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Detecting the Intention to Move Upper Limbs from Electroencephalographic Brain Signals. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2016; 2016:3195373. [PMID: 27217826 PMCID: PMC4863091 DOI: 10.1155/2016/3195373] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/24/2015] [Accepted: 02/21/2016] [Indexed: 11/30/2022]
Abstract
Early decoding of motor states directly from the brain activity is essential to develop brain-machine interfaces (BMI) for natural motor control of neuroprosthetic devices. Hence, this study aimed to investigate the detection of movement information before the actual movement occurs. This information piece could be useful to provide early control signals to drive BMI-based rehabilitation and motor assisted devices, thus providing a natural and active rehabilitation therapy. In this work, electroencephalographic (EEG) brain signals from six healthy right-handed participants were recorded during self-initiated reaching movements of the upper limbs. The analysis of these EEG traces showed that significant event-related desynchronization is present before and during the execution of the movements, predominantly in the motor-related α and β frequency bands and in electrodes placed above the motor cortex. This oscillatory brain activity was used to continuously detect the intention to move the limbs, that is, to identify the motor phase prior to the actual execution of the reaching movement. The results showed, first, significant classification between relax and movement intention and, second, significant detection of movement intention prior to the onset of the executed movement. On the basis of these results, detection of movement intention could be used in BMI settings to reduce the gap between mental motor processes and the actual movement performed by an assisted or rehabilitation robotic device.
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52
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Stankevich LA, Sonkin KM, Shemyakina NV, Nagornova ZV, Khomenko JG, Perets DS, Koval AV. EEG pattern decoding of rhythmic individual finger imaginary movements of one hand. ACTA ACUST UNITED AC 2016. [DOI: 10.1134/s0362119716010175] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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53
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Lana EP, Adorno BV, Tierra-Criollo CJ. Detection of movement intention using EEG in a human-robot interaction environment. ACTA ACUST UNITED AC 2015. [DOI: 10.1590/2446-4740.0777] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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54
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Chang MH, Lee JS, Heo J, Park KS. Eliciting dual-frequency SSVEP using a hybrid SSVEP-P300 BCI. J Neurosci Methods 2015; 258:104-13. [PMID: 26561770 DOI: 10.1016/j.jneumeth.2015.11.001] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Revised: 10/30/2015] [Accepted: 11/01/2015] [Indexed: 11/28/2022]
Abstract
BACKGROUND Steady-state visual-evoked potential (SSVEP)-based brain-computer interfaces (BCIs) generate weak SSVEP with a monitor and cannot use harmonic frequencies, whereas P300-based BCIs need multiple stimulation sequences. These issues can decrease the information transfer rate (ITR). NEW METHOD In this paper, we introduce a novel hybrid SSVEP-P300 speller that generates dual-frequency SSVEP, allowing it to overcome the abovementioned limitations and improve the performance. The hybrid speller consists of nine panels flickering at different frequencies. Each panel contains four different characters that appear in a random sequence. The flickering panel and the periodically updating character evoke the dual-frequency SSVEP, while the oddball stimulus of the target character evokes the P300. A canonical correlation analysis (CCA) and a step-wise linear discriminant analysis (SWLDA) classified SSVEP and P300, respectively. Ten subjects participated in offline and online experiments, in which accuracy and ITR were compared with those of conventional SSVEP and P300 spellers. RESULTS The offline analysis revealed not only the P300 potential but also SSVEP with peaks at sub-harmonic frequencies, demonstrating that the proposed speller elicited dual-frequency SSVEP. This dual-frequency stimulation improved SSVEP recognition, increased the number of targets by employing harmonic frequencies, reduced the stimulation time for P300, and consequently improved ITR as compared to the conventional spellers. COMPARISON WITH EXISTING METHODS The new method reduces the stimulation time and allows harmonic frequencies to be employed for different stimuli. CONCLUSIONS The results indicate that this study provides a promising approach to make the BCI speller more reliable and efficient.
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Affiliation(s)
- Min Hye Chang
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, Republic of Korea
| | - Jeong Su Lee
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, Republic of Korea
| | - Jeong Heo
- Interdisciplinary Program for Bioengineering, Seoul National University, Seoul, Republic of Korea
| | - Kwang Suk Park
- Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul, Republic of Korea.
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55
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Jestrović I, Coyle JL, Sejdić E. Decoding human swallowing via electroencephalography: a state-of-the-art review. J Neural Eng 2015; 12:051001. [PMID: 26372528 PMCID: PMC4596245 DOI: 10.1088/1741-2560/12/5/051001] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Swallowing and swallowing disorders have garnered continuing interest over the past several decades. Electroencephalography (EEG) is an inexpensive and non-invasive procedure with very high temporal resolution which enables analysis of short and fast swallowing events, as well as an analysis of the organizational and behavioral aspects of cortical motor preparation, swallowing execution and swallowing regulation. EEG is a powerful technique which can be used alone or in combination with other techniques for monitoring swallowing, detection of swallowing motor imagery for diagnostic or biofeedback purposes, or to modulate and measure the effects of swallowing rehabilitation. This paper provides a review of the existing literature which has deployed EEG in the investigation of oropharyngeal swallowing, smell, taste and texture related to swallowing, cortical pre-motor activation in swallowing, and swallowing motor imagery detection. Furthermore, this paper provides a brief review of the different modalities of brain imaging techniques used to study swallowing brain activities, as well as the EEG components of interest for studies on swallowing and on swallowing motor imagery. Lastly, this paper provides directions for future swallowing investigations using EEG.
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Affiliation(s)
- Iva Jestrović
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - James L. Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, PA, USA
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56
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Wang L, Zhang X, Zhang Y. Extending motor imagery by speech imagery for brain-computer interface. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2013:7056-9. [PMID: 24111370 DOI: 10.1109/embc.2013.6611183] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
An electroencephalogram (EEG)-based brain computer interface (BCI) is a novel tool that translates brain intentions into control signals. As the operational dimensions of motor imagery are limited, we describe in this paper an extension of its capability by including speech imagery. Our new system was tested with the help of subjects, whose native language is Chinese. The tests were divided into two steps. The first step was speech imagery; consequently motor imagery and speech imagery were merged in the second step. Feature vectors of EEG signals were extracted from both common spatial patterns (CSP) and cross-correlation functions; then these vectors were classified by a support vector machine (SVM). The distinguishing accuracies of two intentions were found to be between 79.33% and 88.26%. This result shows that the capability of BCI for motor imagery can be extended by combining motor imagery and speech imagery.
