@article {4295,
	title = {BCI-based sensorimotor rhythm training can affect individuated finger movements },
	journal = {Brain Computer Interface Society},
	volume = {7},
	year = {2020},
	month = {06/2020},
	chapter = {38},
	abstract = {Brain-computer interface (BCI) technology can restore communication and control to people who are severely paralyzed. BCI technology might also be able to enhance rehabilitation of motor function. We have previously shown that pre-movement sensorimotor rhythm (SMR) amplitude affects reaction time and performance on a joystick-based cursor movement task. The present study explores in adults without motor impairment the possibility that pre-movement SMR amplitude affects performance of individuated finger movements. In Phase 1, 8 individuals performed a finger flexion task that was monitored by an exoskeleton. During a 1-sec preparatory period, two colored targets on a video monitor cued flexion of the index finger, middle finger, both fingers, or neither finger; sudden color change then triggered the movement (or non-movement). SMR features (i.e. EEG amplitudes in specific frequency bands at specific scalp locations) in the pre-movement EEG that correlated with movement versus no movement were identified. In Phase 2, the participants learned to increase or decrease these SMR features to control a two-target BCI task. Finally, in Phase 3, they were asked to increase or decrease the SMR features to initiate the finger flexion task of Phase 1 and the impact on finger flexion performance was assessed. After BCI training, pre-movement SMR feature amplitude affected performance in a subset of individuals: lower amplitude was associated with shorter movement onset. In a subset of individuals, the beneficial effect on performance of lower SMR amplitude was greater when both fingers were flexed than when one was flexed and the other remained extended; thus, the impact of SMR amplitude modulation depended on the specificity of the subsequent motor task. These results indicate that BCI-based training of SMR activity in the pre-movement preparatory period can affect finger movements in a subset of individuals. They encourage studies that integrate such training into rehabilitation protocols and examine its capacity to enhance restoration of useful hand function.},
	keywords = {BCI, movement preparation, Rehabilitation, Robotics, sensorimotor beta rhythms},
	doi = {10.1080/2326263X.2020.1763060},
	url = {https://www.tandfonline.com/doi/abs/10.1080/2326263X.2020.1763060?journalCode=tbci20},
	author = {D.J. McFarland and S.L. Norman and W.A. Sarnacki and E.T. Wolbrecht and D.J. Reinkensmeyer and J.R. Wolpaw}
}
@article {4296,
	title = {Brain-computer interfaces: Definitions and principles},
	journal = {Handbook of Clinical Neurology},
	volume = {168},
	year = {2020},
	month = {03/2020},
	chapter = {15},
	abstract = {Throughout life, the central nervous system (CNS) interacts with the world and with the body by activating muscles and excreting hormones. In contrast, brain-computer interfaces (BCIs) quantify CNS activity and translate it into new artificial outputs that replace, restore, enhance, supplement, or improve the natural CNS outputs. BCIs thereby modify the interactions between the CNS and the environment. Unlike the natural CNS outputs that come from spinal and brainstem motoneurons, BCI outputs come from brain signals that represent activity in other CNS areas, such as the sensorimotor cortex. If BCIs are to be useful for important communication and control tasks in real life, the CNS must control these brain signals nearly as reliably and accurately as it controls spinal motoneurons. To do this, they might, for example, need to incorporate software that mimics the function of the subcortical and spinal mechanisms that participate in normal movement control. The realization of high reliability and accuracy is perhaps the most difficult and critical challenge now facing BCI research and development.

The ongoing adaptive modifications that maintain effective natural CNS outputs take place primarily in the CNS. The adaptive modifications that maintain effective BCI outputs can also take place in the BCI. This means that the BCI operation depends on the effective collaboration of two adaptive controllers, the CNS and the BCI. Realization of this second adaptive controller, the BCI, and management of its interactions with concurrent adaptations in the CNS comprise another complex and critical challenge for BCI development.

BCIs can use different kinds of brain signals recorded in different ways from different brain areas. Decisions about which signals recorded in which ways from which brain areas should be selected for which applications are empirical questions that can only be properly answered by experiments.

