C. Ang, M. Sakel, M. Pepper, and M. Phillips, "Use of brain computer interface in neurological rehabilitation," British Journal of Neuroscience Nursing, vol. 7, pp. 523-528, 2011 W. Wang, J. L. Collinger, M. A. Perez, E. C. Tyler-Kabara, L. G. Cohen, N. Birbaumer, et al., "Neural interface technology for rehabilitation: exploiting and promoting neuroplasticity," Physical Medicine and Rehabilitation Clinics of North America, vol. 21, no. 1, pp. 157-178, 2010. N. Birbaumer and L. G. Cohen. "Brain-computer interfaces: communication and restoration of movement in paralysis," J Physiol, vol. 579, no. 3, pp. 621-636, 2007. R. Scherer, G. R. Müller-Putz and G. Pfurtscheller, "Flexibility and practicality graz brain-computer interface approach," International Review of Neurobiology, vol. 86, pp. 119-131, 2009. N. Birbaumer, A. Ramos-Murguialday, C. Weber and P. Montoya, "Neurofeedback and brain computer interface clinical applications," International Review of Neurobiology, vol. 86, pp. 107-117, 2009. ...
TY - GEN. T1 - Nonnegative matrix factorization common spatial pattern in brain machine interface. AU - Tsubakida, H.. AU - Shiratori, T.. AU - Ishiyama, Atsushi. AU - Ono, Y.. PY - 2015/3/30. Y1 - 2015/3/30. N2 - Fast and accurate discrimination of Electroencephalography (EEG) data is necessary for controlling brain machine interface. This paper introduces a novel method to discriminate 2-class motor imagery states (left and right hand) using nonnegative matrix factorization (NMF), common spatial pattern (CSP) and random forest. Conventionally CSP is used after extracting frequency band segment of EEG signal, which is called bandpass-filtered CSP (BPCSP). Especially filter bank CSP (FBCSP) has been extensively used to extract feature vectors from EEG data. However in these methods, the range of frequency band needed to be specified in advance and the performance depends on the selected frequency band. Our new method can decide the frequency band automatically by using NMF (NMFCSP). After the ...
Director of Research Simon Carlile, Ph.D., looks at brain computer interfaces and how it affects hearing aid and hearable technology for the future.
Objective: Brain stimulation has the potential to become a valuable intervention for patients with severe upper-limb paresis following brain injury, surgery or stroke. Neuromodulation has been shown to be most effective in inducing plasticity when applied in conjunction with repetitive motor practice. Novel Brain-Computer Interface (BCI) based training environments are a promising solution to facilitate motor restoration substituting the disturbed efferent channel of the human motor system and enabling brain-state dependent application of brain stimulation.. Method: We introduced a training set-up that consists of both gravity-compensating and robot-controlled devices for the upper extremity of patients with severe arm and hand paresis. Embedded in closed-loop brain-computer interface (BCI) technology this set-up allowed patients to volitionally control an orthosis attached to their paretic extremity by the modulation of brain oscillations. The significant increase of BCI control was paralleled ...
A brain-computer interface (BCI), sometimes called a neural-control interface (NCI), mind-machine interface (MMI), direct neural interface (DNI), or brain-machine interface (BMI), is a direct communication pathway between an enhanced or wired brain and an external device. BCI differs from neuromodulation in that it allows for bidirectional information flow. BCIs are often directed at researching, mapping, assisting, augmenting, or repairing human cognitive or sensory-motor functions. Research on BCIs began in the 1970s at the University of California, Los Angeles (UCLA) under a grant from the National Science Foundation, followed by a contract from DARPA. The papers published after this research also mark the first appearance of the expression brain-computer interface in scientific literature. The field of BCI research and development has since focused primarily on neuroprosthetics applications that aim at restoring damaged hearing, sight and movement. Thanks to the remarkable cortical ...
Apples new headset allegedly kept a radiation expert: do not use for a long time [Abstract] Apple launched a new wireless headset AirPods, American public health experts pointed out that this wireless headset radiation problem, can damage the brain vascular barrier of human body. IPhone7 new headphones 16, Apples new phone iPhone7 sale in mainland china. This year, Apple launched a new wireless headset AirPods, however, this wireless headset is Tucao users like hearing aids, there are people worried that it is easy to lose. In addition, the United States public health experts pointed out that this wireless headset radiation problems, can damage the human brain vascular barrier. , , headset radiation microwave transmission equipment near the brain is playing with fire according to the Daily Mail reported by iPhone7, called AirPods wireless Bluetooth headset. Apple AirPods external smooth, waterproof, can avoid the problem of tangled headphone wire. This headset is driven by Bluetooth ...