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57
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Blokland Y, Spyrou L, Lerou J, Mourisse J, Jan Scheffer G, Geffen GJV, Farquhar J, Bruhn J. Detection of attempted movement from the EEG during neuromuscular block: proof of principle study in awake volunteers. Sci Rep 2015; 5:12815. [PMID: 26248679 PMCID: PMC4528221 DOI: 10.1038/srep12815] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2015] [Accepted: 07/02/2015] [Indexed: 11/18/2022] Open
Abstract
Brain-Computer Interfaces (BCIs) have the potential to detect intraoperative awareness during general anaesthesia. Traditionally, BCI research is aimed at establishing or improving communication and control for patients with permanent paralysis. Patients experiencing intraoperative awareness also lack the means to communicate after administration of a neuromuscular blocker, but may attempt to move. This study evaluates the principle of detecting attempted movements from the electroencephalogram (EEG) during local temporary neuromuscular blockade. EEG was obtained from four healthy volunteers making 3-second hand movements, both before and after local administration of rocuronium in one isolated forearm. Using offline classification analysis we investigated whether the attempted movements the participants made during paralysis could be distinguished from the periods when they did not move or attempt to move. Attempted movement trials were correctly identified in 81 (68-94)% (mean (95% CI)) and 84 (74-93)% of the cases using 30 and 9 EEG channels, respectively. Similar accuracies were obtained when training the classifier on the participants' actual movements. These results provide proof of the principle that a BCI can detect movement attempts during neuromuscular blockade. Based on this, in the future a BCI may serve as a communication channel between a patient under general anaesthesia and the anaesthesiologist.
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Affiliation(s)
- Yvonne Blokland
- Radboud University Medical Centre, Department of Anaesthesiology, Pain and Palliative Medicine, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Montessorilaan 3, 6525 HR Nijmegen, The Netherlands
| | - Loukianos Spyrou
- Radboud University Medical Centre, Department of Anaesthesiology, Pain and Palliative Medicine, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Montessorilaan 3, 6525 HR Nijmegen, The Netherlands
| | - Jos Lerou
- Radboud University Medical Centre, Department of Anaesthesiology, Pain and Palliative Medicine, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Jo Mourisse
- Radboud University Medical Centre, Department of Anaesthesiology, Pain and Palliative Medicine, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Gert Jan Scheffer
- Radboud University Medical Centre, Department of Anaesthesiology, Pain and Palliative Medicine, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Geert-Jan van Geffen
- Radboud University Medical Centre, Department of Anaesthesiology, Pain and Palliative Medicine, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
| | - Jason Farquhar
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Montessorilaan 3, 6525 HR Nijmegen, The Netherlands
| | - Jörgen Bruhn
- Radboud University Medical Centre, Department of Anaesthesiology, Pain and Palliative Medicine, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
- Radboud University Nijmegen, Donders Institute for Brain, Cognition and Behaviour, Montessorilaan 3, 6525 HR Nijmegen, The Netherlands
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58
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Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J Neurosci Methods 2015; 250:126-36. [DOI: 10.1016/j.jneumeth.2015.01.010] [Citation(s) in RCA: 376] [Impact Index Per Article: 41.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2014] [Revised: 01/06/2015] [Accepted: 01/07/2015] [Indexed: 11/21/2022]
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59
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Aliakbaryhosseinabadi S, Jiang N, Vuckovic A, Dremstrup K, Farina D, Mrachacz-Kersting N. Detection of movement intention from single-trial movement-related cortical potentials using random and non-random paradigms. BRAIN-COMPUTER INTERFACES 2015. [DOI: 10.1080/2326263x.2015.1053301] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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60
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Ibáñez J, Serrano JI, del Castillo MD, Minguez J, Pons JL. Predictive classification of self-paced upper-limb analytical movements with EEG. Med Biol Eng Comput 2015; 53:1201-10. [PMID: 25980505 DOI: 10.1007/s11517-015-1311-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2014] [Accepted: 05/04/2015] [Indexed: 12/01/2022]
Abstract
The extent to which the electroencephalographic activity allows the characterization of movements with the upper limb is an open question. This paper describes the design and validation of a classifier of upper-limb analytical movements based on electroencephalographic activity extracted from intervals preceding self-initiated movement tasks. Features selected for the classification are subject specific and associated with the movement tasks. Further tests are performed to reject the hypothesis that other information different from the task-related cortical activity is being used by the classifiers. Six healthy subjects were measured performing self-initiated upper-limb analytical movements. A Bayesian classifier was used to classify among seven different kinds of movements. Features considered covered the alpha and beta bands. A genetic algorithm was used to optimally select a subset of features for the classification. An average accuracy of 62.9 ± 7.5% was reached, which was above the baseline level observed with the proposed methodology (30.2 ± 4.3%). The study shows how the electroencephalography carries information about the type of analytical movement performed with the upper limb and how it can be decoded before the movement begins. In neurorehabilitation environments, this information could be used for monitoring and assisting purposes.
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Affiliation(s)
- Jaime Ibáñez
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council - CSIC, Av. Doctor Arce, 37, 28002, Madrid, Spain.
| | - J I Serrano
- Neural and Cognitive Engineering Group, Centro de Automática y Robótica, Spanish National Research Council - CSIC, Arganda del Rey, Spain
| | - M D del Castillo
- Neural and Cognitive Engineering Group, Centro de Automática y Robótica, Spanish National Research Council - CSIC, Arganda del Rey, Spain
| | - J Minguez
- Aragon Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain
- BitBrain Technologies, Zaragoza, Spain
| | - J L Pons
- Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council - CSIC, Av. Doctor Arce, 37, 28002, Madrid, Spain
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61
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Suraj, Tiwari P, Ghosh S, Sinha RK. Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2015; 2015:945729. [PMID: 25972896 PMCID: PMC4417985 DOI: 10.1155/2015/945729] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2014] [Revised: 03/20/2015] [Accepted: 03/21/2015] [Indexed: 11/17/2022]
Abstract
Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means clustering is found to outperform genetic algorithm (GA) and particle swarm optimization (PSO) based K-means clustering techniques in terms of both accuracy and execution time. The lesser execution time of hybrid GA-PSO technique makes it suitable for real time BCI application. Time frequency representation (TFR) techniques have been used to extract the feature of the signal under investigation. TFRs based features are extracted and relying on the concept of event related synchronization (ERD) and desynchronization (ERD) feature vector is formed.