BCIs, like other communication and control technologies, often face artifacts that contaminate or imitate their chosen signals. Noninvasive BCIs (e.g., EEG- or fNIRS-based) need to take special care to avoid interpreting nonbrain signals (e.g., cranial EMG) as brain signals. This typically requires comprehensive topographical and spectral evaluations.

In theory, the outputs of BCIs can select a goal or control a process. In the future, the most effective BCIs will probably be those that combine goal selection and process control so as to distribute control between the BCI and the application in a fashion suited to the current action. Through such distribution, BCIs may most effectively imitate natural CNS operation.

The primary measure of BCI development is the extent to which BCI systems benefit people with neuromuscular disorders. Thus, BCI clinical evaluation, validation, and dissemination is a key step. It is at the same time a complex and difficult process that depends on multidisciplinary collaboration and management of the demanding requirements of clinical studies.

Twenty-five years ago, BCI research was an esoteric endeavor pursued in only a few isolated laboratories. It is now a steadily growing field that engages many hundreds of scientists, engineers, and clinicians throughout the world in an increasingly interconnected community that is addressing the key issues and pursuing the high potential of BCI technology.},
	keywords = {BCI, BMI, brain-computer interface, brain{\textendash}machine interface},
	doi = {10.1016/B978-0-444-63934-9.00002-0},
	url = {https://www.sciencedirect.com/science/article/pii/B9780444639349000020},
	author = {J.R. Wolpaw and Jos{\'e} del R. Mill{\'a}n and N.F. Ramsey}
}
@article {4297,
	title = {Operant Condition of the Flexor Carpi Radialis H-reflex},
	journal = {Archives of Physical Medicine and Rehabilitation },
	volume = {101},
	year = {2020},
	month = {12/2020},
	chapter = {145},
	abstract = {Operant conditioning of the largely monosynaptic H-reflex is a targeted and non-invasive therapeutic intervention for people with motor dysfunction after spinal cord injury and possibly stroke.1,2,3 It can complement other therapies and has no known adverse side effects. To date, H-reflex operant conditioning has focused on the leg. Here, we extend it to the arm by asking participants to either increase or decrease the flexor carpi radialis (FCR) H-reflex. In addition, we examine concurrent changes in brain activity by recording electroencephalographic activity (EEG).},
	keywords = {BCI, FCR, H-Reflex, operant conditioning, Rehabilitation},
	doi = {10.1016/j.apmr.2020.10.062},
	url = {https://www.archives-pmr.org/article/S0003-9993(20)31081-9/abstract},
	author = {J Norton and T Vaughan and D Gemoets and S Heckman and S.D. Toliou and J Carp and J.R. Wolpaw}
}
@article {4111,
	title = {Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke},
	journal = {Journal of Neural Engineering},
	volume = {15},
	year = {2018},
	month = {08/2018},
	abstract = {Objective. Brain{\textendash}computer interface (BCI) technology is attracting increasing interest as a tool for enhancing recovery of motor function after stroke, yet the optimal way to apply this technology is unknown. Here, we studied the immediate and therapeutic effects of BCI-based training to control pre-movement sensorimotor rhythm (SMR) amplitude on robot-assisted finger extension in people with stroke. Approach. Eight people with moderate to severe hand impairment due to chronic stroke completed a four-week three-phase protocol during which they practiced finger extension with assistance from the FINGER robotic exoskeleton. In Phase 1, we identified spatiospectral SMR features for each person that correlated with the intent to extend the index and/or middle finger(s). In Phase 2, the participants learned to increase or decrease SMR features given visual feedback, without movement. In Phase 3, the participants were cued to increase or decrease their SMR features, and when successful, were then cued to immediately attempt to extend the finger(s) with robot assistance. Main results. Of the four participants that achieved SMR control in Phase 2, three initiated finger extensions with a reduced reaction time after decreasing (versus increasing) pre-movement SMR amplitude during Phase 3. Two also extended at least one of their fingers more forcefully after decreasing pre-movement SMR amplitude. Hand function, measured by the box and block test (BBT), improved by 7.3  {\textpm}  7.5 blocks versus 3.5  {\textpm}  3.1 blocks in those with and without SMR control, respectively. Higher BBT scores at baseline correlated with a larger change in BBT score. Significance. These results suggest that learning to control person-specific pre-movement SMR features associated with finger extension can improve finger extension ability after stroke for some individuals. These results merit further investigation in a rehabilitation context.},
