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Portillo-Lara R, Tahirbegi B, Chapman CAR, Goding JA, Green RA. Mind the gap: State-of-the-art technologies and applications for EEG-based brain-computer interfaces. APL Bioeng 2021; 5:031507. [PMID: 34327294 PMCID: PMC8294859 DOI: 10.1063/5.0047237] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Accepted: 05/19/2021] [Indexed: 11/14/2022] Open
Abstract
Brain-computer interfaces (BCIs) provide bidirectional communication between the brain and output devices that translate user intent into function. Among the different brain imaging techniques used to operate BCIs, electroencephalography (EEG) constitutes the preferred method of choice, owing to its relative low cost, ease of use, high temporal resolution, and noninvasiveness. In recent years, significant progress in wearable technologies and computational intelligence has greatly enhanced the performance and capabilities of EEG-based BCIs (eBCIs) and propelled their migration out of the laboratory and into real-world environments. This rapid translation constitutes a paradigm shift in human-machine interaction that will deeply transform different industries in the near future, including healthcare and wellbeing, entertainment, security, education, and marketing. In this contribution, the state-of-the-art in wearable biosensing is reviewed, focusing on the development of novel electrode interfaces for long term and noninvasive EEG monitoring. Commercially available EEG platforms are surveyed, and a comparative analysis is presented based on the benefits and limitations they provide for eBCI development. Emerging applications in neuroscientific research and future trends related to the widespread implementation of eBCIs for medical and nonmedical uses are discussed. Finally, a commentary on the ethical, social, and legal concerns associated with this increasingly ubiquitous technology is provided, as well as general recommendations to address key issues related to mainstream consumer adoption.
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Affiliation(s)
- Roberto Portillo-Lara
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
| | - Bogachan Tahirbegi
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
| | - Christopher A. R. Chapman
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
| | - Josef A. Goding
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
| | - Rylie A. Green
- Department of Bioengineering, Imperial College London, Royal School of Mines, London SW7 2AZ, United Kingdom
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A review of electroencephalogram signal processing methods for brain-controlled robots. COGNITIVE ROBOTICS 2021. [DOI: 10.1016/j.cogr.2021.07.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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A comprehensive assessment of Brain Computer Interfaces: Recent trends and challenges. J Neurosci Methods 2020; 346:108918. [PMID: 32853592 DOI: 10.1016/j.jneumeth.2020.108918] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 07/15/2020] [Accepted: 08/19/2020] [Indexed: 12/23/2022]
Abstract
BACKGROUND An uninterrupted channel of communication and control between the human brain and electronic processing units has led to an increased use of Brain Computer Interfaces (BCIs). This article attempts to present an all-encompassing review on BCI and the scientific advancements associated with it. The ultimate goal of this review is to provide a general overview of the BCI technology and to shed light on different aspects of BCIs. This review also underscores the applications, practical challenges and opportunities associated with BCI technology, which can be used to accelerate future developments in this field. METHODS This review is based on a systematic literature search for tracking down the relevant research annals and proceedings. Using a methodical search strategy, the search was carried out across major technical databases. The retrieved records were screened for their relevance and a total of 369 research chronicles were engulfed in this review based on the inclusion criteria. RESULTS This review describes the present scenario and recent advancements in BCI technology. It also identifies several application areas of BCI technology. This comprehensive review provides evidence that, while we are getting ever closer, significant challenges still exist for the development of BCIs that can seamlessly integrate with the user's biological system. CONCLUSION The findings of this review confirm the importance of BCI technology in various applications. It is concluded that BCI technology, still in its sprouting phase, requires significant explorations for further development.
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A space-frequency localized approach of spatial filtering for motor imagery classification. Health Inf Sci Syst 2020; 8:15. [PMID: 32257126 DOI: 10.1007/s13755-020-00106-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 03/13/2020] [Indexed: 01/22/2023] Open
Abstract
Classification of Motor Imagery (MI) signals is the heart of Brain-Computer Interface (BCI) based applications. Spatial filtering is an important step in this process that produce new set of signals for better discrimination of two classes of EEG signals. In this work, a new approach of spatial filtering called Space-Frequency Localized Spatial Filtering (SFLSF) is proposed to enhance the performances of MI classification. The SFLSF method initially divides the scalp-EEG channels into local overlapping spatial windows. Then a filter bank is used to divide the signals into local frequency bands. The group of channels, localized in space and frequency, are then processed with spatial filter, and features are subsequently extracted for classification task. Experimental results corroborate that the proposed space localization helps to increase the classification accuracy when compared to the existing methods using spatial filters. The classification performance is further improved when frequency localization is incorporated. Thus, the proposed space-frequency localized approach of spatial filtering helps to deliver better classification result which is consistently 3-5% higher than traditional methods.