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Affiliation(s)
- Suraj
- Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, India
| | - Purnendu Tiwari
- M. Tech., Computer Technology, National Institute of Technology, Raipur 492001, India
| | - Subhojit Ghosh
- Electrical and Electronics Engineering, National Institute of Technology, Raipur 492001, India
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62
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Yang H, Guan C, Wang CC, Ang KK. Detection of motor imagery of brisk walking from electroencephalogram. J Neurosci Methods 2015; 244:33-44. [DOI: 10.1016/j.jneumeth.2014.05.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2014] [Revised: 05/03/2014] [Accepted: 05/06/2014] [Indexed: 10/25/2022]
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63
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Sonkin KM, Stankevich LA, Khomenko JG, Nagornova ZV, Shemyakina NV. Development of electroencephalographic pattern classifiers for real and imaginary thumb and index finger movements of one hand. Artif Intell Med 2015; 63:107-17. [DOI: 10.1016/j.artmed.2014.12.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2014] [Revised: 12/08/2014] [Accepted: 12/09/2014] [Indexed: 11/28/2022]
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64
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Zapała D, Zabielska-Mendyk E, Cudo A, Krzysztofiak A, Augustynowicz P, Francuz P. Short-term kinesthetic training for sensorimotor rhythms: effects in experts and amateurs. J Mot Behav 2014; 47:312-8. [PMID: 25514553 DOI: 10.1080/00222895.2014.982067] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
The authors' aim was to examine whether short-term kinesthetic training affects the level of sensorimotor rhythm (SMR) in different frequency band: alpha (8-12 Hz), lower beta (12.5-16 Hz) and beta (16.5-20 Hz) during the execution of a motor imagery task of closing and opening the right and the left hand by experts (jugglers, practicing similar exercises on an everyday basis) and amateurs (individuals not practicing any sports). It was found that the performance of short kinesthetic training increases the power of alpha rhythm when executing imagery tasks only in the group of amateurs. Therefore, kinesthetic training may be successfully used as a method increasing the vividness of motor imagery, for example, in tasks involving the control of brain-computer interfaces based on SMR.
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Affiliation(s)
- Dariusz Zapała
- a Department of Experimental Psychology , The John Paul II Catholic University of Lublin , Poland
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65
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Bulea TC, Prasad S, Kilicarslan A, Contreras-Vidal JL. Sitting and standing intention can be decoded from scalp EEG recorded prior to movement execution. Front Neurosci 2014; 8:376. [PMID: 25505377 PMCID: PMC4243562 DOI: 10.3389/fnins.2014.00376] [Citation(s) in RCA: 78] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 11/04/2014] [Indexed: 12/18/2022] Open
Abstract
Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode movement intent from delta-band (0.1-4 Hz) EEG recorded immediately before movement execution in healthy volunteers. We used data from epochs starting 1.5 s before movement onset to classify future movements into one of three classes: stand-up, sit-down, or quiet. We assessed classification accuracy in both externally triggered and self-paced paradigms. Movement onset was determined from electromyography (EMG) recordings synchronized with EEG signals. We employed an artifact subspace reconstruction (ASR) algorithm to eliminate high amplitude noise before building our time-embedded EEG features. We applied local Fisher's discriminant analysis to reduce the dimensionality of our spatio-temporal features and subsequently used a Gaussian mixture model classifier for our three class problem. Our results demonstrate significantly better than chance classification accuracy (chance level = 33.3%) for the self-initiated (78.0 ± 2.6%) and triggered (74.7 ± 5.7%) paradigms. Surprisingly, we found no significant difference in classification accuracy between the self-paced and cued paradigms when using the full set of non-peripheral electrodes. However, accuracy was significantly increased for self-paced movements when only electrodes over the primary motor area were used. Overall, this study demonstrates that delta-band EEG recorded immediately before movement carries discriminative information regarding movement type. Our results suggest that EEG-based classifiers could improve lower-limb neuroprostheses and neurorehabilitation techniques by providing earlier detection of movement intent, which could be used in robot-assisted strategies for motor training and recovery of function.
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Affiliation(s)
- Thomas C Bulea
- Functional and Applied Biomechanics Section, Rehabilitation Medicine Department, National Institutes of Health Bethesda, MD, USA ; Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA
| | - Saurabh Prasad
- Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA
| | - Atilla Kilicarslan
- Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA
| | - Jose L Contreras-Vidal
- Laboratory for Non-invasive Brain-Machine Interface Systems, Department of Electrical and Computer Engineering, University of Houston Houston, TX, USA
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66
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López-Larraz E, Montesano L, Gil-Agudo Á, Minguez J. Continuous decoding of movement intention of upper limb self-initiated analytic movements from pre-movement EEG correlates. J Neuroeng Rehabil 2014; 11:153. [PMID: 25398273 PMCID: PMC4247645 DOI: 10.1186/1743-0003-11-153] [Citation(s) in RCA: 92] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2014] [Accepted: 10/27/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Brain-machine interfaces (BMI) have recently been integrated within motor rehabilitation therapies by actively involving the central nervous system (CNS) within the exercises. For instance, the online decoding of intention of motion of a limb from pre-movement EEG correlates is being used to convert passive rehabilitation strategies into active ones mediated by robotics. As early stages of upper limb motor rehabilitation usually focus on analytic single-joint mobilizations, this paper investigates the feasibility of building BMI decoders for these specific types of movements. METHODS Two different experiments were performed within this study. For the first one, six healthy subjects performed seven self-initiated upper-limb analytic movements, involving from proximal to distal articulations. For the second experiment, three spinal cord injury patients performed two of the previously studied movements with their healthy elbow and paralyzed wrist. In both cases EEG neural correlates such as the event-related desynchronization (ERD) and movement related cortical potentials (MRCP) were analyzed, as well as the accuracies of continuous decoders built using the pre-movement features of these correlates (i.e., the intention of motion was decoded before movement onset). RESULTS The studied movements could be decoded in both healthy subjects and patients. For healthy subjects there were significant differences in the EEG correlates and decoding accuracies, dependent on the moving joint. Percentages of correctly anticipated trials ranged from 75% to 40% (with chance level being around 20%), with better performances for proximal than for distal movements. For the movements studied for the SCI patients the accuracies were similar to the ones of the healthy subjects. CONCLUSIONS This paper shows how it is possible to build continuous decoders to detect movement intention from EEG correlates for seven different upper-limb analytic movements. Furthermore we report differences in accuracies among movements, which might have an impact on the design of the rehabilitation technologies that will integrate this new type of information. The applicability of the decoders was shown in a clinical population, with similar performances between healthy subjects and patients.