EEG Feature Extraction and Pattern Classification Based on Motor Imagery in Brain-Computer Interface: 10.4018/ijssci.2011070104: Accurate classification of EEG left and right hand motor imagery is an important issue in brain-computer interface. Firstly, discrete wavelet transform method
Until recently, the concept of controlling your environment through thought was purely science fiction. It was only 1968 when Eb Fetz, a researcher at the Center for Sensorimotor Neural Engineering (CSNE), first pioneered connecting machines to minds. He showed that monkeys can amplify their brain signals to control a needle that moved on a dial.. Today, the field of brain-computer interface (BCI) technology has allowed people to functionally merge with electrical devices. BCI tech can assist individuals unable to speak in communicating, and those unable to use their limbs regain mobility. At the University of Pittsburgh, researchers used signals recorded inside the brain to control a robotic arm. At Stanford, researchers extracted the movement intentions of paralyzed patients from their brain signals, allowing them wireless control of a tablet. The most common BCI tech gadgets are Cochlear implants, devices that assist with hearing. What is brain-computer interface technology?. Matthew Sample, ...
1. Bell, C.J., Shenoy, P., Chalodhorn, R., and Rao, R.P.N. Control of a humanoid robot by a noninvasive brain-computer interface in humans. Journal of Neural Engineering 5, 2 (June 2008), 214 220.. 2. Black, A.H., Young, G.A., and Batenchuk, C. Avoidance training of hippocampal theta waves in flaxedilized dogs and its relation to skeletal movement. Journal of Comparative and Physiological Psychology 70, 1 (Jan. 1970), 15 24.. 3. Blankertz, B., Muller, K-R, Krusienski, D.J., Schalk, G., Wolpaw, J.R., Schlogl, A., Pfurtscheller, G., Millan, J., Schroder, M., and Birbaumer, N. The BCI competition III: Validating alternative approaches to actual BCI problems. IEEE Transactions on Neural Systems and Rehabilitation Engineering 14, 2 (June 2006), 153 159.. 4. Coyle, S.M., Ward, T.E., and Markham, C.M. Brain-computer interface using a simplified functional near-infrared spectroscopy system. Journal of Neural Engineering 4, 3 (Sept. 2007), 219 226.. 5. Curran, E., Sykacek, P., Stokes, M., Roberts, S.J., ...
Brain-computer interfaces allow you to manipulate computers and machinery with your thoughts. Learn more about brain-computer interface technology.
Klaus-Robert Müller will give an IPAM public lecture on Monday, March 4, 2013 entitled "Toward Brain Computer Interfacing." His talk will provide a brief overview of current Brain Computer Interface technology. In particular, it will show the wealth, complexity and difficulties of the data available. The talk will then report in more detail about the Berlin Brain Computer Interface that is based on Electroencephalography (EEG) signals. Klaus-Robert Müller is Professor of Computer Science at Technical University Berlin and Director of the Bernstein Focus on Neurotechnology Berlin. The lecture will begin at 4:30 pm in the CNSI auditorium at UCLA. It is free and open to the public.. ...
Given the important challenges associated with the processing of brain signals obtained from neuroimaging modalities, fuzzy sets and systems have been proposed as a useful and effective framework for the analysis of brain activity as well as to enable a direct communication pathway between the brain and external devices (brain computer/machine interfaces). While there has been increasing interest in these questions, the contribution of fuzzy logic sets and systems has been diverse depending on the area of application. On the one hand, considering the decoding of brain activity, fuzzy sets and systems represent an excellent tool to overcome the challenge of processing extremely noisy signals that are very likely to be affected by non-stationarities. On the other hand, as regards neuroscience research, fuzziness has equally been employed for the measurement of smooth integration between synapses, neurons, and brain regions or areas. In this context, the proposed special session aims at providing ...
ABSTRACT: For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world - a brain-computer interface (BCI). Over the past 15 years, productive BCI research programs have arisen. Encouraged by new understanding of brain function, by the advent of powerful low-cost computer equipment, and by growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new augmentative communication and control technology for those with severe neuromuscular disorders, such as amyotrophic lateral sclerosis, brainstem stroke, and spinal cord injury. The immediate goal is to provide these users, who may be completely paralyzed, or locked in, with basic communication capabilities so that they can express their wishes to caregivers or even operate word processing programs or neuroprostheses. ...
