<?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%">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%">Brain-computer interface (BCI) operation: optimizing information transfer rates.</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%">augmentative communication</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">information</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%">operant conditioning</style></keyword><keyword><style  face="normal" font="default" size="100%">prosthesis</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%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2003</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/12853169</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">63</style></volume><pages><style face="normal" font="default" size="100%">237–251</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 mu (8-12 Hz) or beta (18-25 Hz) rhythm amplitude in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. In the present version of the cursor movement task, vertical cursor movement is a linear function of mu or beta rhythm amplitude. At the same time the cursor moves horizontally from left to right at a fixed rate. A target occupies 50% (2-target task) to 20% (5-target task) of the right edge of the screen. The user's task is to move the cursor vertically so that it hits the target when it reaches the right edge. The goal of the present study was to optimize system performance. To accomplish this, we evaluated the impact on system performance of number of targets (i.e. 2-5) and trial duration (i.e. horizontal movement time from 1 to 4 s). Performance was measured as accuracy (percent of targets selected correctly) and also as bit rate (bits/min) (which incorporates, in addition to accuracy, speed and the number of possible targets). Accuracy declined as target number increased. At the same time, for six of eight users, four targets yielded the maximum bit rate. Accuracy increased as movement time increased. At the same time, the movement time with the highest bit rate varied across users from 2 to 4 s. These results indicate that task parameters such as target number and trial duration can markedly affect system performance. They also indicate that optimal parameter values vary across users. Selection of parameters suited both to the specific user and the requirements of the specific application is likely to be a key factor in maximizing the success of EEG-based communication and control.</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%">McCane, L. M.</style></author><author><style face="normal" font="default" size="100%">David, S. V.</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%">Spatial filter selection for EEG-based communication.</style></title><secondary-title><style face="normal" font="default" size="100%">Electroencephalography and clinical neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">assistive communication</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">mu rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">operant conditioning</style></keyword><keyword><style  face="normal" font="default" size="100%">prosthesis</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%">1997</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/1997</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/9305287</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">103</style></volume><pages><style face="normal" font="default" size="100%">386–394</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Individuals can learn to control the amplitude of mu-rhythm activity in the EEG recorded over sensorimotor cortex and use it to move a cursor to a target on a video screen. The speed and accuracy of cursor movement depend on the consistency of the control signal and on the signal-to-noise ratio achieved by the spatial and temporal filtering methods that extract the activity prior to its translation into cursor movement. The present study compared alternative spatial filtering methods. Sixty-four channel EEG data collected while well-trained subjects were moving the cursor to targets at the top or bottom edge of a video screen were analyzed offline by four different spatial filters, namely a standard ear-reference, a common average reference (CAR), a small Laplacian (3 cm to set of surrounding electrodes) and a large Laplacian (6 cm to set of surrounding electrodes). The CAR and large Laplacian methods proved best able to distinguish between top and bottom targets. They were significantly superior to the ear-reference method. The difference in performance between the large Laplacian and small Laplacian methods presumably indicated that the former was better matched to the topographical extent of the EEG control signal. The results as a whole demonstrate the importance of proper spatial filter selection for maximizing the signal-to-noise ratio and thereby improving the speed and accuracy of EEG-based 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%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">Flotzinger, D.</style></author><author><style face="normal" font="default" size="100%">Pfurtscheller, G.</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%">Timing of EEG-based cursor control.</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">assistive communication</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">mu rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">operant conditioning</style></keyword><keyword><style  face="normal" font="default" size="100%">prosthesis</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%">1997</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/1997</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/9458060</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">529–538</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Recent studies show that humans can learn to control the amplitude of electroencephalography (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could be a valuable new communication and control option for those with severe motor disabilities. Realization of this potential requires detailed knowledge of the characteristic features of EEG control. This study examined the course of EEG control after presentation of a target. At the beginning of each trial, a target appeared at the top or bottom edge of the subject's video screen and 1 sec later a cursor began to move vertically as a function of EEG amplitude in a specific frequency band. In well-trained subjects, this amplitude was high at the time the target appeared and then either remained high (i.e., for a top target) or fell rapidly (i.e., for a bottom target). Target-specific EEG amplitude control began 0.5 sec after the target appeared and appeared to wax and wane with a period of approximately 1 sec until the cursor reached the target (i.e., a hit) or the opposite edge of the screen (i.e., a miss). Accuracy was 90% or greater for each subject. Top-target errors usually occurred later in the trial because of failure to reach and/or maintain sufficiently high amplitude, whereas bottom-target errors usually occurred immediately because of failure to reduce an initially high amplitude quickly enough. The results suggest modifications that could improve performance. These include lengthening the intertrial period, shortening the delay between target appearance and cursor movement, and including time within the trial as a variable in the equation that translates EEG into 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%">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%">Multichannel EEG-based brain-computer communication.</style></title><secondary-title><style face="normal" font="default" size="100%">Electroencephalography and clinical neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">assistive communication</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">mu rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">operant conditioning</style></keyword><keyword><style  face="normal" font="default" size="100%">prosthesis</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%">1994</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/1994</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/7515787</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">90</style></volume><pages><style face="normal" font="default" size="100%">444–449</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Individuals who are paralyzed or have other severe movement disorders often need alternative means for communicating with and controlling their environments. In this study, human subjects learned to use two channels of bipolar EEG activity to control 2-dimensional movement of a cursor on a computer screen. Amplitudes of 8-12 Hz activity in the EEG recorded from the scalp across right and left central sulci were determined by fast Fourier transform and combined to control vertical and horizontal cursor movements simultaneously. This independent control of two separate EEG channels cannot be attributed to a non-specific change in brain activity and appeared to be specific to the mu rhythm frequency range. With further development, multichannel EEG-based communication may prove of significant value to those with severe motor 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%">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%">Neat, G. W.</style></author><author><style face="normal" font="default" size="100%">Forneris, C. A.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An EEG-based brain-computer interface for cursor control.</style></title><secondary-title><style face="normal" font="default" size="100%">Electroencephalography and clinical neurophysiology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Communication</style></keyword><keyword><style  face="normal" font="default" size="100%">computer control</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">mu rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">operant conditioning</style></keyword><keyword><style  face="normal" font="default" size="100%">prosthesis</style></keyword><keyword><style  face="normal" font="default" size="100%">sensorimotor rhythm</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1991</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/1991</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/1707798</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">78</style></volume><pages><style face="normal" font="default" size="100%">252–259</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This study began development of a new communication and control modality for individuals with severe motor deficits. We trained normal subjects to use the 8-12 Hz mu rhythm recorded from the scalp over the central sulcus of one hemisphere to move a cursor from the center of a video screen to a target located at the top or bottom edge. Mu rhythm amplitude was assessed by on-line frequency analysis and translated into cursor movement: larger amplitudes moved the cursor up and smaller amplitudes moved it down. Over several weeks, subjects learned to change mu rhythm amplitude quickly and accurately, so that the cursor typically reached the target in 3 sec. The parameters that translated mu rhythm amplitudes into cursor movements were derived from evaluation of the distributions of amplitudes in response to top and bottom targets. The use of these distributions was a distinctive feature of this study and the key factor in its success. Refinements in training procedures and in the distribution-based method used to translate mu rhythm amplitudes into cursor movements should further improve this 1-dimensional control. Achievement of 2-dimensional control is under study. The mu rhythm may provide a significant new communication and control option for disabled individuals.</style></abstract></record></records></xml>