<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McFarland, D. J.</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EEG-based brain-computer interfaces</style></title><secondary-title><style face="normal" font="default" size="100%">Current Opinion in Biomedical Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">neurotechnology</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">Oct</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.ncbi.nlm.nih.gov/pubmed/21438193</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">194-200</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Brain–Computer Interfaces (BCIs) are real-time computer-based systems that translate brain signals into useful commands. To date most applications have been demonstrations of proof-of-principle; widespread use by people who could benefit from this technology requires further development. Improvements in current EEG recording technology are needed. Better sensors would be easier to apply, more comfortable for the user, and produce higher quality and more stable signals. Although considerable effort has been devoted to evaluating classifiers using public datasets, more attention to real-time signal processing issues and to optimizing the mutually adaptive interaction between the brain and the BCI are essential for improving BCI performance. Further development of applications is also needed, particularly applications of BCI technology to rehabilitation. The design of rehabilitation applications hinges on the nature of BCI control and how it might be used to induce and guide beneficial plasticity in the brain.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chadwick B. Boulay</style></author><author><style face="normal" font="default" size="100%">Xiang Yang Chen</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Electrocorticographic activity over sensorimotor cortex and motor function in awake behaving rats.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neurophysiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Neurophysiol.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">H-Reflex</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor control</style></keyword><keyword><style  face="normal" font="default" size="100%">Spinal Cord</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/25632076</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">113</style></volume><pages><style face="normal" font="default" size="100%">2232-41</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Sensorimotor cortex exerts both short-term and long-term control over the spinal reflex pathways that serve motor behaviors. Better understanding of this control could offer new possibilities for restoring function after central nervous system trauma or disease. We examined the impact of ongoing sensorimotor cortex (SMC) activity on the largely monosynaptic pathway of the H-reflex, the electrical analog of the spinal stretch reflex. In 41 awake adult rats, we measured soleus electromyographic (EMG) activity, the soleus H-reflex, and electrocorticographic activity over the contralateral SMC while rats were producing steady-state soleus EMG activity. Principal component analysis of electrocorticographic frequency spectra before H-reflex elicitation consistently revealed three frequency bands: μβ (5-30 Hz), low γ (γ1; 40-85 Hz), and high γ (γ2; 100-200 Hz). Ongoing (i.e., background) soleus EMG amplitude correlated negatively with μβ power and positively with γ1 power. In contrast, H-reflex size correlated positively with μβ power and negatively with γ1 power, but only when background soleus EMG amplitude was included in the linear model. These results support the hypothesis that increased SMC activation (indicated by decrease in μβ power and/or increase in γ1 power) simultaneously potentiates the H-reflex by exciting spinal motoneurons and suppresses it by decreasing the efficacy of the afferent input. They may help guide the development of new rehabilitation methods and of brain-computer interfaces that use SMC activity as a substitute for lost or impaired motor outputs.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Sarnacki, William A.</style></author><author><style face="normal" font="default" size="100%">Townsend, George</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The P300-based brain-computer interface (BCI): effects of stimulus rate.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">neuroprosthesis</style></keyword><keyword><style  face="normal" font="default" size="100%">P300</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21067970</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">731–737</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">OBJECTIVE:
Brain-computer interface technology can restore communication and control to people who are severely paralyzed. We have developed a non-invasive BCI based on the P300 event-related potential that uses an 8×9 matrix of 72 items that flash in groups of 6. Stimulus presentation rate (i.e., flash rate) is one of several parameters that could affect the speed and accuracy of performance. We studied performance (i.e., accuracy and characters/min) on copy spelling as a function of flash rate.
METHODS:
In the first study of six BCI users, stimulus-on and stimulus-off times were equal and flash rate was 4, 8, 16, or 32 Hz. In the second study of five BCI users, flash rate was varied by changing either the stimulus-on or stimulus-off time.