This paper describes our experiences in building a virtual keyboard implemented using a Brain-Computer Interface (BCI) that interacts with the eMotiv EPOC Neural Headset.
Direct brain control of overground walking in those with paraplegia due to spinal cord injury (SCI) has not been achieved. Invasive brain-computer interfaces (BCIs) may provide a permanent solution to this problem by directly linking the brain to lower extremity prostheses. To justify the pursuit of such invasive systems, the feasibility of BCI controlled overground walking should first be established in a noninvasive manner. To accomplish this goal, we developed an electroencephalogram (EEG)-based BCI to control a functional electrical stimulation (FES) system for overground walking and assessed its performance in an individual with paraplegia due to SCI. An individual with SCI (T6 AIS B) was recruited for the study and was trained to operate an EEG-based BCI system using an attempted walking/idling control strategy. He also underwent muscle reconditioning to facilitate standing and overground walking with a commercial FES system. Subsequently, the BCI and FES systems were integrated and the
Brain-Computer Interface is the paradigm of connecting the human brain directly to a computer, bypassing the sensory system, and the physical body completely. Currently, BCI and neuroprosthetics are experiencing real successes in linking human brains to computers, and the control of virtual, and physical prosthetic limbs via pure thought control.
Brain-computer interfaces (BCIs) are an emerging novel technology for stroke rehabilitation. Little is known about how dose-response relationships for BCI therapies affect brain and behavior changes. We report preliminary results on stroke patients (n = 16, 11 M) with persistent upper extremity motor impairment who received therapy using a BCI system with functional electrical stimulation of the hand and tongue stimulation. We collected MRI scans and behavioral data using the Action Research Arm Test (ARAT), 9-Hole Peg Test (9-HPT), and Stroke Impact Scale (SIS) before, during, and after the therapy period. Using anatomical and functional MRI, we computed Laterality Index (LI) for brain activity in the motor network during impaired hand finger tapping. Changes from baseline LI and behavioral scores were assessed for relationships with dose, intensity, and frequency of BCI therapy. We found that gains in SIS Strength were directly responsive to BCI therapy: therapy dose and intensity correlated
Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular condition characterized by weakness, muscle wasting, fasciculations and increased reflexes. Depending on the site of onset, individuals with ALS progressively lose control of their skeletal muscles; bulbar or the extremities. As symptoms worsen and spread, muscle atrophy becomes apparent and upper motor neuron symptoms such as spasticity complicate gait (in lower limb involvement) and manual dexterity (in upper limb involvement). The patients progress to a state of profound disability and have great difficulty in communicating; some may even be entirely locked in to their bodies. The capacity for simple communication could greatly improve their quality of life.. New technologies are giving people with disabilities alternate communication and control options. One such instrument is the EEG-based Brain-Computer Interface (BCI) which can provide both communication and control functions to those who have lost muscle control. By ...
This paper presents a steady-state visually evoked potential (SSEVP) EEG-based brain-computer interface (BCI) with real-time artifact removal using indepen
Why join us?. EMOTIV is a bioinformatics company advancing understanding of the human brain using electroencephalography (EEG). Our mission is to empower individuals to understand their own brain and accelerate brain research globally.. Founded in 2011 by tech entrepreneurs Tan Le (CEO) and Dr. Geoff Mackellar (CTO) the company is headquartered in San Francisco, U.S.A. with facilities in Sydney, Hanoi and Ho Chi Minh City.. The technology falls under the umbrella of BCIs (Brain Computer Interface) also referred to as MMI (Mind Machine Interface), DNI (Direct Neural Interface), BMI (Brain Machine Interface) and aims to track cognitive performance, monitor emotions, and control both virtual and physical objects via machine learning of trained mental commands.. Applications for the EMOTIV technology and interface span an amazing variety of potential industries and applications - from gaming to interactive television, everyday computer interactions, hands-free control system, smart adaptive ...