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Iturrate I, Chavarriaga R, Millán JDR. General principles of machine learning for brain-computer interfacing. HANDBOOK OF CLINICAL NEUROLOGY 2020; 168:311-328. [PMID: 32164862 DOI: 10.1016/b978-0-444-63934-9.00023-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Brain-computer interfaces (BCIs) are systems that translate brain activity patterns into commands that can be executed by an artificial device. This enables the possibility of controlling devices such as a prosthetic arm or exoskeleton, a wheelchair, typewriting applications, or games directly by modulating our brain activity. For this purpose, BCI systems rely on signal processing and machine learning algorithms to decode the brain activity. This chapter provides an overview of the main steps required to do such a process, including signal preprocessing, feature extraction and selection, and decoding. Given the large amount of possible methods that can be used for these processes, a comprehensive review of them is beyond the scope of this chapter, and it is focused instead on the general principles that should be taken into account, as well as discussing good practices on how these methods should be applied and evaluated for proper design of reliable BCI systems.
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Affiliation(s)
- Iñaki Iturrate
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Ricardo Chavarriaga
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland; Institute of Applied Information Technology (InIT), Zurich University of Applied Sciences ZHAW, Winterthur, Switzerland.
| | - José Del R Millán
- Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, United States; Department of Neurology, The University of Texas at Austin, Austin, TX, United States
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Volkova K, Lebedev MA, Kaplan A, Ossadtchi A. Decoding Movement From Electrocorticographic Activity: A Review. Front Neuroinform 2019; 13:74. [PMID: 31849632 PMCID: PMC6901702 DOI: 10.3389/fninf.2019.00074] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/14/2019] [Indexed: 01/08/2023] Open
Abstract
Electrocorticography (ECoG) holds promise to provide efficient neuroprosthetic solutions for people suffering from neurological disabilities. This recording technique combines adequate temporal and spatial resolution with the lower risks of medical complications compared to the other invasive methods. ECoG is routinely used in clinical practice for preoperative cortical mapping in epileptic patients. During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of different complexity. Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings. Here we review the evolution of this field and its recent tendencies, and discuss the potential areas for future development.
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Affiliation(s)
- Ksenia Volkova
- Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia
| | - Mikhail A. Lebedev
- Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia
| | - Alexander Kaplan
- Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia
- Center for Biotechnology Development, National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
- Laboratory for Neurophysiology and Neuro-Computer Interfaces, Faculty of Biology, Lomonosov Moscow State University, Moscow, Russia
| | - Alexei Ossadtchi
- Center for Bioelectric Interfaces, Higher School of Economics, National Research University, Moscow, Russia
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Liu D, Chen W, Chavarriaga R, Pei Z, Millán JDR. Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors. Front Hum Neurosci 2017; 11:560. [PMID: 29218004 PMCID: PMC5703734 DOI: 10.3389/fnhum.2017.00560] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2017] [Accepted: 11/06/2017] [Indexed: 12/31/2022] Open
Abstract
Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on the detection of upper-limb movement intention, while the fewer study has investigated the lower-limb movement intention decoding. This study presents a BMI to decode the self-paced lower-limb movement intention, with 10 healthy subjects participating in the experiment. We varied four influence factors including the movement type (dorsiflexion or plantar flexion), the limb side (left or right leg), the processing method (time-series analysis based on MRCP, i.e., movement-related cortical potential or frequency-domain estimation based on SMR, i.e., sensory motor rhythm) and the frequency band (e.g., delta, theta, mu, beta and MRCP band at [0.1 1] Hz), to estimate both single-trial and sample-based performance. Feature analysis was then conducted to show the discriminant power (DP) and brain modulations. The average detection latency was -0.334 ± 0.216 s in single-trial basis across all conditions. An average area under the curve (AUC) of 91.0 ± 3.5% and 68.2 ± 4.6% was obtained for the MRCP-based and SMR-based method in the classification, respectively. The best performance was yielded from plantar flexion with left leg using time-series analysis on the MRCP band. The feature analysis indicated a cross-subject consistency of DP with the MRCP-based method and subject-specific variance of DP with the SMR-based method. The results presented here might be further exploited in a rehabilitation scenario. The comprehensive factor analysis might be used to shed light on the design of an effective brain switch to trigger external robotic devices.