RESULTS:
For all users, lower flash rates gave higher accuracy. The flash rate that gave the highest characters/min varied across users, ranging from 8 to 32 Hz. However, variations in stimulus-on and stimulus-off times did not themselves significantly affect accuracy. Providing feedback did not affect results in either study suggesting that offline analyses should readily generalize to online performance. However there do appear to be session-specific effects that can influence the generalizability of classifier results.
CONCLUSIONS:
The results show that stimulus presentation (i.e., flash) rate affects the accuracy and speed of P300 BCI performance.
SIGNIFICANCE:
These results extend the range over which slower flash rates increase the amplitude of the P300. Considering also presentation time, the optimal rate differs among users, and thus should be set empirically for each user. Optimal flash rate might also vary with other parameters such as the number of items in the matrix.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Sarnacki, William A.</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Should the parameters of a BCI translation algorithm be continually adapted?.</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of neuroscience methods</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">adaptation</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21571004</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">199</style></volume><pages><style face="normal" font="default" size="100%">103–107</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">People with or without motor disabilities can learn to control sensorimotor rhythms (SMRs) recorded from the scalp to move a computer cursor in one or more dimensions or can use the P300 event-related potential as a control signal to make discrete selections. Data collected from individuals using an SMR-based or P300-based BCI were evaluated offline to estimate the impact on performance of continually adapting the parameters of the translation algorithm during BCI operation. The performance of the SMR-based BCI was enhanced by adaptive updating of the feature weights or adaptive normalization of the features. In contrast, P300 performance did not benefit from either of these procedures.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chadwick B. Boulay</style></author><author><style face="normal" font="default" size="100%">Sarnacki, W. A.</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Trained modulation of sensorimotor rhythms can affect reaction time.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">Reaction Time</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21411366</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">122</style></volume><pages><style face="normal" font="default" size="100%">1820–1826</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">OBJECTIVE:
Brain-computer interface (BCI) technology might be useful for rehabilitation of motor function. This speculation is based on the premise that modifying the EEG will modify behavior, a proposition for which there is limited empirical data. The present study examined the possibility that voluntary modulation of sensorimotor rhythm (SMR) can affect motor behavior in normal human subjects.
METHODS:
Six individuals performed a cued-reaction task with variable warning periods. A typical variable foreperiod effect was associated with SMR desynchronization. SMR features that correlated with reaction times were then used to control a two-target cursor movement BCI task. Following successful BCI training, an uncued reaction time task was embedded within the cursor movement task.
RESULTS:
Voluntarily increasing SMR beta rhythms was associated with longer reaction times than decreasing SMR beta rhythms.
CONCLUSIONS:
Voluntary modulation of EEG SMR can affect motor behavior.
SIGNIFICANCE:
These results encourage studies that integrate BCI training into rehabilitation protocols and examine its capacity to augment restoration of useful motor function.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Brain-computer interface research comes of age: traditional assumptions meet emerging realities.</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of motor behavior</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-machine interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">human</style></keyword><keyword><style  face="normal" font="default" size="100%">neuroprosthesis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21184352</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">42</style></volume><pages><style face="normal" font="default" size="100%">351–353</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Brain-computer interfaces (BCIs) could provide important new communication and control options for people with severe motor disabilities. Most BCI research to date has been based on 4 assumptions that: (a) intended actions are fully represented in the cerebral cortex; (b) neuronal action potentials can provide the best picture of an intended action; (c) the best BCI is one that records action potentials and decodes them; and (d) ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible. Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually as the CNS adapts to optimize performance. BCIs must track and guide these adaptations if they are to achieve and maintain good performance. Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users' intentions.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Townsend, G.</style></author><author><style face="normal" font="default" size="100%">LaPallo, B. K.</style></author><author><style face="normal" font="default" size="100%">Chadwick B. Boulay</style></author><author><style face="normal" font="default" size="100%">Krusienski, D. J.</style></author><author><style face="normal" font="default" size="100%">Frye, G. E.</style></author><author><style face="normal" font="default" size="100%">Hauser, C. K.</style></author><author><style face="normal" font="default" size="100%">Schwartz, N. E.</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">Sellers, E. W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A novel P300-based brain-computer interface stimulus presentation paradigm: moving beyond rows and columns.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-machine interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">event-related potential</style></keyword><keyword><style  face="normal" font="default" size="100%">P300</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/20347387</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">121</style></volume><pages><style face="normal" font="default" size="100%">1109–1120</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">OBJECTIVE:
An electroencephalographic brain-computer interface (BCI) can provide a non-muscular means of communication for people with amyotrophic lateral sclerosis (ALS) or other neuromuscular disorders. We present a novel P300-based BCI stimulus presentation - the checkerboard paradigm (CBP). CBP performance is compared to that of the standard row/column paradigm (RCP) introduced by Farwell and Donchin (1988).