	keywords = {BCI, Motor control, Rehabilitation, robot, sensorimotor rhythm, Stroke},
	doi = {10.1088/1741-2552/aad724},
	url = {http://stacks.iop.org/1741-2552/15/i=5/a=056026},
	author = {Norman, SL and McFarland, DJ and Miner, A and Cramer, SC and Wolbrecht, ET and Jonathan Wolpaw and Reinkensmeyer, DJ}
}
@article {4112,
	title = {Real-time detection and discrimination of visual perception using electrocorticographic signals},
	journal = {Journal of Neural Engineering},
	volume = {15},
	year = {2018},
	month = {02/2018},
	abstract = {Several neuroimaging studies have demonstrated that the ventral temporal cortex contains specialized regions that process visual stimuli. This study investigated the spatial and temporal dynamics of electrocorticographic (ECoG) responses to different types and colors of visual stimulation that were presented to four human participants, and demonstrated a real-time decoder that detects and discriminates responses to untrained natural images. Approach. ECoG signals from the participants were recorded while they were shown colored and greyscale versions of seven types of visual stimuli (images of faces, objects, bodies, line drawings, digits, and kanji and hiragana characters), resulting in 14 classes for discrimination (experiment I). Additionally, a real-time system asynchronously classified ECoG responses to faces, kanji and black screens presented via a monitor (experiment II), or to natural scenes (i.e. the face of an experimenter, natural images of faces and kanji, and a mirror) (experiment III). Outcome measures in all experiments included the discrimination performance across types based on broadband γ activity. Main results. Experiment I demonstrated an offline classification accuracy of 72.9\% when discriminating among the seven types (without color separation). Further discrimination of grey versus colored images reached an accuracy of 67.1\%. Discriminating all colors and types (14 classes) yielded an accuracy of 52.1\%. In experiment II and III, the real-time decoder correctly detected 73.7\% responses to face, kanji and black computer stimuli and 74.8\% responses to presented natural scenes. Significance. Seven different types and their color information (either grey or color) could be detected and discriminated using broadband γ activity. Discrimination performance maximized for combined spatial-temporal information. The discrimination of stimulus color information provided the first ECoG-based evidence for color-related population-level cortical broadband γ responses in humans. Stimulus categories can be detected by their ECoG responses in real time within 500 ms with respect to stimulus onset.},
	keywords = {BCI, Brain{\textendash}computer interface, ECoG, gamma, high gamma mapping, real-time, visual},
	doi = {http://iopscience.iop.org/article/10.1088/1741-2552/aaa9f6/pdf},
	url = {http://iopscience.iop.org/article/10.1088/1741-2552/aaa9f6/pdf},
	author = {Kapeller, C and Ogawa, H and Schalk, G and Kunii, N and Coon, WG and Scharinger, J and Guger, C and Kamada, K}
}
@article {4095,
	title = {Differential roles of high gamma and local motor potentials for movement preparation and execution},
	journal = {Brain-Computer Interfaces},
	volume = {3},
	year = {2016},
	month = {May},
	pages = {88-102},