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Affiliation(s)
- Dong Liu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China.,Defitech Chair in Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Weihai Chen
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - Ricardo Chavarriaga
- Defitech Chair in Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
| | - Zhongcai Pei
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, China
| | - José Del R Millán
- Defitech Chair in Brain-Machine Interface, École Polytechnique Fédérale de Lausanne, Geneva, Switzerland
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A Review of Techniques for Detection of Movement Intention Using Movement-Related Cortical Potentials. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:346217. [PMID: 26881008 PMCID: PMC4735988 DOI: 10.1155/2015/346217] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Revised: 11/23/2015] [Accepted: 12/02/2015] [Indexed: 11/17/2022]
Abstract
The movement-related cortical potential (MRCP) is a low-frequency negative shift in the electroencephalography (EEG) recording that takes place about 2 seconds prior to voluntary movement production. MRCP replicates the cortical processes employed in planning and preparation of movement. In this study, we recapitulate the features such as signal's acquisition, processing, and enhancement and different electrode montages used for EEG data recoding from different studies that used MRCPs to predict the upcoming real or imaginary movement. An authentic identification of human movement intention, accompanying the knowledge of the limb engaged in the performance and its direction of movement, has a potential implication in the control of external devices. This information could be helpful in development of a proficient patient-driven rehabilitation tool based on brain-computer interfaces (BCIs). Such a BCI paradigm with shorter response time appears more natural to the amputees and can also induce plasticity in brain. Along with different training schedules, this can lead to restoration of motor control in stroke patients.
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Jochumsen M, Niazi IK, Dremstrup K, Kamavuako EN. Detecting and classifying three different hand movement types through electroencephalography recordings for neurorehabilitation. Med Biol Eng Comput 2015; 54:1491-501. [PMID: 26639017 DOI: 10.1007/s11517-015-1421-5] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Accepted: 11/11/2015] [Indexed: 11/28/2022]
Affiliation(s)
- Mads Jochumsen
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Fredrik Bajers vej 7D3, 9220, Aalborg, Denmark.
| | - Imran Khan Niazi
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Fredrik Bajers vej 7D3, 9220, Aalborg, Denmark
- New Zealand College of Chiropractic, Auckland, New Zealand
- Rehabilitation Research Institute, AUT University, Auckland, New Zealand
| | - Kim Dremstrup
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Fredrik Bajers vej 7D3, 9220, Aalborg, Denmark
| | - Ernest Nlandu Kamavuako
- Center for Sensory-Motor Interaction, Department of Health Science and Technology, Aalborg University, Fredrik Bajers vej 7D3, 9220, Aalborg, Denmark
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Comparison of Features for Movement Prediction from Single-Trial Movement-Related Cortical Potentials in Healthy Subjects and Stroke Patients. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015; 2015:858015. [PMID: 26161089 PMCID: PMC4487719 DOI: 10.1155/2015/858015] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Revised: 05/29/2015] [Accepted: 06/01/2015] [Indexed: 11/18/2022]
Abstract
Detection of movement intention from the movement-related cortical potential (MRCP) derived from the electroencephalogram (EEG) signals has shown to be important in combination with assistive devices for effective neurofeedback in rehabilitation. In this study, we compare time and frequency domain features to detect movement intention from EEG signals prior to movement execution. Data were recoded from 24 able-bodied subjects, 12 performing real movements, and 12 performing imaginary movements. Furthermore, six stroke patients with lower limb paresis were included. Temporal and spectral features were investigated in combination with linear discriminant analysis and compared with template matching. The results showed that spectral features were best suited for differentiating between movement intention and noise across different tasks. The ensemble average across tasks when using spectral features was (error = 3.4 ± 0.8%, sensitivity = 97.2 ± 0.9%, and specificity = 97 ± 1%) significantly better (P < 0.01) than temporal features (error = 15 ± 1.4%, sensitivity: 85 ± 1.3%, and specificity: 84 ± 2%). The proposed approach also (error = 3.4 ± 0.8%) outperformed template matching (error = 26.9 ± 2.3%) significantly (P > 0.001). Results imply that frequency information is important for detecting movement intention, which is promising for the application of this approach to provide patient-driven real-time neurofeedback.