METHODS:
Using an 8x9 matrix of alphanumeric characters and keyboard commands, 18 participants used the CBP and RCP in counter-balanced fashion. With approximately 9-12 min of calibration data, we used a stepwise linear discriminant analysis for online classification of subsequent data.
RESULTS:
Mean online accuracy was significantly higher for the CBP, 92%, than for the RCP, 77%. Correcting for extra selections due to errors, mean bit rate was also significantly higher for the CBP, 23 bits/min, than for the RCP, 17 bits/min. Moreover, the two paradigms produced significantly different waveforms. Initial tests with three advanced ALS participants produced similar results. Furthermore, these individuals preferred the CBP to the RCP.
CONCLUSIONS:
These results suggest that the CBP is markedly superior to the RCP in performance and user acceptability.
SIGNIFICANCE:
The CBP has the potential to provide a substantially more effective BCI than the RCP. This is especially important for people with severe neuromuscular disabilities.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Friedrich, Elisabeth V. C.</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Neuper, Christa</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Peter Brunner</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A scanning protocol for a sensorimotor rhythm-based brain-computer interface.</style></title><secondary-title><style face="normal" font="default" size="100%">Biological psychology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">BCI</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">scanning protocol</style></keyword><keyword><style  face="normal" font="default" size="100%">sensorimotor rhythm</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18786603</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">80</style></volume><pages><style face="normal" font="default" size="100%">169–175</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The scanning protocol is a novel brain-computer interface (BCI) implementation that can be controlled with sensorimotor rhythms (SMRs) of the electroencephalogram (EEG). The user views a screen that shows four choices in a linear array with one marked as target. The four choices are successively highlighted for 2.5s each. When a target is highlighted, the user can select it by modulating the SMR. An advantage of this method is the capacity to choose among multiple choices with just one learned SMR modulation. Each of 10 naive users trained for ten 30 min sessions over 5 weeks. User performance improved significantly (p&lt;0.001) over the sessions and ranged from 30 to 80% mean accuracy of the last three sessions (chance accuracy=25%). The incidence of correct selections depended on the target position. These results suggest that, with further improvements, a scanning protocol can be effective. The ultimate goal is to expand it to a large matrix of selections.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Klobassa, D. S.</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Peter Brunner</style></author><author><style face="normal" font="default" size="100%">Schwartz, N. E.</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">Neuper, C.</style></author><author><style face="normal" font="default" size="100%">Sellers, E. W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward a high-throughput auditory P300-based brain-computer interface.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-machine interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">event-related potential</style></keyword><keyword><style  face="normal" font="default" size="100%">P300</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19574091</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">120</style></volume><pages><style face="normal" font="default" size="100%">1252–1261</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">OBJECTIVE:
Brain-computer interface (BCI) technology can provide severely disabled people with non-muscular communication. For those most severely disabled, limitations in eye mobility or visual acuity may necessitate auditory BCI systems. The present study investigates the efficacy of the use of six environmental sounds to operate a 6x6 P300 Speller.
METHODS:
A two-group design was used to ascertain whether participants benefited from visual cues early in training. Group A (N=5) received only auditory stimuli during all 11 sessions, whereas Group AV (N=5) received simultaneous auditory and visual stimuli in initial sessions after which the visual stimuli were systematically removed. Stepwise linear discriminant analysis determined the matrix item that elicited the largest P300 response and thereby identified the desired choice.