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Affiliation(s)
- Eduardo López-Larraz
- />DIIS, Universidad de Zaragoza, María de Luna, 1, Zaragoza, Spain
- />Instituto de Investigación en Ingeniería de Aragón, Zaragoza, Spain
| | - Luis Montesano
- />DIIS, Universidad de Zaragoza, María de Luna, 1, Zaragoza, Spain
- />Instituto de Investigación en Ingeniería de Aragón, Zaragoza, Spain
| | - Ángel Gil-Agudo
- />Unidad de Biomecánica y Ayudas Técnicas, Hospital Nacional de Parapléjicos, Toledo, Spain
| | - Javier Minguez
- />DIIS, Universidad de Zaragoza, María de Luna, 1, Zaragoza, Spain
- />Instituto de Investigación en Ingeniería de Aragón, Zaragoza, Spain
- />Bit & Brain Technologies SL, Zaragoza, Spain
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67
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Ibáñez J, Serrano JI, del Castillo MD, Monge-Pereira E, Molina-Rueda F, Alguacil-Diego I, Pons JL. Detection of the onset of upper-limb movements based on the combined analysis of changes in the sensorimotor rhythms and slow cortical potentials. J Neural Eng 2014; 11:056009. [PMID: 25082789 DOI: 10.1088/1741-2560/11/5/056009] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Characterizing the intention to move by means of electroencephalographic activity can be used in rehabilitation protocols with patients' cortical activity taking an active role during the intervention. In such applications, the reliability of the intention estimation is critical both in terms of specificity 'number of misclassifications' and temporal accuracy. Here, a detector of the onset of voluntary upper-limb reaching movements based on the cortical rhythms and the slow cortical potentials is proposed. The improvement in detections due to the combination of these two cortical patterns is also studied. APPROACH Upper-limb movements and cortical activity were recorded in healthy subjects and stroke patients performing self-paced reaching movements. A logistic regression combined the output of two classifiers: (i) a naïve Bayes classifier trained to detect the event-related desynchronization preceding the movement onset and (ii) a matched filter detecting the bereitschaftspotential. The proposed detector was compared with the detectors by using each one of these cortical patterns separately. In addition, differences between the patients and healthy subjects were analysed. MAIN RESULTS On average, 74.5 ± 13.8% and 82.2 ± 10.4% of the movements were detected with 1.32 ± 0.87 and 1.50 ± 1.09 false detections generated per minute in the healthy subjects and the patients, respectively. A significantly better performance was achieved by the combined detector (as compared to the detectors of the two cortical patterns separately) in terms of true detections (p = 0.099) and false positives (p = 0.0083). SIGNIFICANCE A rationale is provided for combining information from cortical rhythms and slow cortical potentials to detect the onsets of voluntary upper-limb movements. It is demonstrated that the two cortical processes supply complementary information that can be summed up to boost the performance of the detector. Successful results have been also obtained with stroke patients, which supports the use of the proposed system in brain-computer interface applications with this group of patients.
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Affiliation(s)
- J Ibáñez
- Bioengineering Group, Spanish Research Council (CSIC), Arganda del Rey, Madrid E-28500, Spain
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Movement type prediction before its onset using signals from prefrontal area: an electrocorticography study. BIOMED RESEARCH INTERNATIONAL 2014; 2014:783203. [PMID: 25126578 PMCID: PMC4122137 DOI: 10.1155/2014/783203] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/28/2014] [Revised: 05/29/2014] [Accepted: 06/24/2014] [Indexed: 11/18/2022]
Abstract
Power changes in specific frequency bands are typical brain responses during motor planning or preparation. Many studies have demonstrated that, in addition to the premotor, supplementary motor, and primary sensorimotor areas, the prefrontal area contributes to generating such responses. However, most brain-computer interface (BCI) studies have focused on the primary sensorimotor area and have estimated movements using postonset period brain signals. Our aim was to determine whether the prefrontal area could contribute to the prediction of voluntary movement types before movement onset. In our study, electrocorticography (ECoG) was recorded from six epilepsy patients while performing two self-paced tasks: hand grasping and elbow flexion. The prefrontal area was sufficient to allow classification of different movements through the area's premovement signals (−2.0 s to 0 s) in four subjects. The most pronounced power difference frequency band was the beta band (13–30 Hz). The movement prediction rate during single trial estimation averaged 74% across the six subjects. Our results suggest that premovement signals in the prefrontal area are useful in distinguishing different movement tasks and that the beta band is the most informative for prediction of movement type before movement onset.
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Kober SE, Wood G. Changes in hemodynamic signals accompanying motor imagery and motor execution of swallowing: a near-infrared spectroscopy study. Neuroimage 2014; 93 Pt 1:1-10. [PMID: 24576696 DOI: 10.1016/j.neuroimage.2014.02.019] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2013] [Revised: 01/21/2014] [Accepted: 02/16/2014] [Indexed: 01/25/2023] Open
Abstract
In the present study we investigated hemodynamic changes in the brain in response to motor execution (ME) and motor imagery (MI) of swallowing using near-infrared spectroscopy (NIRS). Previous studies provide evidence that ME and MI of limb movements lead to comparable brain activation patterns indicating the potential value of MI for motor rehabilitation. In this context, identifying brain correlates of MI of swallowing may be potentially useful for the treatment of dysphagia. Fourteen healthy participants actively swallowed water (ME) and mentally imagined to swallow water (MI) in a randomized order while changes in concentration of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) were assessed. MI and ME led to the strongest NIRS signal changes in the inferior frontal gyrus. During and after ME, oxy-Hb significantly increased, with a maximum peak around 15s after task onset. In contrast, oxy-Hb decreased during MI compared to a rest period probably because of motor inhibition mechanisms. Changes in deoxy-Hb were largely comparable between MI and ME, especially when participants used a kinesthetic motor imagery strategy during MI compared to no specific strategy. Hence, the present study provides new evidence concerning timing and topographical distribution of the hemodynamic response during ME and MI of swallowing.
