201
|
Sinyukov D, Desmond R, Dickerman M, Fleming J, Schaufeld J, Padir T. Multi-modal control framework for a semi-autonomous wheelchair using modular sensor designs. INTEL SERV ROBOT 2014. [DOI: 10.1007/s11370-014-0149-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
202
|
Ramsey NF, Aarnoutse EJ, Vansteensel MJ. Brain implants for substituting lost motor function: state of the art and potential impact on the lives of motor-impaired seniors. Gerontology 2014; 60:366-72. [PMID: 24642607 DOI: 10.1159/000357565] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2013] [Accepted: 11/26/2013] [Indexed: 11/19/2022] Open
Abstract
Recent scientific achievements bring the concept of neural prosthetics for reinstating lost motor function closer to medical application. Current research involves severely paralyzed people under the age of 65, but implications for seniors with stroke or trauma-induced impairments are clearly on the horizon. Demographic changes will lead to a shortage of personnel to care for an increasing population of senior citizens, threatening maintenance of an acceptable level of care and urging ways for people to live longer at their home independent from personal assistance. This is particularly challenging when people suffer from disabilities such as partial paralysis after stroke or trauma, where daily personal assistance is required. For some of these people, neural prosthetics can reinstate some lost motor function and/or lost communication, thereby increasing independence and possibly quality of life. In this viewpoint article, we present the state of the art in decoding brain activity in the service of brain-computer interfacing. Although some noninvasive applications produce good results, we focus on brain implants that benefit from better quality brain signals. Fully implantable neural prostheses for home use are not available yet, but clinical trials are being prepared. More sophisticated systems are expected to follow in the years to come, with capabilities of interest for less severe paralysis. Eventually the combination of smart robotics and brain implants is expected to enable people to interact well enough with their environment to live an independent life in spite of motor disabilities.
Collapse
Affiliation(s)
- N F Ramsey
- Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | | |
Collapse
|
203
|
Billinger M, Brunner C, Müller-Putz GR. SCoT: a Python toolbox for EEG source connectivity. Front Neuroinform 2014; 8:22. [PMID: 24653694 PMCID: PMC3949292 DOI: 10.3389/fninf.2014.00022] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2013] [Accepted: 02/20/2014] [Indexed: 11/29/2022] Open
Abstract
Analysis of brain connectivity has become an important research tool in neuroscience. Connectivity can be estimated between cortical sources reconstructed from the electroencephalogram (EEG). Such analysis often relies on trial averaging to obtain reliable results. However, some applications such as brain-computer interfaces (BCIs) require single-trial estimation methods. In this paper, we present SCoT-a source connectivity toolbox for Python. This toolbox implements routines for blind source decomposition and connectivity estimation with the MVARICA approach. Additionally, a novel extension called CSPVARICA is available for labeled data. SCoT estimates connectivity from various spectral measures relying on vector autoregressive (VAR) models. Optionally, these VAR models can be regularized to facilitate ill posed applications such as single-trial fitting. We demonstrate basic usage of SCoT on motor imagery (MI) data. Furthermore, we show simulation results of utilizing SCoT for feature extraction in a BCI application. These results indicate that CSPVARICA and correct regularization can significantly improve MI classification. While SCoT was mainly designed for application in BCIs, it contains useful tools for other areas of neuroscience. SCoT is a software package that (1) brings combined source decomposition and connectivtiy estimation to the open Python platform, and (2) offers tools for single-trial connectivity estimation. The source code is released under the MIT license and is available online at github.com/SCoT-dev/SCoT.
Collapse
Affiliation(s)
| | - Clemens Brunner
- Institute for Knowledge Discovery, Graz University of TechnologyGraz, Austria
| | | |
Collapse
|
204
|
Feasibility of a hybrid brain-computer interface for advanced functional electrical therapy. ScientificWorldJournal 2014; 2014:797128. [PMID: 24616644 PMCID: PMC3926286 DOI: 10.1155/2014/797128] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Accepted: 11/12/2013] [Indexed: 11/30/2022] Open
Abstract
We present a feasibility study of a novel hybrid brain-computer interface (BCI) system for advanced functional electrical therapy (FET) of grasp. FET procedure is improved with both automated stimulation pattern selection and stimulation triggering. The proposed hybrid BCI comprises the two BCI control signals: steady-state visual evoked potentials (SSVEP) and event-related desynchronization (ERD). The sequence of the two stages, SSVEP-BCI and ERD-BCI, runs in a closed-loop architecture. The first stage, SSVEP-BCI, acts as a selector of electrical stimulation pattern that corresponds to one of the three basic types of grasp: palmar, lateral, or precision. In the second stage, ERD-BCI operates as a brain switch which activates the stimulation pattern selected in the previous stage. The system was tested in 6 healthy subjects who were all able to control the device with accuracy in a range of 0.64–0.96. The results provided the reference data needed for the planned clinical study. This novel BCI may promote further restoration of the impaired motor function by closing the loop between the “will to move” and contingent temporally synchronized sensory feedback.
Collapse
|
205
|
Kaufmann T, Herweg A, Kübler A. Toward brain-computer interface based wheelchair control utilizing tactually-evoked event-related potentials. J Neuroeng Rehabil 2014; 11:7. [PMID: 24428900 PMCID: PMC3898245 DOI: 10.1186/1743-0003-11-7] [Citation(s) in RCA: 100] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2013] [Accepted: 12/23/2013] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND People with severe disabilities, e.g., due to neurodegenerative disease, depend on technology that allows for accurate wheelchair control. For those who cannot operate a wheelchair with a joystick, brain-computer interfaces (BCI) may offer a valuable option. Technology depending on visual or auditory input may not be feasible as these modalities are dedicated to processing of environmental stimuli (e.g., recognition of obstacles, ambient noise). Herein we thus validated the feasibility of a BCI based on tactually-evoked event-related potentials (ERP) for wheelchair control. Furthermore, we investigated use of a dynamic stopping method to improve speed of the tactile BCI system. METHODS Positions of four tactile stimulators represented navigation directions (left thigh: move left; right thigh: move right; abdomen: move forward; lower neck: move backward) and N = 15 participants delivered navigation commands by focusing their attention on the desired tactile stimulus in an oddball-paradigm. RESULTS Participants navigated a virtual wheelchair through a building and eleven participants successfully completed the task of reaching 4 checkpoints in the building. The virtual wheelchair was equipped with simulated shared-control sensors (collision avoidance), yet these sensors were rarely needed. CONCLUSION We conclude that most participants achieved tactile ERP-BCI control sufficient to reliably operate a wheelchair and dynamic stopping was of high value for tactile ERP classification. Finally, this paper discusses feasibility of tactile ERPs for BCI based wheelchair control.