RESULTS:
Online results and offline analyses showed that the two groups achieved equivalent accuracy. In the last session, eight of 10 participants achieved 50% or more, and four of these achieved 75% or more, online accuracy (2.8% accuracy expected by chance). Mean bit rates averaged about 2 bits/min, and maximum bit rates reached 5.6 bits/min.
CONCLUSIONS:
This study indicates that an auditory P300 BCI is feasible, that reasonable classification accuracy and rate of communication are achievable, and that the paradigm should be further evaluated with a group of severely disabled participants who have limited visual mobility.
SIGNIFICANCE:
With further development, this auditory P300 BCI could be of substantial value to severely disabled people who cannot use a visual BCI.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Nijboer, F.</style></author><author><style face="normal" font="default" size="100%">Sellers, E. W.</style></author><author><style face="normal" font="default" size="100%">Mellinger, J.</style></author><author><style face="normal" font="default" size="100%">Jordan, M. A.</style></author><author><style face="normal" font="default" size="100%">Matuz, T.</style></author><author><style face="normal" font="default" size="100%">Adrian Furdea</style></author><author><style face="normal" font="default" size="100%">S Halder</style></author><author><style face="normal" font="default" size="100%">Mochty, U.</style></author><author><style face="normal" font="default" size="100%">Krusienski, D. J.</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">Niels Birbaumer</style></author><author><style face="normal" font="default" size="100%">Kübler, A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A P300-based brain-computer interface for people with amyotrophic lateral sclerosis.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amyotrophic Lateral Sclerosis</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">electroencephalogram</style></keyword><keyword><style  face="normal" font="default" size="100%">event-related potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">P300</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18571984</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">119</style></volume><pages><style face="normal" font="default" size="100%">1909–1916</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">OBJECTIVE:
The current study evaluates the efficacy of a P300-based brain-computer interface (BCI) communication device for individuals with advanced ALS.
METHODS:
Participants attended to one cell of a N x N matrix while the N rows and N columns flashed randomly. Each cell of the matrix contained one character. Every flash of an attended character served as a rare event in an oddball sequence and elicited a P300 response. Classification coefficients derived using a stepwise linear discriminant function were applied to the data after each set of flashes. The character receiving the highest discriminant score was presented as feedback.
RESULTS:
In Phase I, six participants used a 6 x 6 matrix on 12 separate days with a mean rate of 1.2 selections/min and mean online and offline accuracies of 62% and 82%, respectively. In Phase II, four participants used either a 6 x 6 or a 7 x 7 matrix to produce novel and spontaneous statements with a mean online rate of 2.1 selections/min and online accuracy of 79%. The amplitude and latency of the P300 remained stable over 40 weeks.
CONCLUSIONS:
Participants could communicate with the P300-based BCI and performance was stable over many months.