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An Accurate, Versatile, and Robust Brain Switch for Neurorehabilitation. SPRINGERBRIEFS IN ELECTRICAL AND COMPUTER ENGINEERING 2014. [DOI: 10.1007/978-3-319-09979-8_5] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Jochumsen M, Niazi IK, Rovsing H, Rovsing C, Nielsen GAR, Andersen TK, Dong NPT, Sørensen ME, Mrachacz-Kersting N, Jiang N, Farina D, Dremstrup K. Detection of Movement Intentions through a Single Channel of Electroencephalography. BIOSYSTEMS & BIOROBOTICS 2014. [DOI: 10.1007/978-3-319-08072-7_69] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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Leal Y, Ruiz M, Lorencio C, Bondia J, Mujica L, Vehi J. Principal component analysis in combination with case-based reasoning for detecting therapeutically correct and incorrect measurements in continuous glucose monitoring systems. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.05.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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Brain Control: Human-computer Integration Control Based on Brain-computer Interface Approach. ACTA ACUST UNITED AC 2013. [DOI: 10.1016/s1874-1029(13)60023-3] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Marchal-Crespo L, Zimmermann R, Lambercy O, Edelmann J, Fluet MC, Wolf M, Gassert R, Riener R. Motor execution detection based on autonomic nervous system responses. Physiol Meas 2012; 34:35-51. [PMID: 23248174 DOI: 10.1088/0967-3334/34/1/35] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Triggered assistance has been shown to be a successful robotic strategy for provoking motor plasticity, probably because it requires neurologic patients' active participation to initiate a movement involving their impaired limb. Triggered assistance, however, requires sufficient residual motor control to activate the trigger and, thus, is not applicable to individuals with severe neurologic injuries. In these situations, brain and body-computer interfaces have emerged as promising solutions to control robotic devices. In this paper, we investigate the feasibility of a body-machine interface to detect motion execution only monitoring the autonomic nervous system (ANS) response. Four physiological signals were measured (blood pressure, breathing rate, skin conductance response and heart rate) during an isometric pinching task and used to train a classifier based on hidden Markov models. We performed an experiment with six healthy subjects to test the effectiveness of the classifier to detect rest and active pinching periods. The results showed that the movement execution can be accurately classified based only on peripheral autonomic signals, with an accuracy level of 84.5%, sensitivity of 83.8% and specificity of 85.2%. These results are encouraging to perform further research on the use of the ANS response in body-machine interfaces.
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Affiliation(s)
- Laura Marchal-Crespo
- Sensory-Motor Systems Laboratory, Institute of Robotics and Intelligent Systems, ETH Zurich, 8006 Zurich, Switzerland.
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Brain computer interfaces, a review. SENSORS 2012; 12:1211-79. [PMID: 22438708 PMCID: PMC3304110 DOI: 10.3390/s120201211] [Citation(s) in RCA: 710] [Impact Index Per Article: 59.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2011] [Revised: 01/16/2012] [Accepted: 01/29/2012] [Indexed: 11/16/2022]
Abstract
A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.
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Niazi IK, Jiang N, Tiberghien O, Nielsen JF, Dremstrup K, Farina D. Detection of movement intention from single-trial movement-related cortical potentials. J Neural Eng 2011; 8:066009. [PMID: 22027549 DOI: 10.1088/1741-2560/8/6/066009] [Citation(s) in RCA: 141] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Detection of movement intention from neural signals combined with assistive technologies may be used for effective neurofeedback in rehabilitation. In order to promote plasticity, a causal relation between intended actions (detected for example from the EEG) and the corresponding feedback should be established. This requires reliable detection of motor intentions. In this study, we propose a method to detect movements from EEG with limited latency. In a self-paced asynchronous BCI paradigm, the initial negative phase of the movement-related cortical potentials (MRCPs), extracted from multi-channel scalp EEG was used to detect motor execution/imagination in healthy subjects and stroke patients. For MRCP detection, it was demonstrated that a new optimized spatial filtering technique led to better accuracy than a large Laplacian spatial filter and common spatial pattern. With the optimized spatial filter, the true positive rate (TPR) for detection of movement execution in healthy subjects (n = 15) was 82.5 ± 7.8%, with latency of -66.6 ± 121 ms. Although TPR decreased with motor imagination in healthy subject (n = 10, 64.5 ± 5.33%) and with attempted movements in stroke patients (n = 5, 55.01 ± 12.01%), the results are promising for the application of this approach to provide patient-driven real-time neurofeedback.
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Eliseyev A, Moro C, Costecalde T, Torres N, Gharbi S, Mestais C, Benabid AL, Aksenova T. IterativeN-way partial least squares for a binary self-paced brain–computer interface in freely moving animals. J Neural Eng 2011; 8:046012. [DOI: 10.1088/1741-2560/8/4/046012] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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