Collapse
Affiliation(s)
- Tobias Kaufmann
- Department of Psychology I, University of Würzburg, Marcusstr 9-11, Würzburg 97070, Germany
| | - Andreas Herweg
- Department of Psychology I, University of Würzburg, Marcusstr 9-11, Würzburg 97070, Germany
| | - Andrea Kübler
- Department of Psychology I, University of Würzburg, Marcusstr 9-11, Würzburg 97070, Germany
| |
Collapse
|
206
|
Akcakaya M, Peters B, Moghadamfalahi M, Mooney AR, Orhan U, Oken B, Erdogmus D, Fried-Oken M. Noninvasive brain-computer interfaces for augmentative and alternative communication. IEEE Rev Biomed Eng 2014; 7:31-49. [PMID: 24802700 PMCID: PMC6525622 DOI: 10.1109/rbme.2013.2295097] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Brain-computer interfaces (BCIs) promise to provide a novel access channel for assistive technologies, including augmentative and alternative communication (AAC) systems, to people with severe speech and physical impairments (SSPI). Research on the subject has been accelerating significantly in the last decade and the research community took great strides toward making BCI-AAC a practical reality to individuals with SSPI. Nevertheless, the end goal has still not been reached and there is much work to be done to produce real-world-worthy systems that can be comfortably, conveniently, and reliably used by individuals with SSPI with help from their families and care givers who will need to maintain, setup, and debug the systems at home. This paper reviews reports in the BCI field that aim at AAC as the application domain with a consideration on both technical and clinical aspects.
Collapse
|
207
|
Huggins JE, Guger C, Allison B, Anderson CW, Batista A, Brouwer AM(AM, Brunner C, Chavarriaga R, Fried-Oken M, Gunduz A, Gupta D, Kübler A, Leeb R, Lotte F, Miller LE, Müller-Putz G, Rutkowski T, Tangermann M, Thompson DE. Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future. BRAIN-COMPUTER INTERFACES 2014; 1:27-49. [PMID: 25485284 PMCID: PMC4255956 DOI: 10.1080/2326263x.2013.876724] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The Fifth International Brain-Computer Interface (BCI) Meeting met June 3-7th, 2013 at the Asilomar Conference Grounds, Pacific Grove, California. The conference included 19 workshops covering topics in brain-computer interface and brain-machine interface research. Topics included translation of BCIs into clinical use, standardization and certification, types of brain activity to use for BCI, recording methods, the effects of plasticity, special interest topics in BCIs applications, and future BCI directions. BCI research is well established and transitioning to practical use to benefit people with physical impairments. At the same time, new applications are being explored, both for people with physical impairments and beyond. Here we provide summaries of each workshop, illustrating the breadth and depth of BCI research and high-lighting important issues for future research and development.
Collapse
Affiliation(s)
- Jane E. Huggins
- Department of Physical Medicine and Rehabilitation, Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States, 325 East Eisenhower, Room 3017; Ann Arbor, Michigan 48108-5744, 734-936-7177
| | - Christoph Guger
- Christoph Guger, g.tec medical engineering GmbH/Guger Technologies OG, Austria, Sierningstrasse 14, 4521 Schiedlberg, Austria, +43725122240-0
| | - Brendan Allison
- University of California at San Diego, La Jolla, CA 91942 (415) 490 7551
| | - Charles W. Anderson
- Department of Computer Science, Colorado State University, Fort Collins, CO 80523; telephone: 970-491-7491
| | - Aaron Batista
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, 3501 5th Av, BST3 4074; Pittsburgh, PA 15261; (412) 383-5394
| | - Anne-Marie (A.-M.) Brouwer
- The Netherlands Organization for Applied Scientific Research; P.O. Box 23/Kampweg 5, 3769 ZG Soesterberg, the Netherlands, ++31 (0)888 665960
| | - Clemens Brunner
- Institute for Knowledge Discovery, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Inffeldgasse 13/4, 8010; Graz, Austria
| | - Ricardo Chavarriaga
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland, EPFL-STI-CNBI, Station 11, 1005 Lausanne, Switzerland; Telephone: +41 21 693 6968
| | - Melanie Fried-Oken
- Oregon Health & Science University; Institute on Development & Disability; 707 SW Gaines Street; Portland, Oregon, United States; O: 503.494.7587, F: 503.494.6868
| | - Aysegul Gunduz
- Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA; Phone: +1 (352) 273 6877; Fax: +1 (352) 273 9221
| | - Disha Gupta
- Dept. of Neurology, Albany Medical College/Brain Computer Interfacing Lab, Wadsworth Center, NY State Dept. of Health, Albany, New York, USA
| | - Andrea Kübler
- Institute of Psychology, University of Würzburg; Marcusstr.9-11; 97070 Würzburg, Germany. Phone.: 0049 931 31 80179; Fax: 0049 931 31 82424
| | - Robert Leeb
- Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Switzerland
| | - Fabien Lotte
- Inria Bordeaux Sud-Ouest/LaBRI, 200 avenue de la vieille tour, 33405, Talence Cedex, France, Tel: +33 5 24 57 41 26
| | - Lee E. Miller
- Departments of Physiology, Physical Medicine and Rehab, and Biomedical Engineering; Feinberg School of Medicine; Northwestern University; Chicago, Illinois, United States; Ward 5-01; 303 East Chicago Avenue; Chicago, Illinois 60611; Phone: (312) 503 – 8677; Fax: (312) 503 – 5101
| | - Gernot Müller-Putz
- Institute for Knowledge Discovery, Laboratory of Brain-Computer Interfaces, Graz University of Technology, Inffeldgasse 13/4, 8010; Graz, Austria
| | - Tomasz Rutkowski
- Life Science Center of TARA, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8577 Japan; TEL: +81 (0)29-853-6261
| | - Michael Tangermann
- Excellence Cluster BrainLinks-BrainTools, Dept. Computer Science, University of Freiburg, Freiburg, Germany, Albertstr. 23; 79104 Freiburg; Germany; Phone: +49.(0)761.2038423, Fax : +49.(0)761.2038417
| | - David Edward Thompson
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States, 2800 Plymouth Road, Bdlg 26 Rm G06W-B; Ann Arbor, MI 48109; 734-763-7104
| |
Collapse
|
208
|
Omedes J, Iturrate I, Montesano L. Brain connectivity in continuous error tasks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2014; 2014:3997-4000. [PMID: 25570868 DOI: 10.1109/embc.2014.6944500] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Error-related potentials (ErrP) have been recently incorporated in brain-machine interfaces (BMIs) due to its ability to adapt and correct both the output of the BMI or the behavior of the machine. Most of these applications rely on synchronous tasks with different user's evaluations associated to correct and wrong events. Asynchronous detection during the continuous evaluation of the task, however, has to cope with background noise and an increased number of misdetections common in event-related potential detection. This paper studies a different characteristic that may carry additional information to be exploited by asynchronous ErrP detectors: brain connectivity coherence patterns appearing while the user monitors the continuous operation of a device. The results obtained with five subject revealed the presence of an error potential in an asynchronous reaching task an showed an increase in the coherency within the theta band.
Collapse
|
209
|
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.
Collapse
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.
| |
Collapse
|
210
|
McMullen DP, Hotson G, Katyal KD, Wester BA, Fifer MS, McGee TG, Harris A, Johannes MS, Vogelstein RJ, Ravitz AD, Anderson WS, Thakor NV, Crone NE. Demonstration of a semi-autonomous hybrid brain-machine interface using human intracranial EEG, eye tracking, and computer vision to control a robotic upper limb prosthetic. IEEE Trans Neural Syst Rehabil Eng 2013; 22:784-96. [PMID: 24760914 DOI: 10.1109/tnsre.2013.2294685] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
To increase the ability of brain-machine interfaces (BMIs) to control advanced prostheses such as the modular prosthetic limb (MPL), we are developing a novel system: the Hybrid Augmented Reality Multimodal Operation Neural Integration Environment (HARMONIE). This system utilizes hybrid input, supervisory control, and intelligent robotics to allow users to identify an object (via eye tracking and computer vision) and initiate (via brain-control) a semi-autonomous reach-grasp-and-drop of the object by the MPL. Sequential iterations of HARMONIE were tested in two pilot subjects implanted with electrocorticographic (ECoG) and depth electrodes within motor areas. The subjects performed the complex task in 71.4% (20/28) and 67.7% (21/31) of trials after minimal training. Balanced accuracy for detecting movements was 91.1% and 92.9%, significantly greater than chance accuracies (p < 0.05). After BMI-based initiation, the MPL completed the entire task 100% (one object) and 70% (three objects) of the time. The MPL took approximately 12.2 s for task completion after system improvements implemented for the second subject. Our hybrid-BMI design prevented all but one baseline false positive from initiating the system. The novel approach demonstrated in this proof-of-principle study, using hybrid input, supervisory control, and intelligent robotics, addresses limitations of current BMIs.