SIGNIFICANCE:
BCIs could provide an alternative communication and control technology in the daily lives of people severely disabled by ALS.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krusienski, D. J.</style></author><author><style face="normal" font="default" size="100%">Sellers, E. W.</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward enhanced P300 speller performance.</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of neuroscience methods</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">event related potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">P300 speller</style></keyword><keyword><style  face="normal" font="default" size="100%">stepwise linear discriminant analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/17822777</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">167</style></volume><pages><style face="normal" font="default" size="100%">15–21</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brendan Z. Allison</style></author><author><style face="normal" font="default" size="100%">Wolpaw, Elizabeth Winter</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Brain-computer interface systems: progress and prospects.</style></title><secondary-title><style face="normal" font="default" size="100%">Expert review of medical devices</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ALS</style></keyword><keyword><style  face="normal" font="default" size="100%">assistive communication</style></keyword><keyword><style  face="normal" font="default" size="100%">BCI</style></keyword><keyword><style  face="normal" font="default" size="100%">BMI</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-acuated control</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-machine interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">ERP</style></keyword><keyword><style  face="normal" font="default" size="100%">locked-in syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">slow cortical potential</style></keyword><keyword><style  face="normal" font="default" size="100%">SSVEP</style></keyword><keyword><style  face="normal" font="default" size="100%">Stroke</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/17605682</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">463–474</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sellers, Eric W.</style></author><author><style face="normal" font="default" size="100%">Krusienski, Dean J.</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A P300 event-related potential brain-computer interface (BCI): the effects of matrix size and inter stimulus interval on performance.</style></title><secondary-title><style face="normal" font="default" size="100%">Biological psychology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Amyotrophic Lateral Sclerosis</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">electroencephalogram</style></keyword><keyword><style  face="normal" font="default" size="100%">event-related potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">P300</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/16860920</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">73</style></volume><pages><style face="normal" font="default" size="100%">242–252</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We describe a study designed to assess properties of a P300 brain-computer interface (BCI). The BCI presents the user with a matrix containing letters and numbers. The user attends to a character to be communicated and the rows and columns of the matrix briefly intensify. Each time the attended character is intensified it serves as a rare event in an oddball sequence and it elicits a P300 response. The BCI works by detecting which character elicited a P300 response. We manipulated the size of the character matrix (either 3 x 3 or 6 x 6) and the duration of the inter stimulus interval (ISI) between intensifications (either 175 or 350 ms). Online accuracy was highest for the 3 x 3 matrix 175-ms ISI condition, while bit rate was highest for the 6 x 6 matrix 175-ms ISI condition. Average accuracy in the best condition for each subject was 88%. P300 amplitude was significantly greater for the attended stimulus and for the 6 x 6 matrix. This work demonstrates that matrix size and ISI are important variables to consider when optimizing a BCI system for individual users and that a P300-BCI can be used for effective communication.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Sarnacki, William A.</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Brain-computer interface (BCI) operation: signal and noise during early training sessions.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">mu rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">sensorimotor cortex</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/15589184</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">116</style></volume><pages><style face="normal" font="default" size="100%">56–62</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">OBJECTIVE:
People can learn to control mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the electroencephalogram (EEG) recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The recorded signal may also contain electromyogram (EMG) and other non-EEG artifacts. This study examines the presence and characteristics of EMG contamination during new users' initial brain-computer interface (BCI) training sessions, as they first attempt to acquire control over mu or beta rhythm amplitude and to use that control to move a cursor to a target.
METHODS:
In the standard one-dimensional format, a target appears along the right edge of the screen and 1s later the cursor appears in the middle of the left edge and moves across the screen at a fixed rate with its vertical movement controlled by a linear function of mu or beta rhythm amplitude. In the basic two-choice version, the target occupies the upper or lower half of the right edge. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The present data comprise the first 10 sessions of BCI training from each of 7 users. Their data were selected to illustrate the variations seen in EMG contamination across users.
RESULTS:
Five of the 7 users learned to change rhythm amplitude appropriately, so that the cursor hit the target. Three of these 5 showed no evidence of EMG contamination. In the other two of these 5, EMG was prominent in early sessions, and tended to be associated with errors rather than with hits. As EEG control improved over the 10 sessions, this EMG contamination disappeared. In the remaining two users, who never acquired actual EEG control, EMG was prominent in initial sessions and tended to move the cursor to the target. This EMG contamination was still detectable by Session 10.
CONCLUSIONS:
EMG contamination arising from cranial muscles is often present early in BCI training and gradually wanes. In those users who eventually acquire EEG control, early target-related EMG contamination may be most prominent for unsuccessful trials, and may reflect user frustration. In those users who never acquire EEG control, EMG may initially serve to move the cursor toward the target. Careful and comprehensive topographical and spectral analyses throughout user training are essential for detecting EMG contamination and differentiating between cursor control provided by EEG control and cursor control provided by EMG contamination.