MindSet brainwave-reading headset. Two people wearing a Mindset brain-computer interface (BCI) headset developed by Neurosky Inc., San Jose, USA. The headset contains a single electroencephalogram (EEG) sensor. The sensor makes contact with the wearers forehead and detects electrical activity produced by the brain (as shown on the computer screen in the background). The signals are transmitted wirelessly to a computer where they are translated by software. The user can manipulate virtual objects on computer with mind control alone. This headset is designed to be used with computer games, but may have applications as a medical or therapeutic tool. Photographed in 2008. - Stock Image T498/0013
Albert Martí is raising funds for wheell:Cheap Brain-Computer Interface (BCI) for Wheelchairs on Kickstarter! Motivated biomedical engineers with 3D printed EEG cap, Arduino ADC and classification algorithms. Helping reduced mobility patients.
One of the major limitations of Brain-Computer Interfaces (BCI) is their long calibration time, which limits their use in practice, both by patients and healthy users alike. Such long calibration times are due to the large between-user variability and thus to the need to collect numerous training electroencephalography (EEG) trials for the machine learning algorithms used in BCI design. In this paper, we first survey existing approaches to reduce or suppress calibration time, these approaches being notably based on regularization, user-to-user transfer, semi-supervised learning and a-priori physiological information. We then propose new tools to reduce BCI calibration time. In particular, we propose to generate artificial EEG trials from the few EEG trials initially available, in order to augment the training set size. These artificial EEG trials are obtained by relevant combinations and distortions of the original trials available. We propose 3 different methods to do so. We also propose a new, fast
Motor and somatosensory cortex are know to have a distinct somatotopic organization. This and additional knowledge of neuronal coding functions are being used to develop brain-computer interfaces (BCIs) that establish functional connections between cortical neurons and prosthetic and assistive devices. BCIs often use electrodes placed in brain areas responsible for volitional control and sensation of limb movements, particularly the arm and hand regions.
Fingerprint Dive into the research topics of Retrospectively supervised click decoder calibration for self-calibrating point-and-click brain-computer interfaces. Together they form a unique fingerprint. ...
Brain-Computer Interface: Complete MATLAB-based development / research systems, inlcuding all the necessary software and hardware components. With the software package High-Speed Online Processing under SIMULINK, you can read the biosignal data directly into SIMULINK. Ready to go paradigms for spelling and cursor control. MATLAB/Simulink Rapid Prototyping environment speeds up development times from months to days
Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme
The hybrid brain-computer interface (BCI)s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely,
A. S. Burns et al., "Type and Timing of Rehabilitation Following Acute and Subacute Spinal Cord Injury: A Systematic Review," Glob. Spine J., vol. 7, no. 3_suppl, p. 175S-194S, Sep. 2017. https://doi.org/10.1177/2192568217703084 J. van Tuijl, Y. Janssen-Potten, and H. Seelen, "Evaluation of upper extremity motor function tests in tetraplegics," Spinal Cord, vol. 40, no. 2, pp. 51-64, Feb. 2002. https://doi.org/10.1038/sj.sc.3101261 Paralyzed Veterans of America Consortium for Spinal Cord Medicine, "Preservation of upper limb function following spinal cord injury: a clinical practice guideline for health-care professionals.," J. Spinal Cord Med., vol. 28, no. 5, pp. 434-470, Sep. 2005. https://doi.org/10.1080/10790268.2005.11753844 R. Rupp, S. C. Kleih, R. Leeb, J. del R. Millan, A. Kübler, and G. R. Müller-Putz, "Brain-Computer Interfaces and Assistive Technology," in Brain-Computer-Interfaces in their ethical, social and cultural contexts, vol. 12, Dordrecht: Springer Netherlands, 2014, pp. ...
Information about the open-access article Individually adapted imagery improves brain-computer interface performance in end-users with disability. in DOAJ. DOAJ is an online directory that indexes and provides access to quality open access, peer-reviewed journals.
Researchers from the University of Houston have shown for the first time that the use of a brain-computer interface augmented with a virtual walking avatar can control gait, suggesting the protocol may help patients recover the ability to walk after stroke, some spinal cord injuries and certain other gait disabilities.
A new (semantic) reflexive brain-computer interface: In search for a suitable c lassifier A. Furdea et al. University of Tubingen, Germany. Journal of Neuroscience Methods (2012 ) Presenter : Younghak Shin. GIST, Dept. of Information and Communication, INFONET Lab. Objective. Slideshow 2926260 by yama
Current experiments with brain-computer interfaces have allowed an amputee to feel with his prosthetic hand - what other wonders will we achieve with this technology?
The Paperback of the Brain-Computer Interface Research: A State-of-the-Art Summary by Christoph Guger at Barnes & Noble. FREE Shipping on $25 or more!
