@article {3386, title = {A practical, intuitive brain-computer interface for communicating {\textquoteright}yes{\textquoteright} or {\textquoteright}no{\textquoteright} by listening.}, journal = {J Neural Eng}, volume = {11}, year = {2014}, month = {06/2014}, pages = {035003}, abstract = {OBJECTIVE: Previous work has shown that it is possible to build an EEG-based binary brain-computer interface system (BCI) driven purely by shifts of attention to auditory stimuli. However, previous studies used abrupt, abstract stimuli that are often perceived as harsh and unpleasant, and whose lack of inherent meaning may make the interface unintuitive and difficult for beginners. We aimed to establish whether we could transition to a system based on more natural, intuitive stimuli (spoken words {\textquoteright}yes{\textquoteright} and {\textquoteright}no{\textquoteright}) without loss of performance, and whether the system could be used by people in the locked-in state. APPROACH: We performed a counterbalanced, interleaved within-subject comparison between an auditory streaming BCI that used beep stimuli, and one that used word stimuli. Fourteen healthy volunteers performed two sessions each, on separate days. We also collected preliminary data from two subjects with advanced amyotrophic lateral sclerosis (ALS), who used the word-based system to answer a set of simple yes-no questions. MAIN RESULTS: The N1, N2 and P3 event-related potentials elicited by words varied more between subjects than those elicited by beeps. However, the difference between responses to attended and unattended stimuli was more consistent with words than beeps. Healthy subjects{\textquoteright} performance with word stimuli (mean 77\% {\textpm} 3.3 s.e.) was slightly but not significantly better than their performance with beep stimuli (mean 73\% {\textpm} 2.8 s.e.). The two subjects with ALS used the word-based BCI to answer questions with a level of accuracy similar to that of the healthy subjects. SIGNIFICANCE: Since performance using word stimuli was at least as good as performance using beeps, we recommend that auditory streaming BCI systems be built with word stimuli to make the system more pleasant and intuitive. Our preliminary data show that word-based streaming BCI is a promising tool for communication by people who are locked in.}, keywords = {Adult, Aged, Algorithms, Auditory Perception, brain-computer interfaces, Communication Aids for Disabled, Electroencephalography, Equipment Design, Equipment Failure Analysis, Female, Humans, Male, Man-Machine Systems, Middle Aged, Quadriplegia, Treatment Outcome, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/11/3/035003}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24838278}, author = {Jeremy Jeremy Hill and Ricci, Erin and Haider, Sameah and McCane, Lynn M and Susan M Heckman and Jonathan Wolpaw and Theresa M Vaughan} } @article {2203, title = {Current Trends in Hardware and Software for Brain-Computer Interfaces (BCIs).}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {04/2011}, pages = {025001}, abstract = {

A\ brain-computer interface\ (BCI) provides a non-muscular communication channel to people with and without disabilities.\ BCI\ devices consist of hardware and software.\ BCI\ hardware records signals from the\ brain, either invasively or non-invasively, using a series of device components.\ BCI\ software then translates these signals into device output commands and provides feedback. One may categorize different types of\ BCI\ applications into the following four categories: basic\ research, clinical/translational\ research, consumer products, and emerging applications. These four categories use\ BCI\ hardware and software, but have different sets of requirements. For example, while basic\ research\ needs to explore a wide range of system configurations, and thus requires a wide range of hardware and software capabilities, applications in the other three categories may be designed for relatively narrow purposes and thus may only need a very limited subset of capabilities. This paper summarizes technical aspects for each of these four categories of\ BCI\ applications. The results indicate that\ BCI\ technology is in transition from isolated demonstrations to systematic\ research\ and commercial\ development. This process requires several multidisciplinary efforts, including the\ development\ of better integrated and more robust\ BCI\ hardware and software, the definition of standardized\ interfaces, and the\ developmentof certification, dissemination and reimbursement procedures.

}, keywords = {Biofeedback, Psychology, Brain, Brain Mapping, Electroencephalography, Equipment Design, Equipment Failure Analysis, Humans, Man-Machine Systems, Software, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/2/025001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21436536}, author = {Peter Brunner and Bianchi, L and Guger, C and Cincotti, F and Gerwin Schalk} } @article {2191, title = {Evolution of brain-computer interfaces: going beyond classic motor physiology.}, journal = {Neurosurg Focus}, volume = {27}, year = {2009}, month = {07/2009}, pages = {E4}, abstract = {

The notion that a computer can decode brain signals to infer the intentions of a human and then enact those intentions directly through a machine is becoming a realistic technical possibility. These types of devices are known as brain-computer interfaces (BCIs). The evolution of these neuroprosthetic technologies could have significant implications for patients with motor disabilities by enhancing their ability to interact and communicate with their environment. The cortical physiology most investigated and used for device control has been brain signals from the primary motor cortex. To date, this classic motor physiology has been an effective substrate for demonstrating the potential efficacy of BCI-based control. However, emerging research now stands to further enhance our understanding of the cortical physiology underpinning human intent and provide further signals for more complex brain-derived control. In this review, the authors report the current status of BCIs and detail the emerging research trends that stand to augment clinical applications in the future.