SIGNIFICANCE:
Artifacts such as EMG are common in EEG recordings. Comprehensive spectral and topographical analyses are necessary to detect them and ensure that they do not masquerade as, or interfere with acquisition of, actual EEG-based cursor control.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krusienski, Dean J</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Tracking of the mu rhythm using an empirically derived matched filter.</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE International Conference of Neural Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bioelectric potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Computer Interfaces</style></keyword><keyword><style  face="normal" font="default" size="100%">brain modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">communication device</style></keyword><keyword><style  face="normal" font="default" size="100%">communication system control</style></keyword><keyword><style  face="normal" font="default" size="100%">cortical mu rhythm modulation</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">empirically derived matched filter</style></keyword><keyword><style  face="normal" font="default" size="100%">handicapped aids</style></keyword><keyword><style  face="normal" font="default" size="100%">laboratories</style></keyword><keyword><style  face="normal" font="default" size="100%">matched filters</style></keyword><keyword><style  face="normal" font="default" size="100%">medical signal detection</style></keyword><keyword><style  face="normal" font="default" size="100%">medical signal processing</style></keyword><keyword><style  face="normal" font="default" size="100%">monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">motor imagery</style></keyword><keyword><style  face="normal" font="default" size="100%">mu rhythm tracking</style></keyword><keyword><style  face="normal" font="default" size="100%">noninvasive treatment</style></keyword><keyword><style  face="normal" font="default" size="100%">rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">synchronous motors</style></keyword><keyword><style  face="normal" font="default" size="100%">two-dimensional cursor control data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1419559</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Arlington, VA</style></pub-location><isbn><style face="normal" font="default" size="100%">0-7803-8710-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Benjamin Blankertz</style></author><author><style face="normal" font="default" size="100%">Müller, Klaus-Robert</style></author><author><style face="normal" font="default" size="100%">Curio, Gabriel</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">Schlögl, Alois</style></author><author><style face="normal" font="default" size="100%">Neuper, Christa</style></author><author><style face="normal" font="default" size="100%">Pfurtscheller, Gert</style></author><author><style face="normal" font="default" size="100%">Hinterberger, Thilo</style></author><author><style face="normal" font="default" size="100%">Schröder, Michael</style></author><author><style face="normal" font="default" size="100%">Niels Birbaumer</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE transactions on bio-medical engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">augmentative communication</style></keyword><keyword><style  face="normal" font="default" size="100%">BCI</style></keyword><keyword><style  face="normal" font="default" size="100%">beta-rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">ERP</style></keyword><keyword><style  face="normal" font="default" size="100%">imagined hand movements</style></keyword><keyword><style  face="normal" font="default" size="100%">lateralized readiness potential</style></keyword><keyword><style  face="normal" font="default" size="100%">mu-rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">P300</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword><keyword><style  face="normal" font="default" size="100%">single-trial classification</style></keyword><keyword><style  face="normal" font="default" size="100%">slow cortical potentials</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2004</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2004</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/15188876</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">51</style></volume><pages><style face="normal" font="default" size="100%">1044–1051</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Interest in developing a new method of man-to-machine communication–a brain-computer interface (BCI)–has grown steadily over the past few decades. BCIs create a new communication channel between the brain and an output device by bypassing conventional motor output pathways of nerves and muscles. These systems use signals recorded from the scalp, the surface of the cortex, or from inside the brain to enable users to control a variety of applications including simple word-processing software and orthotics. BCI technology could therefore provide a new communication and control option for individuals who cannot otherwise express their wishes to the outside world. Signal processing and classification methods are essential tools in the development of improved BCI technology. We organized the BCI Competition 2003 to evaluate the current state of the art of these tools. Four laboratories well versed in EEG-based BCI research provided six data sets in a documented format. We made these data sets (i.e., labeled training sets and unlabeled test sets) and their descriptions available on the Internet. The goal in the competition was to maximize the performance measure for the test labels. Researchers worldwide tested their algorithms and competed for the best classification results. This paper describes the six data sets and the results and function of the most successful algorithms.