition, participants performed reach-and-grasp movements to a target and simultaneously shifted their gaze towards it. In a control condition, participants solely shifted their gaze towards the target (oculomotor task). The participants freely decided when to initiate the tasks. After eye artefact correction, the EEG signals were time-locked to the saccade onset and the resulting amplitude features were exploited on a hierarchical classification approach to detect movement asynchronously.Main results. With regards to BCI performance, 54.1% (14.4% SD) of the movements were correctly identified, and all participants achieved a performance above chance-level (around 12%). An average of 21.5% (14.1% SD) of the oculomotor tasks were falsely detected as upper-limb movement. In an additional rest condition, 1.7 (1.6 SD) false positives per minute were measured. Through source imaging, movement information was mapped to sensorimotor, posterior parietal and occipital areas.Significance. We present a novel approach for movement detection using EEG signals which does not rely on upper-limb movement onset measurements or on the presentation of external cues. The participants' behaviour closely matches the natural behaviour during goal-directed reach-and-grasp movements, which also constitutes an advantage with respect to current BCI protocols.
Collapse
Affiliation(s)
- Joana Pereira
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Reinmar Kobler
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Patrick Ofner
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | - Andreas Schwarz
- Institute of Neural Engineering, Graz University of Technology, Graz, Austria
| | | |
Collapse
|
48
|
Decoding the torque of lower limb joints from EEG recordings of pre-gait movements using a machine learning scheme. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.03.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
|
49
|
Savić AM, Aliakbaryhosseinabadi S, Blicher JU, Farina D, Mrachacz-Kersting N, Došen S. Online control of an assistive active glove by slow cortical signals in patients with amyotrophic lateral sclerosis. J Neural Eng 2021; 18. [PMID: 34030137 DOI: 10.1088/1741-2552/ac0488] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 05/24/2021] [Indexed: 02/08/2023]
Abstract
Objective.A brain-computer interface (BCI) allows users to control external devices using brain signals that can be recorded non-invasively via electroencephalography (EEG). Movement related cortical potentials (MRCPs) are an attractive option for BCI control since they arise naturally during movement execution and imagination, and therefore, do not require an extensive training. This study tested the feasibility of online detection of reaching and grasping using MRCPs for the application in patients suffering from amyotrophic lateral sclerosis (ALS).Approach.A BCI system was developed to trigger closing of a soft assistive glove by detecting a reaching movement. The custom-made software application included data collection, a novel method for collecting the input data for classifier training from the offline recordings based on a sliding window approach, and online control of the glove. Eight healthy subjects and two ALS patients were recruited to test the developed BCI system. They performed assessment blocks without the glove active (NG), in which the movement detection was indicated by a sound feedback, and blocks (G) in which the glove was controlled by the BCI system. The true positive rate (TPR) and the positive predictive value (PPV) were adopted as the outcome measures. Correlation analysis between forehead EEG detecting ocular artifacts and sensorimotor area EEG was conducted to confirm the validity of the results.Main results.The overall median TPR and PPV were >0.75 for online BCI control, in both healthy individuals and patients, with no significant difference across the blocks (NG versus G).Significance.The results demonstrate that cortical activity during reaching can be detected and used to control an external system with a limited amount of training data (30 trials). The developed BCI system can be used to provide grasping assistance to ALS patients.
Collapse
Affiliation(s)
- Andrej M Savić
- Science and Research Centre, University of Belgrade-School of Electrical Engineering, Belgrade, Serbia
| | | | - Jakob U Blicher
- Department of Neurology, Aarhus University Hospital, Aarhus, Denmark.,Center of Functionally Integrative Neuroscience, Aarhus University, Aarhus, Denmark
| | - Dario Farina
- Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Natalie Mrachacz-Kersting
- Department of Information Technology, University of Applied Sciences and Arts Dortmund, Dortmund, Germany.,Institut für Sport und Sportwissenschaft, Albert-Ludwigs Universität Freiburg, Freiburg, Germany
| | - Strahinja Došen
- Department of Health Science and Technology, The Faculty of Medicine, Aalborg University, Aalborg, Denmark
| |
Collapse
|
50
|
Velasco-Álvarez F, Fernández-Rodríguez Á, Vizcaíno-Martín FJ, Díaz-Estrella A, Ron-Angevin R. Brain-Computer Interface (BCI) Control of a Virtual Assistant in a Smartphone to Manage Messaging Applications. SENSORS (BASEL, SWITZERLAND) 2021; 21:3716. [PMID: 34073602 PMCID: PMC8199460 DOI: 10.3390/s21113716] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/21/2021] [Accepted: 05/25/2021] [Indexed: 12/13/2022]
Abstract
Brain-computer interfaces (BCI) are a type of assistive technology that uses the brain signals of users to establish a communication and control channel between them and an external device. BCI systems may be a suitable tool to restore communication skills in severely motor-disabled patients, as BCI do not rely on muscular control. The loss of communication is one of the most negative consequences reported by such patients. This paper presents a BCI system focused on the control of four mainstream messaging applications running in a smartphone: WhatsApp, Telegram, e-mail and short message service (SMS). The control of the BCI is achieved through the well-known visual P300 row-column paradigm (RCP), allowing the user to select control commands as well as spelling characters. For the control of the smartphone, the system sends synthesized voice commands that are interpreted by a virtual assistant running in the smartphone. Four tasks related to the four mentioned messaging services were tested with 15 healthy volunteers, most of whom were able to accomplish the tasks, which included sending free text e-mails to an address proposed by the subjects themselves. The online performance results obtained, as well as the results of subjective questionnaires, support the viability of the proposed system.
Collapse
Affiliation(s)
- Francisco Velasco-Álvarez
- Departamento de Tecnología Electrónica, Universidad de Málaga, 29071 Málaga, Spain; (Á.F.-R.); (F.-J.V.-M.); (A.D.-E.); (R.R.-A.)
| | | | | | | | | |
Collapse
|