	abstract = {Determining a person{\textquoteright}s intent, such as the planned direction of their movement, directly from their cortical activity could support important applications such as brain-computer interfaces (BCIs). Continuing development of improved BCI systems requires a better understanding of how the brain prepares for and executes movements. To contribute to this understanding, we recorded surface cortical potentials (electrocorticographic signals; ECoG) in 11 human subjects performing a delayed center-out task to establish the differential role of high gamma activity (HGA) and the local motor potential (LMP) as a function of time and anatomical area during movement preparation and execution. High gamma modulations mostly confirm previous findings of sensorimotor cortex involvement, whereas modulations in LMPs are observed in prefrontal cortices. These modulations include directional information during movement planning as well as execution. Our results suggest that sampling signals from these widely distributed cortical areas improves decoding accuracy.},
	keywords = {BCI, brain-computer interfaces, ECoG, Electrocorticography, sensorimotor systems},
	doi = {https://doi.org/10.1080/2326263X.2016.1179087},
	author = {Gunduz, Aysegul and Peter Brunner and Sharma, Mohit and Leuthardt, Eric C. and Ritaccio, Anthony L. and Pesaran, Bijan and Schalk, Gerwin}
}
@article {2861,
	title = {Interactions Between Pre-Processing and Classification Methods for Event-Related-Potential Classification : Best-Practice Guidelines for Brain-Computer Interfacing.},
	journal = {Neuroinformatics},
	year = {2013},
	month = {04/2013},
	abstract = {Detecting event related potentials (ERPs) from single trials is critical to the operation of many stimulus-driven brain computer interface (BCI) systems. The low strength of the ERP signal compared to the noise (due to artifacts and BCI irrelevant brain processes) makes this a challenging signal detection problem. Previous work has tended to focus on how best to detect a single ERP type (such as the visual oddball response). However, the underlying ERP detection problem is essentially the same regardless of stimulus modality (e.g. visual or tactile), ERP component (e.g. P300 oddball response, or the error-potential), measurement system or electrode layout. To investigate whether a single ERP detection method might work for a wider range of ERP BCIs we compare detection performance over a large corpus of more than 50 ERP BCI datasets whilst systematically varying the electrode montage, spectral filter, spatial filter and classifier training methods. We identify an interesting interaction between spatial whitening and regularised classification which made detection performance independent of the choice of spectral filter low-pass frequency. Our results show that pipeline consisting of spectral filtering, spatial whitening, and regularised classification gives near maximal performance in all cases. Importantly, this pipeline is simple to implement and completely automatic with no expert feature selection or parameter tuning required. Thus, we recommend this combination as a "best-practice" method for ERP detection problems.},
	keywords = {BCI, decoding, EEG, ERP, LDA, spatial filtering, spectral filtering},
	issn = {1559-0089},
	doi = {10.1007/s12021-012-9171-0},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/23250668},
	author = {Farquhar, Jason and Jeremy Jeremy Hill}
}
@article {2541,
	title = {Review of the BCI Competition IV.},
	journal = {Frontiers in Neuroprosthetics},
	volume = {6},
	year = {2012},
	month = {07/2012},
	pages = {1-31},
	abstract = {The BCI competition IV stands in the tradition of prior BCI competitions that aim to provide high quality neuroscientific data for open access to the scientific community. As experienced already in prior competitions not only scientists from the narrow field of BCI compete, but scholars with a broad variety of backgrounds and nationalities. They include high specialists as well as students. The goals of all BCI competitions have always been to challenge with respect to novel paradigms and complex data. We report on the following challenges: (1) asynchronous data, (2) synthetic, (3) multi-class continuous data, (4) session-to-session transfer, (5) directionally modulated MEG, (6) finger movements recorded by ECoG. As after past competitions, our hope is that winning entries may enhance the analysis methods of future BCIs.},
	keywords = {BCI, brain-computer interface, competition},
	doi = {10.3389/fnins.2012.00055},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/22811657},
	author = {Tangermann, M. and Muller, K.R. and Aertsen, A. and Niels Birbaumer and Christoph Braun and Brunner, Clemens and Leeb, R. and Mehring, C. and Miller, K.J. and Mueller-Putz, G. and Nolte, G. and Pfurtscheller, G. and Preissl, H. and Gerwin Schalk and Schl{\"o}gl, A. and Vidaurre, C. and Waldert, S. and Benjamin Blankertz}