Collapse
|
211
|
Affiliation(s)
- Nitish V Thakor
- SINAPSE Institute, National University of Singapore, Singapore 117456, and Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| |
Collapse
|
212
|
Orhan U, Erdogmus D, Roark B, Oken B, Fried-Oken M. Offline analysis of context contribution to ERP-based typing BCI performance. J Neural Eng 2013; 10:066003. [PMID: 24099944 DOI: 10.1088/1741-2560/10/6/066003] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE We aim to increase the symbol rate of electroencephalography (EEG) based brain-computer interface (BCI) typing systems by utilizing context information. APPROACH Event related potentials (ERP) corresponding to a stimulus in EEG can be used to detect the intended target of a person for BCI. This paradigm is widely utilized to build letter-by-letter BCI typing systems. Nevertheless currently available BCI typing systems still require improvement due to low typing speeds. This is mainly due to the reliance on multiple repetitions before making a decision to achieve higher typing accuracy. Another possible approach to increase the speed of typing while not significantly reducing the accuracy of typing is to use additional context information. In this paper, we study the effect of using a language model (LM) as additional evidence for intent detection. Bayesian fusion of an n-gram symbol model with EEG features is proposed, and a specifically regularized discriminant analysis ERP discriminant is used to obtain EEG-based features. The target detection accuracies are rigorously evaluated for varying LM orders, as well as the number of ERP-inducing repetitions. MAIN RESULTS The results demonstrate that the LMs contribute significantly to letter classification accuracy. For instance, we find that a single-trial ERP detection supported by a 4-gram LM may achieve the same performance as using 3-trial ERP classification for the non-initial letters of words. SIGNIFICANCE Overall, the fusion of evidence from EEG and LMs yields a significant opportunity to increase the symbol rate of a BCI typing system.
Collapse
Affiliation(s)
- Umut Orhan
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | | | | | | | | |
Collapse
|
213
|
Choi B, Jo S. A low-cost EEG system-based hybrid brain-computer interface for humanoid robot navigation and recognition. PLoS One 2013; 8:e74583. [PMID: 24023953 PMCID: PMC3762758 DOI: 10.1371/journal.pone.0074583] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2013] [Accepted: 08/06/2013] [Indexed: 11/30/2022] Open
Abstract
This paper describes a hybrid brain-computer interface (BCI) technique that combines the P300 potential, the steady state visually evoked potential (SSVEP), and event related de-synchronization (ERD) to solve a complicated multi-task problem consisting of humanoid robot navigation and control along with object recognition using a low-cost BCI system. Our approach enables subjects to control the navigation and exploration of a humanoid robot and recognize a desired object among candidates. This study aims to demonstrate the possibility of a hybrid BCI based on a low-cost system for a realistic and complex task. It also shows that the use of a simple image processing technique, combined with BCI, can further aid in making these complex tasks simpler. An experimental scenario is proposed in which a subject remotely controls a humanoid robot in a properly sized maze. The subject sees what the surrogate robot sees through visual feedback and can navigate the surrogate robot. While navigating, the robot encounters objects located in the maze. It then recognizes if the encountered object is of interest to the subject. The subject communicates with the robot through SSVEP and ERD-based BCIs to navigate and explore with the robot, and P300-based BCI to allow the surrogate robot recognize their favorites. Using several evaluation metrics, the performances of five subjects navigating the robot were quite comparable to manual keyboard control. During object recognition mode, favorite objects were successfully selected from two to four choices. Subjects conducted humanoid navigation and recognition tasks as if they embodied the robot. Analysis of the data supports the potential usefulness of the proposed hybrid BCI system for extended applications. This work presents an important implication for the future work that a hybridization of simple BCI protocols provide extended controllability to carry out complicated tasks even with a low-cost system.
Collapse
Affiliation(s)
- Bongjae Choi
- Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea
| | - Sungho Jo
- Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, Republic of Korea
| |
Collapse
|
214
|
López-Larraz E, Antelis JM, Montesano L, Gil-Agudo A, Minguez J. Continuous decoding of motor attempt and motor imagery from EEG activity in spinal cord injury patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:1798-801. [PMID: 23366260 DOI: 10.1109/embc.2012.6346299] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Spinal cord injury (SCI) associates brain reorganization with a loss of cortical representation of paralyzed limbs. This effect is more pronounced in the chronic state, which can be reached approximately 6 months after the lesion. As many of the brain-computer interfaces (BCI) developed to date rely on the user motor activity, loss of this activity hinders the application of BCI technology for rehabilitation or motor compensation in these patients. This work is a preliminary study with three quadriplegic patients close to reaching the chronic state, addressing two questions: (i) whether it is still possible to use BCI technology to detect motor intention of the paralyzed hand at this state of chronicity; and (ii) whether it is better for the BCI decoding to rely on the motor attempt or the motor imagery of the hand as mental paradigm. The results show that one of the three patients had already lost the motor programs related to the hand, so it was not possible to build a motor-related BCI for him. For the other patients it was suitable to design a BCI based on both paradigms, but the results were better using motor attempt as it has broader activation associated patterns that are easier to recognize.
Collapse
|
215
|
McCullagh P, Lightbody G, Zygierewicz J, Kernohan WG. Ethical Challenges Associated with the Development and Deployment of Brain Computer Interface Technology. NEUROETHICS-NETH 2013. [DOI: 10.1007/s12152-013-9188-6] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
216
|
López-Larraz E, Escolano C, Minguez J. Upper alpha neurofeedback training over the motor cortex increases SMR desynchronization in motor tasks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:4635-8. [PMID: 23366961 DOI: 10.1109/embc.2012.6347000] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Desynchronization of sensorimotor rhythms (SMR) is a distinctive feature that provides a discriminative pattern for BCI operation. However, individuals such as BCI illiterates can not produce these discriminable patterns with sufficient reliability. Additionally, SMR desynchronization can become deteriorated or extinct in patients with spinal cord injury or a cerebrovascular accident. In all these situations BCI usage is compromised. This paper proposes an intervention based on neurofeedback training of the upper alpha band to improve SMR desynchronization. The feasibility of this intervention is demonstrated in a preliminary study in which five healthy subjects were trained to increase their upper alpha band power. Such increases produced higher SMR desynchronization and better discrimination between rest and execution states of a motor task.