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sheikh, Hesham</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Sarnacki, William A.</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans.</style></title><secondary-title><style face="normal" font="default" size="100%">Neuroscience letters</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">augmentative communication</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-machine interface</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">mu and beta rhythms</style></keyword><keyword><style  face="normal" font="default" size="100%">neuroprosthesis</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2002</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/12821178</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">345</style></volume><pages><style face="normal" font="default" size="100%">89–92</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">People can learn to control electroencephalographic (EEG) sensorimotor rhythm amplitude so as to move a cursor to select among choices on a computer screen. We explored the dependence of system performance on EEG control. Users moved the cursor to reach a target at one of four possible locations. EEG control was measured as the correlation (r(2)) between rhythm amplitude and target location. Performance was measured as accuracy (% of targets hit) and as information transfer rate (bits/trial). The relationship between EEG control and accuracy can be approximated by a linear function that is constant for all users. The results facilitate offline predictions of the effects on performance of using different EEG features or combinations of features to control cursor movement.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Goncharova, I. I.</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EMG contamination of EEG: spectral and topographical characteristics.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">artifact</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">electroencephalogram</style></keyword><keyword><style  face="normal" font="default" size="100%">electromyogram</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2003</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/12948787</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">114</style></volume><pages><style face="normal" font="default" size="100%">1580–1593</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">OBJECTIVE:
Electromyogram (EMG) contamination is often a problem in electroencephalogram (EEG) recording, particularly, for those applications such as EEG-based brain-computer interfaces that rely on automated measurements of EEG features. As an essential prelude to developing methods for recognizing and eliminating EMG contamination of EEG, this study defines the spectral and topographical characteristics of frontalis and temporalis muscle EMG over the entire scalp. It describes both average data and the range of individual differences.
METHODS:
In 25 healthy adults, signals from 64 scalp and 4 facial locations were recorded during relaxation and during defined (15, 30, or 70% of maximum) contractions of frontalis or temporalis muscles.
RESULTS:
In the average data, EMG had a broad frequency distribution from 0 to &gt;200 Hz. Amplitude was greatest at 20-30 Hz frontally and 40-80 Hz temporally. Temporalis spectra also showed a smaller peak around 20 Hz. These spectral components attenuated and broadened centrally. Even with weak (15%) contraction, EMG was detectable (P&lt;0.001) near the vertex at frequencies &gt;12 Hz in the average data and &gt;8 Hz in some individuals.
CONCLUSIONS:
Frontalis or temporalis muscle EMG recorded from the scalp has spectral and topographical features that vary substantially across individuals. EMG spectra often have peaks in the beta frequency range that resemble EEG beta peaks.
SIGNIFICANCE:
While EMG contamination is greatest at the periphery of the scalp near the active muscles, even weak contractions can produce EMG that obscures or mimics EEG alpha, mu, or beta rhythms over the entire scalp. Recognition and elimination of this contamination is likely to require recording from an appropriate set of peripheral scalp locations.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Pfurtscheller, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EEG-based communication: presence of an error potential.</style></title><secondary-title><style face="normal" font="default" size="100%">Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">augmentative communication</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">error potential</style></keyword><keyword><style  face="normal" font="default" size="100%">error related negativity</style></keyword><keyword><style  face="normal" font="default" size="100%">event related potential</style></keyword><keyword><style  face="normal" font="default" size="100%">mu rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</style></keyword><keyword><style  face="normal" font="default" size="100%">sensorimotor cortex</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year><pub-dates><date><style  face="normal" font="default" size="100%">12/2000</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/11090763</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">111</style></volume><pages><style face="normal" font="default" size="100%">2138–2144</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">EEG-based communication could be a valuable new augmentative communication technology for those with severe motor disabilities. Like all communication methods, it faces the problem of errors in transmission. In the Wadsworth EEG-based brain-computer interface (BCI) system, subjects learn to use mu or beta rhythm amplitude to move a cursor to targets on a computer screen. While cursor movement is highly accurate in trained subjects, it is not perfect.</style></abstract></record></records></xml>