No longer a fringe sci-fi concept, were seeing big money being committed to the development of brain-computer interfaces. DARPA has now announced an investment of up to US$65 million across six projects as part of its new Neural Engineering System Design (NESD) program.​
Most investigators of brain computer interface (BCI) believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI) based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation.|br| Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task related states. |br| We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we
Most investigators of brain computer interface (BCI) believe that BCI can be achieved through induced neuronal activity from the cortex, but not by evoked neuronal activity. Motor imagery (MI) based BCI is one of the standard concepts of BCI, in that the user can generate induced activity by imagining motor movements. However, variations in performance over sessions and subjects are too severe to overcome easily; therefore, a basic understanding and investigation of BCI performance variation is necessary to find critical evidence of performance variation.|br| Here we present not only EEG datasets for MI BCI from 52 subjects, but also the results of a psychological and physiological questionnaire, EMG datasets, the locations of 3D EEG electrodes, and EEGs for non-task related states. |br| We validated our EEG datasets by using the percentage of bad trials, event-related desynchronization/synchronization (ERD/ERS) analysis, and classification analysis. After conventional rejection of bad trials, we
Physical therapy approaches are the de facto rehabilitation for stroke, which involve human therapists to assist stroke patients in recovering their motor ability. Modern rehabilitation technologies include robotics, functional electrical stimulation, transcranial magnetic stimulation and virtual reality. Robotic rehabilitation alleviates the labor-intensive aspects of physical rehabilitation by human therapists and could potentially improve the productivity of stroke rehabilitation. However, it is fundamentally based on movement repetition with visual feedback that helps stroke patients improve motor ability in their weak stroke-affected arms and legs. However, the robot is still able to move the weak part of the patient even if the patient is not attentive towards the training and thus the robotic training becomes a passive activity. In contrast, BCI-based robotic training works by ensuring active engagement by the hemiparetic patients in making a volitional movement. In addition, hemiplegic ...
A self-supporting headset worn on the ear of a user without a headband or placed in a stand as a desk-top microphone system by a switch-function installed in the headset. The headset includes a housing which accommodates a receiver, an arcuate earband connected by a spring to the housing and a pivotally connected boom microphone which extends from the housing to near the lips of a user. The earband can have a shape substantially corresponding to human ear structure for placement behind the ear of the user. The arcuate housing is placed over the entire outer ear to transmit audio signals without any need for an ear tube. An earband member can have a female member on the bottom surface so as to connect a member to mate with a male member located on the stand, to thereby secure the headset on the stand. The headset can be equipped with noise cancellation technology to remove background noise for optional use on with telephones, computers, or any the like.
If you are in the market for a good headset there are only so many companies available, one of them is Razer which is well known for their high end gaming accessories. They sent us their Chimaera 5,1 headset that we are going to review today. This headset works for both a PC and on a Xbox 360 but the price tag is quite steep since even Newegg sells them for around 180$ so expectations are obviously high. Lets get on with the review! About Razer: Find out more about Razer on their website, Specifications: Wireless Headset Circumaural Design with 50mm Driver Units Volume & Mic Control Buttons on the Headset 3 Preset EQ Detachable 2.5mm Microphone Cable Dimensions: 200mm(W) * 204mm(H) * 88mm(D) Inner Ear Cup Diameter: 55 mm / 2.16 Approximate Weight (including batteries): 369 g / 0.81 lbs Headphones Radio Frequency: 5.8GHz Dolby® Headphone / Dolby Pro Logic® II / Dolby Digital Wireless Range: 33ft / 10m Frequency Response: 20 - 20,000 Hz Impedance: 32O at 1kHz Sensitivity (@1kHz, 1V/Pa): 105dB +/- 2dB
Trust InSonic Chat Headset für PC+ Notebook (On-Ear, Black) - Colour group: Black, Colour designation: Black, Headphone + Headset area of use: Business, Headphone output: Stereo, Microphone: Yes, Headphone type: On-Ear, Fit: Headband, Isolation: Closed, Frequency range: 70 - 20000 Hz, Sensitivity: 108 dB, Impedance: 32 Ω (ohm), 3.5mm jack: 1 x, 2x 3.5mm jack: Yes, Headphone cable length: 1.8m, Cable management: Single-sided, Plug type: Flat, Weight: 60 g - Headphones + Headsets, Headset, In-Ear, Over-Ear, On-Ear, Earbud, Gaming Headset
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This paper describes a technique based on electroencephalography (EEG) to control a robot arm. This technology could eventually allow people with severe disabilities to control robots that can help them in daily living activities. The EEG-based Brain Computer Interface (BCI) developed consists in register the brain rhythmic activity through a electrodes situated on the scalp in order to differentiate one cognitive process from rest state and use it to control one degree of freedom of the robot arm. In the paper the processing and classifier algorithm are described and an analysis of their parameters has been made with the objective of find the optimum configuration that allow obtaining the best results ...