Collapse
Affiliation(s)
- Eduardo López-Larraz
- Instituto de Investigaciónen Ingeniería de Aragóon-I3A and Dpto. de Informática e Ingeniería de Sistemas-DIIS, Universidad de Zaragoza, Spain.
| | | | | |
Collapse
|
217
|
Performance of the Emotiv Epoc headset for P300-based applications. Biomed Eng Online 2013; 12:56. [PMID: 23800158 PMCID: PMC3710229 DOI: 10.1186/1475-925x-12-56] [Citation(s) in RCA: 95] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2013] [Accepted: 06/13/2013] [Indexed: 11/17/2022] Open
Abstract
Background For two decades, EEG-based Brain-Computer Interface (BCI) systems have been widely studied in research labs. Now, researchers want to consider out-of-the-lab applications and make this technology available to everybody. However, medical-grade EEG recording devices are still much too expensive for end-users, especially disabled people. Therefore, several low-cost alternatives have appeared on the market. The Emotiv Epoc headset is one of them. Although some previous work showed this device could suit the customer’s needs in terms of performance, no quantitative classification-based assessments compared to a medical system are available. Methods This paper aims at statistically comparing a medical-grade system, the ANT device, and the Emotiv Epoc headset by determining their respective performances in a P300 BCI using the same electrodes. On top of that, a review of previous Emotiv studies and a discussion on practical considerations regarding both systems are proposed. Nine healthy subjects participated in this experiment during which the ANT and the Emotiv systems are used in two different conditions: sitting on a chair and walking on a treadmill at constant speed. Results The Emotiv headset performs significantly worse than the medical device; observed effect sizes vary from medium to large. The Emotiv headset has higher relative operational and maintenance costs than its medical-grade competitor. Conclusions Although this low-cost headset is able to record EEG data in a satisfying manner, it should only be chosen for non critical applications such as games, communication systems, etc. For rehabilitation or prosthesis control, this lack of reliability may lead to serious consequences. For research purposes, the medical system should be chosen except if a lot of trials are available or when the Signal-to-Noise Ratio is high. This also suggests that the design of a specific low-cost EEG recording system for critical applications and research is still required.
Collapse
|
218
|
Leeb R, Lancelle M, Kaiser V, Fellner DW, Pfurtscheller G. Thinking Penguin: Multimodal Brain–Computer Interface Control of a VR Game. IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES 2013. [DOI: 10.1109/tciaig.2013.2242072] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
|
219
|
Bermudez i Badia S, Garcia Morgade A, Samaha H, Verschure PFMJ. Using a Hybrid Brain Computer Interface and Virtual Reality System to Monitor and Promote Cortical Reorganization through Motor Activity and Motor Imagery Training. IEEE Trans Neural Syst Rehabil Eng 2013. [DOI: 10.1109/tnsre.2012.2229295] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
220
|
Thomas E, Fruitet J, Clerc M. Investigating brief motor imagery for an ERD/ERS based BCI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:2929-32. [PMID: 23366538 DOI: 10.1109/embc.2012.6346577] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This study establishes the effectiveness of event related synchronisation (ERS) features for a system paced brain computer interface (BCI). In particular, the relationship between the duration of motor imagery (MI) and the quality of the features extracted from the ERS is investigated. To this end, two groups of users performed brief (2s) or sustained (4s) MI, and offline single trial BCIs were validated on each group based on features extracted from the EEG before, during and after MI. The BCIs were designed to recognise two intentional control tasks and a no-control state. Cross-validated results indicate that brief MI leads to more informative ERS features than sustained MI.
Collapse
Affiliation(s)
- Eoin Thomas
- INRIA, 2004 Route des Lucioles, Sophia Antipolis, France.
| | | | | |
Collapse
|
221
|
Cecotti H, Eckstein MP, Giesbrecht B. Effects of performing two visual tasks on single-trial detection of event-related potentials. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2012:1723-6. [PMID: 23366242 DOI: 10.1109/embc.2012.6346281] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The detection of event-related potentials (ERPs) in brain-computer interface (BCI) depends on the ability of the subject to pay attention to specific stimuli presented during the BCI task. For healthy users, a BCI shall be used as a complement to other existing devices, which involve the response to other tasks. Those tasks may impair selective attention, particularly if the stimuli have the same modality e.g. visual. It is therefore critical to analyze how single-trial detection of brain evoked response is impaired by the addition of tasks concerning the same modality. We tested 10 healthy participants using an application that has two visual target detection tasks. The first one corresponds to a rapid serial visual presentation paradigm where target detection is achieved by brain-evoked single-trial detection in the recorded electroencephalogram (EEG) signal. The second task is the detection of a visual event on a tactical map by a behavioral response. These tasks were tested individually (single task) and in parallel (dual-task). Whereas the performance of single-trial detection was not impaired between single and dual-task conditions, the behavioral performance decreased during the dual-task condition. These results quantify the performance drop that can occur in a dual-task system using both brain-evoked responses and behavioral responses.
Collapse
Affiliation(s)
- Hubert Cecotti
- Department of Psychological & Brain Sciences, and Institute for Collaborative Biotechnologies, University of California Santa Barbara, Santa Barbara, CA 93106-9660, USA
| | | | | |
Collapse
|
222
|
Omedes J, Iturrate I, Montesano L, Minguez J. Using frequency-domain features for the generalization of EEG error-related potentials among different tasks. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:5263-5266. [PMID: 24110923 DOI: 10.1109/embc.2013.6610736] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
EEG brain-computer interfaces (BCI) require a calibration phase prior to the on-line control of the device, which is a difficulty for the practical development of this technology as it is user-, session- and task-specific. The large body of research in BCIs based on event-related potentials (ERP) use temporal features, which have demonstrated to be stable for each user along time, but do not generalize well among tasks different from the calibration task. This paper explores the use of low frequency features to improve the generalization capabilities of the BCIs using error-potentials. The results show that there exists a stable pattern in the frequency domain that allows a classifier to generalize among the tasks. Furthermore, the study also shows that it is possible to combine temporal and frequency features to obtain the best of both domains.
Collapse
|
223
|
Nishifuji S. Toward binary brain computer interface using steady-state visually evoked potential under eyes closed condition. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:2232-2235. [PMID: 24110167 DOI: 10.1109/embc.2013.6609980] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
It is highly difficult for severely amyotrophic lateral sclerosis and heavily spinal cord injury patients to use the brain computer interfaces (BCIs) based on the steady-state visual evoked potential (SSVEP) which need to control the direction of their eye gaze. We investigated amplitude change of the SSVEP associated with mental concentration on flicker to develop the SSVEP-based BCI usable under eyes-closed condition. Under the stimulus conditions of the flickering frequency of 10 Hz and the stimulus intensity of 5 lx, significant difference between the SSVEP amplitude in relaxed state and that in concentrated state was observed in the wide region of the scalp except the left frontal region, while such significance was also seen in the bilateral occipital lobes and left parietal region under the conditions of 14 Hz and 5 lx. Such an impact of mental concentration on the SSVEP amplitude was reproducible.
Collapse
|
224
|
Kim M, Chae Y, Jo S. Hybrid EEG and eye movement interface to multi-directional target selection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2013; 2013:763-766. [PMID: 24109799 DOI: 10.1109/embc.2013.6609612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
This work addresses the development of a low-cost hybrid interface with eye tracking and brain signals. Eye movement detection is used for search task and EEG-based brain computer interface (BCI) for selection task. Multi-directional target selection experiments with the hybrid interface device were conducted with five subjects to evaluate the proposed hybrid interface scheme. The task asked each user to move a cursor onto a circular target among twelve possible positions and select it. Using the Fitts' law, the interface performance was compared with the computer mouse. With two BCI selection confirmation schemes, the hybrid interface attained 2-2.7 bit/s overall. Based on the results, the potential of the proposed hybrid interface was discussed.
