@article {3570,
	title = {Prediction of subjective ratings of emotional pictures by EEG features.},
	journal = {Journal of neural engineering},
	volume = {14},
	year = {2017},
	month = {Feb},
	pages = {016009},
	abstract = {Emotion dysregulation is an important aspect of many psychiatric disorders. Brain-computer interface (BCI) technology could be a powerful new approach to facilitating therapeutic self-regulation of emotions. One possible BCI method would be to provide stimulus-specific feedback based on subject-specific electroencephalographic (EEG) responses to emotion-eliciting stimuli.},
	issn = {1741-2552},
	doi = {10.1088/1741-2552/14/1/016009},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/27934776},
	author = {Dennis J. McFarland and Parvaz, Muhammad A. and Sarnacki, William A. and Goldstein, Rita Z. and Jonathan Wolpaw}
}
@article {3569,
	title = {Effects of training pre-movement sensorimotor rhythms on behavioral performance.},
	journal = {Journal of neural engineering},
	volume = {12},
	year = {2015},
	month = {Dec},
	pages = {066021},
	abstract = {Brain-computer interface (BCI) technology might contribute to rehabilitation of motor function. This speculation is based on the premise that modifying the electroencephalographic (EEG) activity will modify behavior, a proposition for which there is limited empirical data. The present study asked whether learned modulation of pre-movement sensorimotor rhythm (SMR) activity can affect motor performance in normal human subjects.},
	keywords = {sensorimotor cortex},
	issn = {1741-2552},
	doi = {10.1088/1741-2560/12/6/066021},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/26529119},
	author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw}
}
@article {3096,
	title = {The P300-based brain-computer interface (BCI): effects of stimulus rate.},
	journal = {Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology},
	volume = {122},
	year = {2011},
	month = {04/2011},
	pages = {731{\textendash}737},
	abstract = {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{\texttimes}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.},
	keywords = {brain-computer interface, neuroprosthesis, P300},
	issn = {1872-8952},
	doi = {10.1016/j.clinph.2010.10.029},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21067970},
	author = {Dennis J. McFarland and Sarnacki, William A. and Townsend, George and Theresa M Vaughan and Jonathan Wolpaw}
}
@article {3091,
	title = {Should the parameters of a BCI translation algorithm be continually adapted?.},
	journal = {Journal of neuroscience methods},
	volume = {199},
	year = {2011},
	month = {07/2011},
	pages = {103{\textendash}107},
	abstract = {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.},
	keywords = {adaptation, brain-computer interface, EEG},
	issn = {1872-678X},
	doi = {10.1016/j.jneumeth.2011.04.037},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/21571004},
	author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw}
}
@article {3103,
	title = {Electroencephalographic (EEG) control of three-dimensional movement.},
	journal = {Journal of neural engineering},
	volume = {7},
	year = {2010},
	month = {06/2010},
	pages = {036007},
	abstract = {Brain-computer interfaces (BCIs) can use brain signals from the scalp (EEG), the cortical surface (ECoG), or within the cortex to restore movement control to people who are paralyzed. Like muscle-based skills, BCIs{\textquoteright} use requires activity-dependent adaptations in the brain that maintain stable relationships between the person{\textquoteright}s intent and the signals that convey it. This study shows that humans can learn over a series of training sessions to use EEG for three-dimensional control. The responsible EEG features are focused topographically on the scalp and spectrally in specific frequency bands. People acquire simultaneous control of three independent signals (one for each dimension) and reach targets in a virtual three-dimensional space. Such BCI control in humans has not been reported previously. The results suggest that with further development noninvasive EEG-based BCIs might control the complex movements of robotic arms or neuroprostheses.},
	keywords = {User-Computer Interface},
	issn = {1741-2552},
	doi = {10.1088/1741-2560/7/3/036007},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/20460690},
	author = {Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw}
}
@article {3131,
	title = {Emulation of computer mouse control with a noninvasive brain-computer interface.},
	journal = {Journal of neural engineering},
	volume = {5},
	year = {2008},
	month = {06/2008},
	pages = {101{\textendash}110},
	abstract = {Brain-computer interface (BCI) technology can provide nonmuscular communication and control to people who are severely paralyzed. BCIs can use noninvasive or invasive techniques for recording the brain signals that convey the user{\textquoteright}s commands. Although noninvasive BCIs are used for simple applications, it has frequently been assumed that only invasive BCIs, which use electrodes implanted in the brain, will be able to provide multidimensional sequential control of a robotic arm or a neuroprosthesis. The present study shows that a noninvasive BCI using scalp-recorded electroencephalographic (EEG) activity and an adaptive algorithm can provide people, including people with spinal cord injuries, with two-dimensional cursor movement and target selection. Multiple targets were presented around the periphery of a computer screen, with one designated as the correct target. The user{\textquoteright}s task was to use EEG to move a cursor from the center of the screen to the correct target and then to use an additional EEG feature to select the target. If the cursor reached an incorrect target, the user was instructed not to select it. Thus, this task emulated the key features of mouse operation. The results indicate that people with severe motor disabilities could use brain signals for sequential multidimensional movement and selection.},
	keywords = {User-Computer Interface},
	issn = {1741-2560},
	doi = {10.1088/1741-2560/5/2/001},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18367779},
	author = {Dennis J. McFarland and Krusienski, Dean J. and Sarnacki, William A. and Jonathan Wolpaw}
}
@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 {3145,
	title = {Electroencephalographic(EEG)-based communication: EEG control versus system performance in humans.},
	journal = {Neuroscience letters},
	volume = {345},
	year = {2003},
	month = {07/2002},
	pages = {89{\textendash}92},
	abstract = {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.},
	keywords = {augmentative communication, brain-computer interface, brain-machine interface, Electroencephalography, mu and beta rhythms, neuroprosthesis, Rehabilitation},
	issn = {0304-3940},
	doi = {10.1016/S0304-3940(03)00470-1},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12821178},
	author = {Sheikh, Hesham and Dennis J. McFarland and Sarnacki, William A. and Jonathan Wolpaw}
}