}
@conference {2261,
	title = {Brain-Computer Interaction.},
	booktitle = {5th Intl. Conference on Augmented Cognition},
	year = {2009},
	month = {2009},
	publisher = {Springer},
	organization = {Springer},
	abstract = {<p><span style="color: $\#$333333; font-family: {\textquoteright}Helvetica Neue{\textquoteright}, Arial, Helvetica, sans-serif; font-size: 13px; line-height: 20px;">Detection and automated interpretation of attention-related or intention-related brain activity carries significant promise for many military and civilian applications. This interpretation of brain activity could provide information about a person{\textquoteright}s intended movements, imagined movements, or attentional focus, and thus could be valuable for optimizing or replacing traditional motor-based communication between a person and a computer or other output devices. We describe here the objective and preliminary results of our studies in this area.</span></p>},
	keywords = {BCI, brain-computer interface, neural engineering, neural prosthesis},
	isbn = {978-3-642-02811-3},
	doi = {10.1007/978-3-642-02812-0_81},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-642-02812-0_81},
	author = {Peter Brunner and Gerwin Schalk}
}
@article {3113,
	title = {A scanning protocol for a sensorimotor rhythm-based brain-computer interface.},
	journal = {Biological psychology},
	volume = {80},
	year = {2009},
	month = {02/2009},
	pages = {169{\textendash}175},
	abstract = {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<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.},
	keywords = {BCI, brain-computer interface, scanning protocol, sensorimotor rhythm},
	issn = {1873-6246},
	doi = {10.1016/j.biopsycho.2008.08.004},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/18786603},
	author = {Friedrich, Elisabeth V. C. and Dennis J. McFarland and Neuper, Christa and Theresa M Vaughan and Peter Brunner and Jonathan Wolpaw}
}
@article {3207,
	title = {Brain-computer interface systems: progress and prospects.},
	journal = {Expert review of medical devices},
	volume = {4},
	year = {2007},
	month = {07/2007},
	pages = {463{\textendash}474},
	abstract = {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.},
	keywords = {ALS, assistive communication, BCI, BMI, brain-acuated control, brain-computer interface, brain-machine interface, EEG, ERP, locked-in syndrome, slow cortical potential, SSVEP, Stroke},
	issn = {1743-4440},
	doi = {10.1586/17434440.4.4.463},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17605682},
	author = {Brendan Z. Allison and Wolpaw, Elizabeth Winter and Jonathan Wolpaw}
}
@article {3133,
	title = {Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.},
	journal = {Progress in brain research},
	volume = {159},
	year = {2006},
	month = {02/2006},
	pages = {411{\textendash}419},
	abstract = {The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.},
	keywords = {adaptation, BCI, Signal Processing},
	issn = {0079-6123},
	doi = {10.1016/S0079-6123(06)59026-0},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/17071245},
	author = {Dennis J. McFarland and Krusienski, Dean J. and Jonathan Wolpaw}
}
@article {3222,
	title = {The BCI Competition 2003: progress and perspectives in detection and discrimination of EEG single trials.},
	journal = {IEEE transactions on bio-medical engineering},
	volume = {51},
	year = {2004},
	month = {06/2004},
	pages = {1044{\textendash}1051},
	abstract = {Interest in developing a new method of man-to-machine communication{\textendash}a brain-computer interface (BCI){\textendash}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.},
	keywords = {augmentative communication, BCI, beta-rhythm, brain-computer interface, EEG, ERP, imagined hand movements, lateralized readiness potential, mu-rhythm, P300, Rehabilitation, single-trial classification, slow cortical potentials},
	issn = {0018-9294},
	doi = {10.1109/TBME.2004.826692},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/15188876},
	author = {Benjamin Blankertz and M{\"u}ller, Klaus-Robert and Curio, Gabriel and Theresa M Vaughan and Gerwin Schalk and Jonathan Wolpaw and Schl{\"o}gl, Alois and Neuper, Christa and Pfurtscheller, Gert and Hinterberger, Thilo and Schr{\"o}der, Michael and Niels Birbaumer}
}