Collapse
|
225
|
|
226
|
Mulvenna M, Lightbody G, Thomson E, McCullagh P, Ware M, Martin S. Realistic expectations with brain computer interfaces. ACTA ACUST UNITED AC 2012. [DOI: 10.1108/17549451211285735] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
|
227
|
Postelnicu CC, Talaba D. P300-based brain-neuronal computer interaction for spelling applications. IEEE Trans Biomed Eng 2012. [PMID: 23204263 DOI: 10.1109/tbme.2012.2228645] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A brain-neuronal computer interaction system can provide a communication channel for severely disabled people or a supplementary control channel for able-bodied subjects. In this paper, a physiological hybrid P300-based speller that uses a modified stimulus presentation paradigm-the half checkerboard paradigm (HCBP)-is evaluated. The speller uses electrooculography and electroencephalography signals for selecting alphanumeric characters or commands arranged in an 8 × 9 matrix. In this study a group of subjects, who can voluntarily gaze at a target, used the checkerboard paradigm- and HCBP-based spellers in a counterbalanced fashion for comparing their performances under a series of online tests. A 16-character-long text was spelled by each subject, while a 13-character-long text was used for calibrating the system. By using the HCBP, the time required for spelling one character is reduced, resulting in higher information transfer rates. The results suggest that the HCBP has the potential to provide a more effective P300 paradigm with a major importance for people with neuromuscular diseases and also for healthy people as a supplementary communication channel.
Collapse
Affiliation(s)
- C-C Postelnicu
- Transilvania University of Brasov, Brasov 500036, Romania.
| | | |
Collapse
|
228
|
Filipe S, Charvet G, Foerster M, Porcherot J, Bêche JF, Bonnet S, Audebert P, Régis G, Zongo B, Robinet S, Condemine C, Mestais C, Guillemaud R. A wireless multichannel EEG recording platform. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:6319-22. [PMID: 22255783 DOI: 10.1109/iembs.2011.6091559] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
A wireless multichannel data acquisition system is being designed for ElectroEncephaloGraphy (EEG) recording. The system is based on a custom integrated circuit (ASIC) for signal conditioning, amplification and digitization and also on commercial components for RF transmission. It supports the RF transmission of a 32-channel EEG recording sampled at 1 kHz with a 12-bit resolution. The RF communication uses the MICS band (Medical Implant Communication Service) at 402-405 Mhz. This integration is a first step towards a lightweight EEG cap for Brain Computer Interface (BCI) studies. Here, we present the platform architecture and its submodules. In vivo validations are presented with noise characterization and wireless data transfer measurements.
Collapse
Affiliation(s)
- S Filipe
- Department DTBS, CEA/LETI, MINATEC Campus, Grenoble, France.
| | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
229
|
Ahangi A, Karamnejad M, Mohammadi N, Ebrahimpour R, Bagheri N. Multiple classifier system for EEG signal classification with application to brain–computer interfaces. Neural Comput Appl 2012. [DOI: 10.1007/s00521-012-1074-3] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
230
|
Tangermann M, Müller KR, Aertsen A, Birbaumer N, Braun C, Brunner C, Leeb R, Mehring C, Miller KJ, Müller-Putz GR, Nolte G, Pfurtscheller G, Preissl H, Schalk G, Schlögl A, Vidaurre C, Waldert S, Blankertz B. Review of the BCI Competition IV. Front Neurosci 2012; 6:55. [PMID: 22811657 PMCID: PMC3396284 DOI: 10.3389/fnins.2012.00055] [Citation(s) in RCA: 347] [Impact Index Per Article: 28.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2011] [Accepted: 03/30/2012] [Indexed: 11/13/2022] Open
Abstract
The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.
Collapse
Affiliation(s)
- Michael Tangermann
- Machine Learning Laboratory, Berlin Institute of Technology Berlin, Germany
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
231
|
|
232
|
Mattia M, Spadacenta S, Pavone L, Quarato P, Esposito V, Sparano A, Sebastiano F, Di Gennaro G, Morace R, Cantore G, Mirabella G. Stop-event-related potentials from intracranial electrodes reveal a key role of premotor and motor cortices in stopping ongoing movements. FRONTIERS IN NEUROENGINEERING 2012; 5:12. [PMID: 22754525 PMCID: PMC3386527 DOI: 10.3389/fneng.2012.00012] [Citation(s) in RCA: 63] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2012] [Accepted: 06/15/2012] [Indexed: 11/17/2022]
Abstract
In humans, the ability to withhold manual motor responses seems to rely on a right-lateralized frontal–basal ganglia–thalamic network, including the pre-supplementary motor area and the inferior frontal gyrus (IFG). These areas should drive subthalamic nuclei to implement movement inhibition via the hyperdirect pathway. The output of this network is expected to influence those cortical areas underlying limb movement preparation and initiation, i.e., premotor (PMA) and primary motor (M1) cortices. Electroencephalographic (EEG) studies have shown an enhancement of the N200/P300 complex in the event-related potentials (ERPs) when a planned reaching movement is successfully stopped after the presentation of an infrequent stop-signal. PMA and M1 have been suggested as possible neural sources of this ERP complex but, due to the limited spatial resolution of scalp EEG, it is not yet clear which cortical areas contribute to its generation. To elucidate the role of motor cortices, we recorded epicortical ERPs from the lateral surface of the fronto-temporal lobes of five pharmacoresistant epileptic patients performing a reaching version of the countermanding task while undergoing presurgical monitoring. We consistently found a stereotyped ERP complex on a single-trial level when a movement was successfully cancelled. These ERPs were selectively expressed in M1, PMA, and Brodmann's area (BA) 9 and their onsets preceded the end of the stop process, suggesting a causal involvement in this executive function. Such ERPs also occurred in unsuccessful-stop (US) trials, that is, when subjects moved despite the occurrence of a stop-signal, mostly when they had long reaction times (RTs). These findings support the hypothesis that motor cortices are the final target of the inhibitory command elaborated by the frontal–basal ganglia–thalamic network.
Collapse
Affiliation(s)
- M Mattia
- Department of Technologies and Health, Istituto Superiore di Sanità, Viale Regina Elena Rome, Italy
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
233
|
Sadeghian EB, Huo X, Ghovanloo M. Command detection and classification in tongue drive assistive technology. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:5465-8. [PMID: 22255574 DOI: 10.1109/iembs.2011.6091394] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Tongue Drive System (TDS) is a new assistive technology that enables individuals with severe disabilities such as those with spinal cord injury (SCI) to regain environmental control using their tongue motion. We have developed a new sensor signal processing (SSP) algorithm which uses four 3-axial magneto-resistive sensor outputs to accurately detect and classify between seven different user-control commands in stationary as well as mobile conditions. The new algorithm employs a two-stage classification method with a combination of 9 classifiers to discriminate between 4 commands on the left or right side of the oral cavity (one neutral command shared on both sides). Evaluation of the new SSP algorithm on five able-bodied subjects resulted in true positive rates in the range of 70-99% with corresponding false positive rates in the range of 5-7%, showing a notable improvement in the resulted true-false (TF) differences when compared to the previous algorithm.