}, keywords = {Brain, Cerebral Cortex, Humans, Man-Machine Systems, Motor Cortex, Movement, Movement Disorders, Neuronal Plasticity, Prostheses and Implants, Research, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1092-0684}, doi = {10.3171/2009.4.FOCUS0979}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19569892}, author = {Leuthardt, E C and Gerwin Schalk and Roland, Jarod and Rouse, Adam and Moran, D} } @article {2183, title = {Brain-computer interfaces (BCIs): Detection Instead of Classification.}, journal = {J Neurosci Methods}, volume = {167}, year = {2008}, month = {01/2008}, pages = {51-62}, abstract = {

Many studies over the past two decades have shown that people can use\ brain\ signals to convey their intent to a\ computer\ through\ brain-computer interfaces\ (BCIs). These devices operate by recording signals from the\ brain\ and translating these signals into device commands. They can be used by people who are severely paralyzed to communicate without any use of muscle activity. One of the major impediments in translating this novel technology into\ clinical\ applications is the current requirement for preliminary analyses to identify the\ brain\ signal features best suited for communication. This paper introduces and validates signal detection, which does not require such analysis procedures, as a new concept in BCI signal processing. This detection concept is realized with Gaussian mixture models (GMMs) that are used to model resting\ brain\ activity so that any change in\ relevantbrain\ signals can be detected. It is implemented in a package called SIGFRIED (SIGnal modeling For Real-time Identification and Event Detection). The results indicate that SIGFRIED produces results that are within the range of those achieved using a common analysis strategy that requires preliminary identification of signal features. They indicate that such laborious analysis procedures could be replaced by merely recording\ brain\ signals during rest. In summary, this paper demonstrates how SIGFRIED could be used to overcome one of the present impediments to translation of laboratory BCI demonstrations into clinically practical applications.

}, keywords = {Adult, Algorithms, Brain, Brain Mapping, Electrocardiography, Electroencephalography, Humans, Male, Man-Machine Systems, Normal Distribution, Online Systems, Signal Detection, Psychological, Signal Processing, Computer-Assisted, Software Validation, User-Computer Interface}, issn = {0165-0270}, doi = {10.1016/j.jneumeth.2007.08.010}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17920134}, author = {Gerwin Schalk and Peter Brunner and Lester A Gerhardt and H Bischof and Jonathan Wolpaw} } @article {2176, title = {BCI meeting 2005 - Workshop on Technology: Hardware and Software.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {128-31}, abstract = {

This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop to review and evaluate the current state of BCI-related hardware and software. Technical requirements and current technologies, standardization procedures and future trends are covered. The main conclusion was recognition of the need to focus technical requirements on the users{\textquoteright} needs and the need for consistent standards in BCI research.

}, keywords = {Algorithms, Biotechnology, Brain, Communication Aids for Disabled, Computers, Electroencephalography, Equipment Design, Humans, Internationality, Man-Machine Systems, Neuromuscular Diseases, Software, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875584}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792276}, author = {Cincotti, F and Bianchi, L and Birch, Gary and Guger, C and Mellinger, J{\"u}rgen and Scherer, Reinhold and Schmidt, Robert N and Y{\'a}{\~n}ez Su{\'a}rez, Oscar and Gerwin Schalk} } @article {2175, title = {ECoG factors underlying multimodal control of a brain-computer interface.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {246-50}, abstract = {

Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.

}, keywords = {Adult, Brain Mapping, Cerebral Cortex, Communication Aids for Disabled, Computer Peripherals, Evoked Potentials, Female, Humans, Imagination, Male, Man-Machine Systems, Neuromuscular Diseases, Systems Integration, User-Computer Interface, Volition}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875570}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792305}, author = {Adam J Wilson and Felton, Elizabeth A and Garell, P Charles and Gerwin Schalk and Williams, Justin C} } @article {2178, title = {The emerging world of motor neuroprosthetics: a neurosurgical perspective.}, journal = {Neurosurgery}, volume = {59}, year = {2006}, month = {07/2006}, pages = {1-14; discussion 1-14}, abstract = {

A MOTOR NEUROPROSTHETIC device, or\ brain computer interface, is a machine that can take some type of signal from the\ brain\ and convert that information into overt device control such that it reflects the intentions of the user{\textquoteright}s\ brain. In essence, these constructs can decode the electrophysiological signals representing motor intent. With the parallel evolution of neuroscience, engineering, and rapid computing, the era of clinical neuroprosthetics is approaching as a practical reality for people with severe motor impairment. Patients with such diseases as spinal cord injury, stroke, limb loss, and neuromuscular disorders may benefit through the implantation of these\ brain computer interfaces\ that serve to augment their ability to communicate and interact with their environment. In the upcoming years, it will be important for the neurosurgeon to understand what a\ brain computer interface\ is, its fundamental principle of operation, and what the salient surgical issues are when considering implantation. We review the current state of the field of motor neuroprosthetics research, the early clinical applications, and the essential considerations from a neurosurgical perspective for the future.

}, keywords = {Brain, Humans, Man-Machine Systems, Movement, Neurosurgery, Prostheses and Implants, User-Computer Interface}, issn = {1524-4040}, doi = {10.1227/01.NEU.0000221506.06947.AC}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16823294}, author = {Leuthardt, E C and Gerwin Schalk and Moran, D and Ojemann, J G} }