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Affiliation(s)
- S E Kober
- Department of Psychology, University of Graz, Graz, Austria.
| | - G Wood
- Department of Psychology, University of Graz, Graz, Austria.
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70
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Abstract
The ability to decode an individual's intentions in real time has long been a ‘holy grail’ of research on human volition. For example, a reliable method could be used to improve scientific study of voluntary action by allowing external probe stimuli to be delivered at different moments during development of intention and action. Several Brain Computer Interface applications have used motor imagery of repetitive actions to achieve this goal. These systems are relatively successful, but only if the intention is sustained over a period of several seconds; much longer than the timescales identified in psychophysiological studies for normal preparation for voluntary action. We have used a combination of sensorimotor rhythms and motor imagery training to decode intentions in a single-trial cued-response paradigm similar to those used in human and non-human primate motor control research. Decoding accuracy of over 0.83 was achieved with twelve participants. With this approach, we could decode intentions to move the left or right hand at sub-second timescales, both for instructed choices instructed by an external stimulus and for free choices generated intentionally by the participant. The implications for volition are considered.
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Affiliation(s)
- Mathew Salvaris
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
- * E-mail:
| | - Patrick Haggard
- Institute of Cognitive Neuroscience, University College London, London, United Kingdom
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71
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Liao K, Xiao R, Gonzalez J, Ding L. Decoding individual finger movements from one hand using human EEG signals. PLoS One 2014; 9:e85192. [PMID: 24416360 PMCID: PMC3885680 DOI: 10.1371/journal.pone.0085192] [Citation(s) in RCA: 98] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2012] [Accepted: 12/02/2013] [Indexed: 11/18/2022] Open
Abstract
Brain computer interface (BCI) is an assistive technology, which decodes neurophysiological signals generated by the human brain and translates them into control signals to control external devices, e.g., wheelchairs. One problem challenging noninvasive BCI technologies is the limited control dimensions from decoding movements of, mainly, large body parts, e.g., upper and lower limbs. It has been reported that complicated dexterous functions, i.e., finger movements, can be decoded in electrocorticography (ECoG) signals, while it remains unclear whether noninvasive electroencephalography (EEG) signals also have sufficient information to decode the same type of movements. Phenomena of broadband power increase and low-frequency-band power decrease were observed in EEG in the present study, when EEG power spectra were decomposed by a principal component analysis (PCA). These movement-related spectral structures and their changes caused by finger movements in EEG are consistent with observations in previous ECoG study, as well as the results from ECoG data in the present study. The average decoding accuracy of 77.11% over all subjects was obtained in classifying each pair of fingers from one hand using movement-related spectral changes as features to be decoded using a support vector machine (SVM) classifier. The average decoding accuracy in three epilepsy patients using ECoG data was 91.28% with the similarly obtained features and same classifier. Both decoding accuracies of EEG and ECoG are significantly higher than the empirical guessing level (51.26%) in all subjects (p<0.05). The present study suggests the similar movement-related spectral changes in EEG as in ECoG, and demonstrates the feasibility of discriminating finger movements from one hand using EEG. These findings are promising to facilitate the development of BCIs with rich control signals using noninvasive technologies.
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Affiliation(s)
- Ke Liao
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Ran Xiao
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Jania Gonzalez
- Center for Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, United States of America
| | - Lei Ding
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, Oklahoma, United States of America
- Center for Biomedical Engineering, University of Oklahoma, Norman, Oklahoma, United States of America
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Detection of the Onset of Voluntary Movements Based on the Combination of ERD and BP Cortical Patterns. BIOSYSTEMS & BIOROBOTICS 2014. [DOI: 10.1007/978-3-319-08072-7_66] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Castermans T, Duvinage M, Cheron G, Dutoit T. Towards effective non-invasive brain-computer interfaces dedicated to gait rehabilitation systems. Brain Sci 2013; 4:1-48. [PMID: 24961699 PMCID: PMC4066236 DOI: 10.3390/brainsci4010001] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2013] [Revised: 11/05/2013] [Accepted: 12/12/2013] [Indexed: 12/24/2022] Open
Abstract
In the last few years, significant progress has been made in the field of walk rehabilitation. Motor cortex signals in bipedal monkeys have been interpreted to predict walk kinematics. Epidural electrical stimulation in rats and in one young paraplegic has been realized to partially restore motor control after spinal cord injury. However, these experimental trials are far from being applicable to all patients suffering from motor impairments. Therefore, it is thought that more simple rehabilitation systems are desirable in the meanwhile. The goal of this review is to describe and summarize the progress made in the development of non-invasive brain-computer interfaces dedicated to motor rehabilitation systems. In the first part, the main principles of human locomotion control are presented. The paper then focuses on the mechanisms of supra-spinal centers active during gait, including results from electroencephalography, functional brain imaging technologies [near-infrared spectroscopy (NIRS), functional magnetic resonance imaging (fMRI), positron-emission tomography (PET), single-photon emission-computed tomography (SPECT)] and invasive studies. The first brain-computer interface (BCI) applications to gait rehabilitation are then presented, with a discussion about the different strategies developed in the field. The challenges to raise for future systems are identified and discussed. Finally, we present some proposals to address these challenges, in order to contribute to the improvement of BCI for gait rehabilitation.
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Affiliation(s)
| | | | - Guy Cheron
- LNMB lab, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, Bruxelles 1050, Belgium.
| | - Thierry Dutoit
- TCTS lab, Université de Mons, Place du Parc 20, Mons 7000, Belgium.