Collapse
Affiliation(s)
- Elnaz Banan Sadeghian
- GT-Bionics Lab, School of Electrical and Computer Engineering at Georgia Institute of Technology, Atlanta, GA, USA
| | | | | |
Collapse
|
234
|
Long J, Li Y, Wang H, Yu T, Pan J, Li F. A hybrid brain computer interface to control the direction and speed of a simulated or real wheelchair. IEEE Trans Neural Syst Rehabil Eng 2012; 20:720-9. [PMID: 22692936 DOI: 10.1109/tnsre.2012.2197221] [Citation(s) in RCA: 132] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Brain-computer interfaces (BCIs) are used to translate brain activity signals into control signals for external devices. Currently, it is difficult for BCI systems to provide the multiple independent control signals necessary for the multi-degree continuous control of a wheelchair. In this paper, we address this challenge by introducing a hybrid BCI that uses the motor imagery-based mu rhythm and the P300 potential to control a brain-actuated simulated or real wheelchair. The objective of the hybrid BCI is to provide a greater number of commands with increased accuracy to the BCI user. Our paradigm allows the user to control the direction (left or right turn) of the simulated or real wheelchair using left- or right-hand imagery. Furthermore, a hybrid manner can be used to control speed. To decelerate, the user imagines foot movement while ignoring the flashing buttons on the graphical user interface (GUI). If the user wishes to accelerate, then he/she pays attention to a specific flashing button without performing any motor imagery. Two experiments were conducted to assess the BCI control; both a simulated wheelchair in a virtual environment and a real wheelchair were tested. Subjects steered both the simulated and real wheelchairs effectively by controlling the direction and speed with our hybrid BCI system. Data analysis validated the use of our hybrid BCI system to control the direction and speed of a wheelchair.
Collapse
Affiliation(s)
- Jinyi Long
- School of Automation Science and Engineering, South China University of Technology, Guangzhou, China.
| | | | | | | | | | | |
Collapse
|
235
|
Corbett EA, Perreault EJ, Körding KP. Decoding with limited neural data: a mixture of time-warped trajectory models for directional reaches. J Neural Eng 2012; 9:036002. [PMID: 22488128 PMCID: PMC5578432 DOI: 10.1088/1741-2560/9/3/036002] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Neuroprosthetic devices promise to allow paralyzed patients to perform the necessary functions of everyday life. However, to allow patients to use such tools it is necessary to decode their intent from neural signals such as electromyograms (EMGs). Because these signals are noisy, state of the art decoders integrate information over time. One systematic way of doing this is by taking into account the natural evolution of the state of the body--by using a so-called trajectory model. Here we use two insights about movements to enhance our trajectory model: (1) at any given time, there is a small set of likely movement targets, potentially identified by gaze; (2) reaches are produced at varying speeds. We decoded natural reaching movements using EMGs of muscles that might be available from an individual with spinal cord injury. Target estimates found from tracking eye movements were incorporated into the trajectory model, while a mixture model accounted for the inherent uncertainty in these estimates. Warping the trajectory model in time using a continuous estimate of the reach speed enabled accurate decoding of faster reaches. We found that the choice of richer trajectory models, such as those incorporating target or speed, improves decoding particularly when there is a small number of EMGs available.
Collapse
Affiliation(s)
- Elaine A Corbett
- Department of Biomedical Engineering, Northwestern University, Evanston, IL 60208, USA.
| | | | | |
Collapse
|
236
|
Cowan RE, Fregly BJ, Boninger ML, Chan L, Rodgers MM, Reinkensmeyer DJ. Recent trends in assistive technology for mobility. J Neuroeng Rehabil 2012; 9:20. [PMID: 22520500 PMCID: PMC3474161 DOI: 10.1186/1743-0003-9-20] [Citation(s) in RCA: 55] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2011] [Accepted: 04/20/2012] [Indexed: 11/10/2022] Open
Abstract
Loss of physical mobility makes maximal participation in desired activities more difficult and in the worst case fully prevents participation. This paper surveys recent work in assistive technology to improve mobility for persons with a disability, drawing on examples observed during a tour of academic and industrial research sites in Europe. The underlying theme of this recent work is a more seamless integration of the capabilities of the user and the assistive technology. This improved integration spans diverse technologies, including powered wheelchairs, prosthetic limbs, functional electrical stimulation, and wearable exoskeletons. Improved integration is being accomplished in three ways: 1) improving the assistive technology mechanics; 2) improving the user-technology physical interface; and 3) sharing of control between the user and the technology. We provide an overview of these improvements in user-technology integration and discuss whether such improvements have the potential to be transformative for people with mobility impairments.
Collapse
Affiliation(s)
- Rachel E Cowan
- Department of Neurological Surgery, The Miami Project to Cure Paralysis, University of Miami Miller School of Medicine, 1095 NW 14th Terrace, Miami, FL 33136, USA.
| | | | | | | | | | | |
Collapse
|
237
|
Breitwieser C, Daly I, Neuper C, Muller-Putz GR. Proposing a Standardized Protocol for Raw Biosignal Transmission. IEEE Trans Biomed Eng 2012; 59:852-9. [DOI: 10.1109/tbme.2011.2174637] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
|
238
|
From spinal central pattern generators to cortical network: integrated BCI for walking rehabilitation. Neural Plast 2012; 2012:375148. [PMID: 22272380 PMCID: PMC3261492 DOI: 10.1155/2012/375148] [Citation(s) in RCA: 84] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2011] [Revised: 09/08/2011] [Accepted: 09/22/2011] [Indexed: 12/02/2022] Open
Abstract
Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs). Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG), upper limb electromyogram (EMG), or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs) or dynamic recurrent neural networks (DRNNs). Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.
Collapse
|
239
|
Iturrate I, Chavarriaga R, Montesano L, Minguez J, Millan JDR. Latency correction of error potentials between different experiments reduces calibration time for single-trial classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2012:3288-3291. [PMID: 23366628 DOI: 10.1109/embc.2012.6346667] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
One fundamental limitation of EEG-based brain-computer interfaces is the time needed to calibrate the system prior to the detection of signals, due to the wide variety of issues affecting the EEG measurements. For event-related potentials (ERP), one of these sources of variability is the application performed: Protocols with different cognitive workloads might yield to different latencies of the ERPs. In this sense, it is still not clear the effect that these latency variations have on the single-trial classification. This work studies the differences in the latencies of error potentials across three experiments with increasing cognitive workloads. A delay-correction algorithm based on the cross-correlation of the averaged signals is presented, and tested with a single-trial classification of the signals. The results showed that latency variations exist between different protocols, and that it is feasible to re-use data from previous experiments to calibrate a classifier able to detect the signals of a new experiment, thus reducing the calibration time.
Collapse
|
240
|
Combining BCI with Virtual Reality: Towards New Applications and Improved BCI. TOWARDS PRACTICAL BRAIN-COMPUTER INTERFACES 2012. [DOI: 10.1007/978-3-642-29746-5_10] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
|
241
|
Escolano C, Antelis JM, Minguez J. A telepresence mobile robot controlled with a noninvasive brain-computer interface. ACTA ACUST UNITED AC 2011; 42:793-804. [PMID: 22180512 DOI: 10.1109/tsmcb.2011.2177968] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This paper reports an electroencephalogram-based brain-actuated telepresence system to provide a user with presence in remote environments through a mobile robot, with access to the Internet. This system relies on a P300-based brain-computer interface (BCI) and a mobile robot with autonomous navigation and camera orientation capabilities. The shared-control strategy is built by the BCI decoding of task-related orders (selection of visible target destinations or exploration areas), which can be autonomously executed by the robot. The system was evaluated using five healthy participants in two consecutive steps: 1) screening and training of participants and 2) preestablished navigation and visual exploration telepresence tasks. On the basis of the results, the following evaluation studies are reported: 1) technical evaluation of the device and its main functionalities and 2) the users' behavior study. The overall result was that all participants were able to complete the designed tasks, reporting no failures, which shows the robustness of the system and its feasibility to solve tasks in real settings where joint navigation and visual exploration were needed. Furthermore, the participants showed great adaptation to the telepresence system.
