@article {3138, title = {Brain-computer interface (BCI) operation: signal and noise during early training sessions.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {116}, year = {2005}, month = {01/2005}, pages = {56{\textendash}62}, abstract = {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{\textquoteright} 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{\textquoteright}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.}, keywords = {brain-computer interface, EEG, Electroencephalography, Learning, mu rhythm, sensorimotor cortex}, issn = {1388-2457}, doi = {10.1016/j.clinph.2004.07.004}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15589184}, author = {Dennis J. McFarland and Sarnacki, William A. and Theresa M Vaughan and Jonathan Wolpaw} } @article {3144, title = {Brain-computer interface (BCI) operation: optimizing information transfer rates.}, journal = {Biological psychology}, volume = {63}, year = {2003}, month = {07/2003}, pages = {237{\textendash}251}, abstract = {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{\textquoteright}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.}, keywords = {augmentative communication, Electroencephalography, information, Learning, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0301-0511}, doi = {10.1016/S0301-0511(03)00073-5}, url = {http://www.ncbi.nlm.nih.gov/pubmed/12853169}, author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw} } @article {3234, title = {EEG-based communication: presence of an error potential.}, journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology}, volume = {111}, year = {2000}, month = {12/2000}, pages = {2138{\textendash}2144}, abstract = {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.}, keywords = {augmentative communication, brain-computer interface, Electroencephalography, error potential, error related negativity, event related potential, mu rhythm, Rehabilitation, sensorimotor cortex}, issn = {1388-2457}, doi = {10.1016/S1388-2457(00)00457-0}, url = {http://www.ncbi.nlm.nih.gov/pubmed/11090763}, author = {Gerwin Schalk and Jonathan Wolpaw and Dennis J. McFarland and Pfurtscheller, G.} } @article {3238, title = {Mu and beta rhythm topographies during motor imagery and actual movements.}, journal = {Brain topography}, volume = {12}, year = {2000}, month = {03/2000}, pages = {177{\textendash}186}, abstract = {People can learn to control the 8-12 Hz mu rhythm and/or the 18-25 Hz beta rhythm in the EEG recorded over sensorimotor cortex and use it to control a cursor on a video screen. Subjects often report using motor imagery to control cursor movement, particularly early in training. We compared in untrained subjects the EEG topographies associated with actual hand movement to those associated with imagined hand movement. Sixty-four EEG channels were recorded while each of 33 adults moved left- or right-hand or imagined doing so. Frequency-specific differences between movement or imagery and rest, and between right- and left-hand movement or imagery, were evaluated by scalp topographies of voltage and r spectra, and principal component analysis. Both movement and imagery were associated with mu and beta rhythm desynchronization. The mu topographies showed bilateral foci of desynchronization over sensorimotor cortices, while the beta topographies showed peak desynchronization over the vertex. Both mu and beta rhythm left/right differences showed bilateral central foci that were stronger on the right side. The independence of mu and beta rhythms was demonstrated by differences for movement and imagery for the subjects as a group and by principal components analysis. The results indicated that the effects of imagery were not simply an attenuated version of the effects of movement. They supply evidence that motor imagery could play an important role in EEG-based communication, and suggest that mu and beta rhythms might provide independent control signals.}, keywords = {beta rhythm, EEG, imagery, mu rhythm, sensorimotor cortex}, issn = {0896-0267}, doi = {10.1023/A:1023437823106}, url = {http://www.ncbi.nlm.nih.gov/pubmed/10791681}, author = {Dennis J. McFarland and Miner, L. A. and Theresa M Vaughan and Jonathan Wolpaw} } @article {3241, title = {EEG-based communication: analysis of concurrent EMG activity.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {107}, year = {1998}, month = {12/1998}, pages = {428{\textendash}433}, abstract = {OBJECTIVE: Recent studies indicate that people can learn to control the amplitude of mu or beta rhythms in the EEG recorded from the scalp over sensorimotor cortex and can use that control to move a cursor to targets on the computer screen. While subjects do not move during performance, it is possible that inapparent or unconscious muscle contractions contribute to the changes in the mu and beta rhythm activity responsible for cursor movement. We evaluated this possibility. METHODS: EMG was recorded from 10 distal limb muscle groups while five trained subjects used mu or beta rhythms to move a cursor to targets at the bottom or top edge of a computer screen. RESULTS: EMG activity was very low during performance, averaging 4.0+/-4.4\% (SD) of maximum voluntary contraction. Most important, the correlation, measured as r2, between target position and EMG activity averaged only 0.01+/-0.02, much lower than the correlation between target position and the EEG activity that controlled cursor movement, which averaged 0.39+/-0.18. CONCLUSIONS: These results strongly support the conclusion that EEG-based cursor control does no depend on concurrent muscle activity. EEG-based communication and control might provide a new augmentative communication option for those with severe motor disabilities.}, keywords = {augmentative communication, conditioning, Electroencephalography, Electromyography, mu rhythm, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/S0013-4694(98)00107-2}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9922089}, author = {Theresa M Vaughan and Miner, L. A. and Dennis J. McFarland and Jonathan Wolpaw} } @article {3249, title = {Spatial filter selection for EEG-based communication.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {103}, year = {1997}, month = {09/1997}, pages = {386{\textendash}394}, abstract = {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.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/S0013-4694(97)00022-2}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9305287}, author = {Dennis J. McFarland and McCane, L. M. and David, S. V. and Jonathan Wolpaw} } @article {3245, title = {Timing of EEG-based cursor control.}, journal = {Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society}, volume = {14}, year = {1997}, month = {11/1997}, pages = {529{\textendash}538}, abstract = {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{\textquoteright}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.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0736-0258}, url = {http://www.ncbi.nlm.nih.gov/pubmed/9458060}, author = {Jonathan Wolpaw and Flotzinger, D. and Pfurtscheller, G. and Dennis J. McFarland} } @article {3262, title = {Multichannel EEG-based brain-computer communication.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {90}, year = {1994}, month = {06/1994}, pages = {444{\textendash}449}, abstract = {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.}, keywords = {assistive communication, Electroencephalography, mu rhythm, operant conditioning, prosthesis, Rehabilitation, sensorimotor cortex}, issn = {0013-4694}, doi = {10.1016/0013-4694(94)90135-X}, url = {http://www.ncbi.nlm.nih.gov/pubmed/7515787}, author = {Jonathan Wolpaw and Dennis J. McFarland} } @article {3267, title = {An EEG-based brain-computer interface for cursor control.}, journal = {Electroencephalography and clinical neurophysiology}, volume = {78}, year = {1991}, month = {03/1991}, pages = {252{\textendash}259}, abstract = {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.}, keywords = {Communication, computer control, EEG, mu rhythm, operant conditioning, prosthesis, sensorimotor rhythm}, issn = {0013-4694}, doi = {10.1016/0013-4694(91)90040-B}, url = {http://www.ncbi.nlm.nih.gov/pubmed/1707798}, author = {Jonathan Wolpaw and Dennis J. McFarland and Neat, G. W. and Forneris, C. A.} }