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74
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Ibáñez J, Serrano J, del Castillo M, Gallego J, Rocon E. Online detector of movement intention based on EEG—Application in tremor patients. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2013.07.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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75
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Jochumsen M, Niazi IK, Mrachacz-Kersting N, Farina D, Dremstrup K. Detection and classification of movement-related cortical potentials associated with task force and speed. J Neural Eng 2013; 10:056015. [PMID: 23986024 DOI: 10.1088/1741-2560/10/5/056015] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE In this study, the objective was to detect movement intentions and extract different levels of force and speed of the intended movement from scalp electroencephalography (EEG). We then estimated the performance of the closed loop system. APPROACH Cued movements were detected from continuous EEG recordings using a template of the initial phase of the movement-related cortical potential in 12 healthy subjects. The temporal features, extracted from the movement intention, were classified with an optimized support vector machine. The system performance was evaluated when combining detection with classification. MAIN RESULTS The system detected 81% of the movements and correctly classified 75 ± 9% and 80 ± 10% of these at the point of detection when varying the force and speed, respectively. When the detector was combined with the classifier, the system detected and correctly classified 64 ± 13% and 67 ± 13% of these movements. The system detected and incorrectly classified 21 ± 7% and 16 ± 9% of the movements. The movements were detected 317 ± 73 ms before the movement onset. SIGNIFICANCE The results indicate that it is possible to detect movement intentions with limited latencies, and extract and classify different levels of force and speed, which may be combined with assistive technologies for patient-driven neurorehabilitation.
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Affiliation(s)
- Mads Jochumsen
- Department of Health Science and Technology, Center for Sensory-Motor Interaction, Aalborg University, Fredrik Bajers vej 7D, D2-212, 9220, Aalborg, Denmark
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76
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Velu PD, de Sa VR. Single-trial classification of gait and point movement preparation from human EEG. Front Neurosci 2013; 7:84. [PMID: 23781166 PMCID: PMC3678086 DOI: 10.3389/fnins.2013.00084] [Citation(s) in RCA: 40] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2012] [Accepted: 05/07/2013] [Indexed: 11/13/2022] Open
Abstract
Neuroimaging studies provide evidence of cortical involvement immediately before and during gait and during gait-related behaviors such as stepping in place or motor imagery of gait. Here we attempt to perform single-trial classification of gait intent from another movement plan (point intent) or from standing in place. Subjects walked naturally from a starting position to a designated ending position, pointed at a designated position from the starting position, or remained standing at the starting position. The 700 ms of recorded electroencephalography (EEG) before movement onset was used for single-trial classification of trials based on action type and direction (left walk, forward walk, right walk, left point, right point, and stand) as well as action type regardless of direction (stand, walk, point). Classification using regularized LDA was performed on a principal components analysis (PCA) reduced feature space composed of coefficients from levels 1 to 9 of a discrete wavelet decomposition using the Daubechies 4 wavelet. We achieved significant classification for all conditions, with errors as low as 17% when averaged across nine subjects. LDA and PCA highly weighted frequency ranges that included movement related potentials (MRPs), with smaller contributions from frequency ranges that included mu and beta idle motor rhythms. Additionally, error patterns suggested a spatial structure to the EEG signal. Future applications of the cortical gait intent signal may include an additional dimension of control for prosthetics, preemptive corrective feedback for gait disturbances, or human computer interfaces (HCI).
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Affiliation(s)
- Priya D Velu
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
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77
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Garipelli G, Chavarriaga R, Millán JDR. Single trial analysis of slow cortical potentials: a study on anticipation related potentials. J Neural Eng 2013; 10:036014. [PMID: 23611808 DOI: 10.1088/1741-2560/10/3/036014] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signal's spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering. APPROACH We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days. MAIN RESULTS We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers. SIGNIFICANCE We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.
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Affiliation(s)
- Gangadhar Garipelli
- Chair on Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, École Polytechnique Fédérale de Lausanne, Station 11, 1015 Lausanne, Switzerland.
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78
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Ahmadian P, Cagnoni S, Ascari L. How capable is non-invasive EEG data of predicting the next movement? A mini review. Front Hum Neurosci 2013; 7:124. [PMID: 23579176 PMCID: PMC3619112 DOI: 10.3389/fnhum.2013.00124] [Citation(s) in RCA: 30] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2013] [Accepted: 03/20/2013] [Indexed: 11/14/2022] Open
Abstract
In this study we summarize the features that characterize the pre-movements and pre-motor imageries (before imagining the movement) electroencephalography (EEG) data in humans from both Neuroscientists' and Engineers' point of view. We demonstrate what the brain status is before a voluntary movement and how it has been used in practical applications such as brain computer interfaces (BCIs). Usually, in BCI applications, the focus of study is on the after-movement or motor imagery potentials. However, this study shows that it is possible to develop BCIs based on the before-movement or motor imagery potentials such as the Bereitschaftspotential (BP). Using the pre-movement or pre-motor imagery potentials, we can correctly predict the onset of the upcoming movement, its direction and even the limb that is engaged in the performance. This information can help in designing a more efficient rehabilitation tool as well as BCIs with a shorter response time which appear more natural to the users.
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Affiliation(s)
- Pouya Ahmadian
- Henesis s.r.l Parma, Italy ; Ibis Lab, Department of Information Engineering, University of Parma Parma, Italy
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79
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Chang MH, Park KS. Frequency recognition methods for dual-frequency SSVEP based brain-computer interface. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:2220-2223. [PMID: 24110164 DOI: 10.1109/embc.2013.6609977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Dual-frequency steady-state visual evoked potential (SSVEP) was suggested to generate more stimuli using a few flickering frequencies for brain-computer interface. Dual--frequency SSVEP peaks at more than two frequencies-both main and harmonic frequencies. However multi-frequency recognition strategy has not been investigated for dual-frequency SSVEP. In this paper, three modified power spectral density analysis (PSDA) methods and two modified canonical correlation analysis (CCA) methods were tested for dual-frequency SSVEP classification. Three methods among the five methods used conventional features or classification techniques, and the other two methods used modified features for harmonic frequencies. As a result, CCA with novel features showed the best BCI performance. Also the use of harmonic frequencies improved BCI performance of dual-frequency SSVEP.