Collapse
Affiliation(s)
- Carlos Escolano
- Instituto de Investigación en Ingeniería de Aragón and the Departamento de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Zaragoza, Spain.
| | | | | |
Collapse
|
242
|
Müller-Putz GR, Breitwieser C, Cincotti F, Leeb R, Schreuder M, Leotta F, Tavella M, Bianchi L, Kreilinger A, Ramsay A, Rohm M, Sagebaum M, Tonin L, Neuper C, Millán JDR. Tools for Brain-Computer Interaction: A General Concept for a Hybrid BCI. Front Neuroinform 2011; 5:30. [PMID: 22131973 PMCID: PMC3223392 DOI: 10.3389/fninf.2011.00030] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2011] [Accepted: 11/01/2011] [Indexed: 11/21/2022] Open
Abstract
The aim of this work is to present the development of a hybrid Brain-Computer Interface (hBCI) which combines existing input devices with a BCI. Thereby, the BCI should be available if the user wishes to extend the types of inputs available to an assistive technology system, but the user can also choose not to use the BCI at all; the BCI is active in the background. The hBCI might decide on the one hand which input channel(s) offer the most reliable signal(s) and switch between input channels to improve information transfer rate, usability, or other factors, or on the other hand fuse various input channels. One major goal therefore is to bring the BCI technology to a level where it can be used in a maximum number of scenarios in a simple way. To achieve this, it is of great importance that the hBCI is able to operate reliably for long periods, recognizing and adapting to changes as it does so. This goal is only possible if many different subsystems in the hBCI can work together. Since one research institute alone cannot provide such different functionality, collaboration between institutes is necessary. To allow for such a collaboration, a new concept and common software framework is introduced. It consists of four interfaces connecting the classical BCI modules: signal acquisition, preprocessing, feature extraction, classification, and the application. But it provides also the concept of fusion and shared control. In a proof of concept, the functionality of the proposed system was demonstrated.
Collapse
Affiliation(s)
- Gernot R. Müller-Putz
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of TechnologyGraz, Austria
| | - Christian Breitwieser
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of TechnologyGraz, Austria
| | - Febo Cincotti
- Neuroelectrical Imaging and Brain-Computer Interfaces Laboratory, Fondazione S. LuciaRome, Italy
| | - Robert Leeb
- Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Martijn Schreuder
- Machine Learning Laboratory, Berlin Institute of TechnologyBerlin, Germany
| | - Francesco Leotta
- Neuroelectrical Imaging and Brain-Computer Interfaces Laboratory, Fondazione S. LuciaRome, Italy
| | - Michele Tavella
- Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Luigi Bianchi
- Neuroelectrical Imaging and Brain-Computer Interfaces Laboratory, Fondazione S. LuciaRome, Italy
| | - Alex Kreilinger
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of TechnologyGraz, Austria
| | - Andrew Ramsay
- Department of Computing Science, University of GlasgowGlasgow, UK
| | - Martin Rohm
- Department Orthopädie, Unfallchirurgie und Paraplegiologie, Experimentelle Paraplegiologie – Neurorehabilitation, Orthopaedic Hospital of Heidelberg UniversityHeidelberg, Germany
| | - Max Sagebaum
- Machine Learning Laboratory, Berlin Institute of TechnologyBerlin, Germany
| | - Luca Tonin
- Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de LausanneLausanne, Switzerland
| | - Christa Neuper
- Laboratory of Brain-Computer Interfaces, Institute for Knowledge Discovery, Graz University of TechnologyGraz, Austria
| | - José del. R. Millán
- Defitech Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, School of Engineering, Ecole Polytechnique Fédérale de LausanneLausanne, Switzerland
| |
Collapse
|
243
|
Höhne J, Schreuder M, Blankertz B, Tangermann M. A Novel 9-Class Auditory ERP Paradigm Driving a Predictive Text Entry System. Front Neurosci 2011; 5:99. [PMID: 21909321 PMCID: PMC3163907 DOI: 10.3389/fnins.2011.00099] [Citation(s) in RCA: 100] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2011] [Accepted: 07/28/2011] [Indexed: 11/17/2022] Open
Abstract
Brain–computer interfaces (BCIs) based on event related potentials (ERPs) strive for offering communication pathways which are independent of muscle activity. While most visual ERP-based BCI paradigms require good control of the user's gaze direction, auditory BCI paradigms overcome this restriction. The present work proposes a novel approach using auditory evoked potentials for the example of a multiclass text spelling application. To control the ERP speller, BCI users focus their attention to two-dimensional auditory stimuli that vary in both, pitch (high/medium/low) and direction (left/middle/right) and that are presented via headphones. The resulting nine different control signals are exploited to drive a predictive text entry system. It enables the user to spell a letter by a single nine-class decision plus two additional decisions to confirm a spelled word. This paradigm – called PASS2D – was investigated in an online study with 12 healthy participants. Users spelled with more than 0.8 characters per minute on average (3.4 bits/min) which makes PASS2D a competitive method. It could enrich the toolbox of existing ERP paradigms for BCI end users like people with amyotrophic lateral sclerosis disease in a late stage.
Collapse
Affiliation(s)
- Johannes Höhne
- Machine Learning Laboratory, Berlin Institute of Technology Berlin, Germany
| | | | | | | |
Collapse
|
244
|
The Asilomar Survey: Stakeholders' Opinions on Ethical Issues Related to Brain-Computer Interfacing. NEUROETHICS-NETH 2011; 6:541-578. [PMID: 24273623 PMCID: PMC3825606 DOI: 10.1007/s12152-011-9132-6] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2010] [Accepted: 07/28/2011] [Indexed: 10/29/2022]
Abstract
Brain-Computer Interface (BCI) research and (future) applications raise important ethical issues that need to be addressed to promote societal acceptance and adequate policies. Here we report on a survey we conducted among 145 BCI researchers at the 4th International BCI conference, which took place in May-June 2010 in Asilomar, California. We assessed respondents' opinions about a number of topics. First, we investigated preferences for terminology and definitions relating to BCIs. Second, we assessed respondents' expectations on the marketability of different BCI applications (BCIs for healthy people, BCIs for assistive technology, BCIs-controlled neuroprostheses and BCIs as therapy tools). Third, we investigated opinions about ethical issues related to BCI research for the development of assistive technology: informed consent process with locked-in patients, risk-benefit analyses, team responsibility, consequences of BCI on patients' and families' lives, liability and personal identity and interaction with the media. Finally, we asked respondents which issues are urgent in BCI research.