On the basis of solutions categorized by technology, electroencephalography (EEG) held the largest share of around 39.6% in 2015. Noninvasive BCI devices are expected to contribute the largest market share among all the other types in the coming years. However, the partially invasive BCI devices segment is expected to expand at the highest CAGR during the forecast period from 2016 to 2024. EEG is expected to witness significantly high adoption in media and entertainment applications in the coming years. The demand for noninvasive BCI devices is expected to increase in the coming years due to its ease of use, which involves the elimination of surgery. The demand for BCI technology is increasing in the health care industry as a part of efforts to provide better medical facilities to patients. On the basis of different applications covered, health care was the largest segment of the BCI market, accounting for over one third of the total market in 2015 ...
P300 and steady state visual evoked potential (SSVEP) are type of electroencephalography (EEG) phenomena that widely used in brain computer interface (BCI) systems since both of them have high signal response and signal noise ratio. Classification accuracy rate of signal, and signal detection time affect overall performance of BCI systems. These both values are used for calculation information transfer rate (ITR) that is a key performance indicator for a BCI system. A P300 based BCI or a SSVEP based BCI have higher ITR values than other type of BCI systems. Thus, in this study our aim was to use together these both P300 and SSVEP phenomena in a BCI speller. We proposed a hybrid BCI speller based on P300 and SSVEP. Moreover, our proposed BCI speller interface allows to use only P300 stimuli, only SSVEP stimuli, or hybrid stimuli. In this BCI speller, there are numbers in 3 × 3 matrix form for elicitind P300 signal and also 9 white square flickering objects were placed near numbers for eliciting ...
We proposed an EEG-based experiment framework that evaluates the performance of 3D interaction techniques in VR. Our lab environment integrates HTC VIVE, Leap Motion, and a 64-channel Compumedic EEG cap. The aims of our research are to 1) establish a continuous and objective measurement for the level of presence in VR, 2) induce design principles for content creation in VR, and 3) building a closed-loop VR + EEG platform that dynamically adjusts content presentation based on users cognition states.. ...
Computational Intelligence and Neuroscience is a forum for the interdisciplinary field of neural computing, neural engineering and artificial intelligence, where neuroscientists, cognitive scientists, engineers, psychologists, physicists, computer scientists, and artificial intelligence investigators among others can publish their work in one periodical that bridges the gap between neuroscience, artificial intelligence and engineering.
The home-brewed EEG/FNIRS software is made in Rebol and called "Jackson". Several versions have been created for dedicated purposes, like jprocessing or jLIVE, a sequencer style live tool, available from 1 to zillion brains, for art performance, cooperative "construction" (graphics, sound, music, MIDI, DMX light control, command,...). The Rebol option was chosen for dead simple portability and to interface seamlessly local and Internet connexions ...
Professional Experience:. (International). 8/1988 - 7/1989 Research Assistant, Intelligent Automation Research Lab, Purdue University, West Lafayette, Indiana.. 8/1989 - 3/1992 Research Assistant, Purdue-NSF Engineering Research Center for Intelligent Manufacturing Systems.. 4/1992 - 8/1992 Post-Doctoral Research Associate, School of Electrical Engineering, Purdue University, West Lafayette, Indiana.. 8/2000 - 7/2001 Chairman, IEEE Robotics and Automation - Taipei Chapter, Taipei, Taiwan.. 2/1999 - present Associate Editor, Asia Journal of Control.. 12/2000 - present Associate Editor, IEEE Transactions on Systems, Man, and Cybernetics, Part B.. 12/2001 - 12/2004 Associate Editor, Automatica.. 5/2002 - present Associate Editor, IEEE Transactions on Fuzzy Systems.. 10/2002 - present Associate Editor, International Journal of Speech Technology.. 12/2002 - 12/2009 Associate Editor, Journal of Information Science and Engineering.. 1/2004 - 12/2006 Associate Editor, IEEE Transactions on Circuits and ...
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