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80
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81
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Rodrigo M, Montesano L, Minguez J. Classification of resting, anticipation and movement states in self-initiated arm movements for EEG brain computer interfaces. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6285-8. [PMID: 22255775 DOI: 10.1109/iembs.2011.6091551] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In the last years, there has been an increasing interest in using Brain Computer Interfaces (BCI) within motor rehabilitation therapies that use robotic devices or functional electro stimulation to help or guide the efforts of the patient to move her body. A crucial step of these therapies is to provide help to the user just when she is actually trying to accomplish a certain motion or task One of the most promising applications of BCI systems in this context is its ability to measure the user intentions and actions to trigger the rehabilitation devices accordingly. This paper studies the single-trial classification based on EEG measurements of three basic states during the execution of self-initiated motion: rest, motion preparation (or anticipation) and motion. We conducted an experiment where the participants had to reach at their will eight different locations from a fixed starting position. Results for seven healthy subjects show that it is possible to achieve good classification rates given that features are carefully selected for each subject and for each pair of states.
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82
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Source Detection and Functional Connectivity of the Sensorimotor Cortex during Actual and Imaginary Limb Movement: A Preliminary Study on the Implementation of eConnectome in Motor Imagery Protocols. ADVANCES IN HUMAN-COMPUTER INTERACTION 2012. [DOI: 10.1155/2012/127627] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Introduction. Sensorimotor cortex is activated similarly during motor execution and motor imagery. The study of functional connectivity networks (FCNs) aims at successfully modeling the dynamics of information flow between cortical areas.Materials and Methods. Seven healthy subjects performed 4 motor tasks (real foot, imaginary foot, real hand, and imaginary hand movements), while electroencephalography was recorded over the sensorimotor cortex. Event-Related Desynchronization/Synchronization (ERD/ERS) of the mu-rhythm was used to evaluate MI performance. Source detection and FCNs were studied with eConnectome.Results and Discussion. Four subjects produced similar ERD/ERS patterns between motor execution and imagery during both hand and foot tasks, 2 subjects only during hand tasks, and 1 subject only during foot tasks. All subjects showed the expected brain activation in well-performed MI tasks, facilitating cortical source estimation. Preliminary functional connectivity analysis shows formation of networks on the sensorimotor cortex during motor imagery and execution.Conclusions. Cortex activation maps depict sensorimotor cortex activation, while similar functional connectivity networks are formed in the sensorimotor cortex both during actual and imaginary movements. eConnectome is demonstrated as an effective tool for the study of cortex activation and FCN. The implementation of FCN in motor imagery could induce promising advancements in Brain Computer Interfaces.
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83
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Mohamed AK, Marwala T, John LR. Single-trial EEG discrimination between wrist and finger movement imagery and execution in a sensorimotor BCI. 2011 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY 2011; 2011:6289-93. [PMID: 22255776 DOI: 10.1109/iembs.2011.6091552] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- A K Mohamed
- School of Electrical and Information Engineering, University of Witwatersrand, Johannesburg, South Africa.
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84
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Gomez-Gil J, San-Jose-Gonzalez I, Nicolas-Alonso LF, Alonso-Garcia S. Steering a tractor by means of an EMG-based human-machine interface. SENSORS (BASEL, SWITZERLAND) 2011; 11:7110-26. [PMID: 22164006 PMCID: PMC3231667 DOI: 10.3390/s110707110] [Citation(s) in RCA: 70] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/14/2011] [Revised: 07/04/2011] [Accepted: 07/07/2011] [Indexed: 11/17/2022]
Abstract
An electromiographic (EMG)-based human-machine interface (HMI) is a communication pathway between a human and a machine that operates by means of the acquisition and processing of EMG signals. This article explores the use of EMG-based HMIs in the steering of farm tractors. An EPOC, a low-cost human-computer interface (HCI) from the Emotiv Company, was employed. This device, by means of 14 saline sensors, measures and processes EMG and electroencephalographic (EEG) signals from the scalp of the driver. In our tests, the HMI took into account only the detection of four trained muscular events on the driver's scalp: eyes looking to the right and jaw opened, eyes looking to the right and jaw closed, eyes looking to the left and jaw opened, and eyes looking to the left and jaw closed. The EMG-based HMI guidance was compared with manual guidance and with autonomous GPS guidance. A driver tested these three guidance systems along three different trajectories: a straight line, a step, and a circumference. The accuracy of the EMG-based HMI guidance was lower than the accuracy obtained by manual guidance, which was lower in turn than the accuracy obtained by the autonomous GPS guidance; the computed standard deviations of error to the desired trajectory in the straight line were 16 cm, 9 cm, and 4 cm, respectively. Since the standard deviation between the manual guidance and the EMG-based HMI guidance differed only 7 cm, and this difference is not relevant in agricultural steering, it can be concluded that it is possible to steer a tractor by an EMG-based HMI with almost the same accuracy as with manual steering.
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Affiliation(s)
- Jaime Gomez-Gil
- Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails: (I.S.-J.-G.); (L.F.N.-A.); (S.A.-G.)
| | - Israel San-Jose-Gonzalez
- Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails: (I.S.-J.-G.); (L.F.N.-A.); (S.A.-G.)
| | - Luis Fernando Nicolas-Alonso
- Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails: (I.S.-J.-G.); (L.F.N.-A.); (S.A.-G.)
| | - Sergio Alonso-Garcia
- Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain; E-Mails: (I.S.-J.-G.); (L.F.N.-A.); (S.A.-G.)
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85
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Nam CS, Jeon Y, Kim YJ, Lee I, Park K. Movement imagery-related lateralization of event-related (de)synchronization (ERD/ERS): Motor-imagery duration effects. Clin Neurophysiol 2011; 122:567-577. [PMID: 20800538 DOI: 10.1016/j.clinph.2010.08.002] [Citation(s) in RCA: 106] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2009] [Revised: 08/04/2010] [Accepted: 08/04/2010] [Indexed: 11/15/2022]
Affiliation(s)
- Chang S Nam
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA.
| | - Yongwoong Jeon
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA.
| | - Young-Joo Kim
- Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA.
| | - Insuk Lee
- Department of Business Administration, Sogang University, Seoul, Republic of Korea.
| | - Kyungkyu Park
- Department of Business Administration, Sogang University, Seoul, Republic of Korea.