Collapse
|
245
|
Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behav Brain Funct 2011; 7:30. [PMID: 21810266 PMCID: PMC3175453 DOI: 10.1186/1744-9081-7-30] [Citation(s) in RCA: 387] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2011] [Accepted: 08/02/2011] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Artifacts contained in EEG recordings hamper both, the visual interpretation by experts as well as the algorithmic processing and analysis (e.g. for Brain-Computer Interfaces (BCI) or for Mental State Monitoring). While hand-optimized selection of source components derived from Independent Component Analysis (ICA) to clean EEG data is widespread, the field could greatly profit from automated solutions based on Machine Learning methods. Existing ICA-based removal strategies depend on explicit recordings of an individual's artifacts or have not been shown to reliably identify muscle artifacts. METHODS We propose an automatic method for the classification of general artifactual source components. They are estimated by TDSEP, an ICA method that takes temporal correlations into account. The linear classifier is based on an optimized feature subset determined by a Linear Programming Machine (LPM). The subset is composed of features from the frequency-, the spatial- and temporal domain. A subject independent classifier was trained on 640 TDSEP components (reaction time (RT) study, n = 12) that were hand labeled by experts as artifactual or brain sources and tested on 1080 new components of RT data of the same study. Generalization was tested on new data from two studies (auditory Event Related Potential (ERP) paradigm, n = 18; motor imagery BCI paradigm, n = 80) that used data with different channel setups and from new subjects. RESULTS Based on six features only, the optimized linear classifier performed on level with the inter-expert disagreement (<10% Mean Squared Error (MSE)) on the RT data. On data of the auditory ERP study, the same pre-calculated classifier generalized well and achieved 15% MSE. On data of the motor imagery paradigm, we demonstrate that the discriminant information used for BCI is preserved when removing up to 60% of the most artifactual source components. CONCLUSIONS We propose a universal and efficient classifier of ICA components for the subject independent removal of artifacts from EEG data. Based on linear methods, it is applicable for different electrode placements and supports the introspection of results. Trained on expert ratings of large data sets, it is not restricted to the detection of eye- and muscle artifacts. Its performance and generalization ability is demonstrated on data of different EEG studies.
Collapse
|
246
|
Bobrov P, Frolov A, Cantor C, Fedulova I, Bakhnyan M, Zhavoronkov A. Brain-computer interface based on generation of visual images. PLoS One 2011; 6:e20674. [PMID: 21695206 PMCID: PMC3112189 DOI: 10.1371/journal.pone.0020674] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2010] [Accepted: 05/10/2011] [Indexed: 11/01/2022] Open
Abstract
This paper examines the task of recognizing EEG patterns that correspond to performing three mental tasks: relaxation and imagining of two types of pictures: faces and houses. The experiments were performed using two EEG headsets: BrainProducts ActiCap and Emotiv EPOC. The Emotiv headset becomes widely used in consumer BCI application allowing for conducting large-scale EEG experiments in the future. Since classification accuracy significantly exceeded the level of random classification during the first three days of the experiment with EPOC headset, a control experiment was performed on the fourth day using ActiCap. The control experiment has shown that utilization of high-quality research equipment can enhance classification accuracy (up to 68% in some subjects) and that the accuracy is independent of the presence of EEG artifacts related to blinking and eye movement. This study also shows that computationally-inexpensive bayesian classifier based on covariance matrix analysis yields similar classification accuracy in this problem as a more sophisticated Multi-class Common Spatial Patterns (MCSP) classifier.
Collapse
Affiliation(s)
- Pavel Bobrov
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Moscow, Russia
- Technical University of Ostrava, Ostrava Poruba, Czech Republic
| | - Alexander Frolov
- Institute of Higher Nervous Activity and Neurophysiology of Russian Academy of Sciences, Moscow, Russia
| | - Charles Cantor
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, United States of America
- Department of Physiology and Biophysics, University of California Irvine, Irvine, California, United States of America
| | - Irina Fedulova
- Moscow State University, Department of Computational Mathematics and Cybernetics, Moscow, Russia
| | | | | |
Collapse
|
247
|
Grübler G. Beyond the responsibility gap. Discussion note on responsibility and liability in the use of brain-computer interfaces. AI & SOCIETY 2011. [DOI: 10.1007/s00146-011-0321-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
248
|
Volosyak I, Valbuena D, Lüth T, Malechka T, Gräser A. BCI demographics II: how many (and what kinds of) people can use a high-frequency SSVEP BCI? IEEE Trans Neural Syst Rehabil Eng 2011; 19:232-9. [PMID: 21421448 DOI: 10.1109/tnsre.2011.2121919] [Citation(s) in RCA: 98] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Brain-computer interface (BCI) systems use brain activity as an input signal and enable communication without movement. This study is a successor of our previous study (BCI demographics I) and examines correlations among BCI performance, personal preferences, and different subject factors such as age or gender for two sets of steady-state visual evoked potential (SSVEP) stimuli: one in the medium frequency range (13, 14, 15 and 16 Hz) and another in the high-frequency range (34, 36, 38, 40 Hz). High-frequency SSVEPs (above 30 Hz) diminish user fatigue and risk of photosensitive epileptic seizures. Results showed that most people, despite having no prior BCI experience, could use the SSVEP-based Bremen-BCI system in a very noisy field setting at a fair. Results showed that demographic parameters as well as handedness, tiredness, alcohol and caffeine consumption, etc., have no significant effect on the performance of SSVEP-based BCI. Most subjects did not consider the flickering stimuli annoying, only five out of total 86 participants indicated change in fatigue during the experiment. 84 subjects performed with a mean information transfer rate of 17.24 ±6.99 bit/min and an accuracy of 92.26 ±7.82% with the medium frequency set, whereas only 56 subjects performed with a mean information transfer rate of 12.10 ±7.31 bit/min and accuracy of 89.16 ±9.29% with the high-frequency set. These and other demographic analyses may help identify the best BCI for each user.
Collapse
Affiliation(s)
- Ivan Volosyak
- Institute of Automation, University of Bremen, 28359 Bremen, Germany.
| | | | | | | | | |
Collapse
|
249
|
Leeb R, Sagha H, Chavarriaga R, Del R Millan J. Multimodal fusion of muscle and brain signals for a hybrid-BCI. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2011; 2010:4343-6. [PMID: 21096001 DOI: 10.1109/iembs.2010.5626233] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Practical Brain-Computer Interfaces (BCIs) for disabled people should allow them to use all their remaining functionalities as control possibilities. Sometimes these people have residual activity of their muscles, most likely in the morning when they are not exhausted. In this work we fuse electromyographic (EMG) with electroencephalographic (EEG) activity in the framework of a so called "Hybrid-BCI" (hBCI) approach. Thereby, subjects could achieve a good control of their hBCI independently of their level of muscular fatigue. Furthermore, although EMG alone yields good performance, it is outperformed by the hybrid fusing of EEG and EMG. Two different fusion techniques are explored showing graceful performance degradation in the case of signal attenuation. Such a system allows a very reliable control and a smooth handover if the subjects get exhausted or fatigued during the day.
Collapse
Affiliation(s)
- Robert Leeb
- Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, Institute of Bioengineering, School of Engineering, Ecole Polytechnique Fédérale de Lausanne, CH-1015, Switzerland.
| | | | | | | |
Collapse
|
250
|
Ventouras EM, Asvestas P, Karanasiou I, Matsopoulos GK. Classification of Error-Related Negativity (ERN) and Positivity (Pe) potentials using kNN and Support Vector Machines. Comput Biol Med 2011; 41:98-109. [DOI: 10.1016/j.compbiomed.2010.12.004] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2010] [Revised: 12/17/2010] [Accepted: 12/21/2010] [Indexed: 11/26/2022]
|