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86
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Bai O, Rathi V, Lin P, Huang D, Battapady H, Fei DY, Schneider L, Houdayer E, Chen X, Hallett M. Prediction of human voluntary movement before it occurs. Clin Neurophysiol 2010; 122:364-72. [PMID: 20675187 DOI: 10.1016/j.clinph.2010.07.010] [Citation(s) in RCA: 99] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2010] [Revised: 07/05/2010] [Accepted: 07/09/2010] [Indexed: 12/11/2022]
Abstract
OBJECTIVE Human voluntary movement is associated with two changes in electroencephalography (EEG) that can be observed as early as 1.5 s prior to movement: slow DC potentials and frequency power shifts in the alpha and beta bands. Our goal was to determine whether and when we can reliably predict human natural movement BEFORE it occurs from EEG signals ONLINE IN REAL-TIME. METHODS We developed a computational algorithm to support online prediction. Seven healthy volunteers participated in this study and performed wrist extensions at their own pace. RESULTS The average online prediction time was 0.62±0.25 s before actual movement monitored by EMG signals. There were also predictions that occurred without subsequent actual movements, where subjects often reported that they were thinking about making a movement. CONCLUSION Human voluntary movement can be predicted before movement occurs. SIGNIFICANCE The successful prediction of human movement intention will provide further insight into how the brain prepares for movement, as well as the potential for direct cortical control of a device which may be faster than normal physical control.
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Affiliation(s)
- Ou Bai
- EEG & BCI Laboratory, Department of Biomedical Engineering, Virginia Commonwealth University, Richmond, VA 23284-3067, USA.
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87
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Abstract
Brain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs' use requires activity-dependent adaptations in the brain that maintain stable relationships between the person's intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.
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Affiliation(s)
- Dennis J McFarland
- Laboratory of Neural Injury and Repair, Wadsworth Center, New York State Department of Health, Albany, NY 12201-0509, USA.
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88
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Chin ZY, Ang KK, Wang C, Guan C, Zhang H. Multi-class filter bank common spatial pattern for four-class motor imagery BCI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2009:571-4. [PMID: 19963466 DOI: 10.1109/iembs.2009.5332383] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper investigates the classification of multi-class motor imagery for electroencephalogram (EEG)-based Brain-Computer Interface (BCI) using the Filter Bank Common Spatial Pattern (FBCSP) algorithm. The FBCSP algorithm classifies EEG measurements from features constructed using subject-specific temporal-spatial filters. However, the FBCSP algorithm is limited to binary-class motor imagery. Hence, this paper proposes 3 approaches of multi-class extension to the FBCSP algorithm: One-versus-Rest, Pair-Wise and Divide-and-Conquer. These approaches decompose the multi-class problem into several binary-class problems. The study is conducted on the BCI Competition IV dataset IIa, which comprises single-trial EEG data from 9 subjects performing 4-class motor imagery of left-hand, right-hand, foot and tongue actions. The results showed that the multi-class FBCSP algorithm could extract features that matched neurophysiological knowledge, and yielded the best performance on the evaluation data compared to other international submissions.
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Affiliation(s)
- Zheng Yang Chin
- Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way #21-01 Connexis (South Tower) Singapore 138632
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White JR, Levy T, Bishop W, Beaty JD. Real-time decision fusion for multimodal neural prosthetic devices. PLoS One 2010; 5:e9493. [PMID: 20209151 PMCID: PMC2830464 DOI: 10.1371/journal.pone.0009493] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2009] [Accepted: 02/10/2010] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The field of neural prosthetics aims to develop prosthetic limbs with a brain-computer interface (BCI) through which neural activity is decoded into movements. A natural extension of current research is the incorporation of neural activity from multiple modalities to more accurately estimate the user's intent. The challenge remains how to appropriately combine this information in real-time for a neural prosthetic device. METHODOLOGY/PRINCIPAL FINDINGS Here we propose a framework based on decision fusion, i.e., fusing predictions from several single-modality decoders to produce a more accurate device state estimate. We examine two algorithms for continuous variable decision fusion: the Kalman filter and artificial neural networks (ANNs). Using simulated cortical neural spike signals, we implemented several successful individual neural decoding algorithms, and tested the capabilities of each fusion method in the context of decoding 2-dimensional endpoint trajectories of a neural prosthetic arm. Extensively testing these methods on random trajectories, we find that on average both the Kalman filter and ANNs successfully fuse the individual decoder estimates to produce more accurate predictions. CONCLUSIONS Our results reveal that a fusion-based approach has the potential to improve prediction accuracy over individual decoders of varying quality, and we hope that this work will encourage multimodal neural prosthetics experiments in the future.
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Affiliation(s)
- James Robert White
- Applied Mathematics and Scientific Computation Program, University of Maryland-College Park, College Park, Maryland, United States of America.
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Solis-Escalante T, Müller-Putz G, Brunner C, Kaiser V, Pfurtscheller G. Analysis of sensorimotor rhythms for the implementation of a brain switch for healthy subjects. Biomed Signal Process Control 2010. [DOI: 10.1016/j.bspc.2009.09.002] [Citation(s) in RCA: 52] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Buildup of choice-predictive activity in human motor cortex during perceptual decision making. Curr Biol 2009; 19:1581-5. [PMID: 19747828 DOI: 10.1016/j.cub.2009.07.066] [Citation(s) in RCA: 318] [Impact Index Per Article: 21.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2009] [Revised: 07/28/2009] [Accepted: 07/30/2009] [Indexed: 11/22/2022]
Abstract
Simple perceptual decisions are ideally suited for studying the sensorimotor transformations underlying flexible behavior. During perceptual detection, a noisy sensory signal is converted into a behavioral report of the presence or absence of a perceptual experience. Here, we used magnetoencephalography (MEG) to link the dynamics of neural population activity in human motor cortex to perceptual choices in a "yes/no" visual motion detection task. We found that (1) motor response-selective MEG activity in the "gamma" (64-100 Hz) and "beta" (12-36 Hz) frequency ranges predicted subjects' choices several seconds before their overt manual response; (2) this choice-predictive activity built up gradually during stimulus viewing toward both "yes" and "no" choices; and (3) the choice-predictive activity in motor cortex reflected the temporal integral of gamma-band activity in motion-sensitive area MT during stimulus viewing. Because gamma-band activity in MT reflects visual motion strength, these findings suggest that, during motion detection, motor plans for both "yes" and "no" choices result from continuously accumulating sensory evidence. We conclude that frequency-specific neural population activity at the cortical output stage of sensorimotor pathways provides a window into the mechanisms underlying perceptual decisions.
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