02272nas a2200277   4500008004100000245008600041210006900127260001200196520140600208653003301614653002901647653002001676653000901696653002501705653002401730653002001754653002201774100001401796700001401810700002401824700001501848700001901863700001901882700001601901856007701917       2015                        eng d00aBrain-to-text: Decoding spoken sentences from phone representations in the brain.0 aBraintotext Decoding spoken sentences from phone representations  c06/20153 aIt has long been speculated whether communication between humans and machines based on natural speech related cortical activity is possible. Over the past decade, studies have suggested that it is feasible to recognize isolated aspects of speech from neural signals, such as auditory features, phones or one of a few isolated words. However, until now it remained an unsolved challenge to decode continuously spoken speech from the neural substrate associated with speech and language processing. Here, we show for the first time that continuously spoken speech can be decoded into the expressed words from intracranial electrocorticographic (ECoG) recordings.Specifically, we implemented a system, which we call Brain-To-Text that models single phones, employs techniques from automatic speech recognition (ASR), and thereby transforms brain activity while speaking into the corresponding textual representation. Our results demonstrate that our system can achieve word error rates as low as 25% and phone error rates below 50%. Additionally, our approach contributes to the current understanding of the neural basis of continuous speech production by identifying those cortical regions that hold substantial information about individual phones. In conclusion, the Brain-To- Text system described in this paper represents an important step toward human-machine communication based on imagined speech.10aautomatic speech recognition10abrain-computer interface10abroadband gamma10aECoG10aElectrocorticography10apattern recognition10aspeech decoding10aspeech production1 aHerff, C.1 aHeger, D.1 ade Pesters, Adriana1 aTelaar, D.1 aBrunner, Peter1 aSchalk, Gerwin1 aSchultz, T.  uhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00217/abstract02073nas a2200241   4500008004100000022001400041245011100055210006900166260001200235300001100247490000700258520141600265653001001681653000801691653000901699653000801708653001201716653001101728653000801739100001701747700001901764856004801783       2015                        eng d  a1559-008900aNeuralAct: A Tool to Visualize Electrocortical (ECoG) Activity on a Three-Dimensional Model of the Cortex.0 aNeuralAct A Tool to Visualize Electrocortical ECoG Activity on a  c04/2015  a167-740 v133 a<p>Electrocorticography (ECoG) records neural signals directly from the surface of the cortex. Due to its high temporal and favorable spatial resolution, ECoG has emerged as a valuable new tool in acquiring cortical activity in cognitive and systems neuroscience. Many studies using ECoG visualized topographies of cortical activity or statistical tests on a three-dimensional model of the cortex, but a dedicated tool for this function has not yet been described. In this paper, we describe the NeuralAct package that serves this purpose. This package takes as input the 3D coordinates of the recording sensors, a cortical model in the same coordinate system (e.g., Talairach), and the activation data to be visualized at each sensor. It then aligns the sensor coordinates with the cortical model, convolves the activation data with a spatial kernel, and renders the resulting activations in color on the cortical model. The NeuralAct package can plot cortical activations of an individual subject as well as activations averaged over subjects. It is capable to render single images as well as sequences of images. The software runs under Matlab and is stable and robust. We here provide the tool and describe its visualization capabilities and procedures. The provided package contains thoroughly documented code and includes a simple demo that guides the researcher through the functionality of the tool.</p>10aBrain10aDOT10aECoG10aEEG10aimaging10aMatlab10aMEG1 aKubanek, Jan1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2538164102627nas a2200277   4500008004100000245009500041210006900136260001200205490000600217520181900223653002602042653001402068653000902082653000802091653002802099653000802127100002202135700002002157700001702177700001902194700001902213700002602232700002302258700002002281856004802301       2014                        eng d00aAssessing dynamics, spatial scale, and uncertainty in task-related brain network analyses.0 aAssessing dynamics spatial scale and uncertainty in taskrelated   c03/20140 v83 aThe brain is a complex network of interconnected elements, whose interactions evolve dynamically in time to cooperatively perform specific functions. A common technique to probe these interactions involves multi-sensor recordings of brain activity during a repeated task. Many techniques exist to characterize the resulting task-related activity, including establishing functional networks, which represent the statistical associations between brain areas. Although functional network inference is commonly employed to analyze neural time series data, techniques to assess the uncertainty—both in the functional network edges and the corresponding aggregate measures of network topology—are lacking. To address this, we describe a statistically principled approach for computing uncertainty in functional networks and aggregate network measures in task-related data. The approach is based on a resampling procedure that utilizes the trial structure common in experimental recordings. We show in simulations that this approach successfully identifies functional networks and associated measures of confidence emergent during a task in a variety of scenarios, including dynamically evolving networks. In addition, we describe a principled technique for establishing functional networks based on predetermined regions of interest using canonical correlation. Doing so provides additional robustness to the functional network inference. Finally, we illustrate the use of these methods on example invasive brain voltage recordings collected during an overt speech task. The general strategy described here—appropriate for static and dynamic network inference and different statistical measures of coupling—permits the evaluation of confidence in network measures in a variety of settings common to neuroscience.10acanonical correlation10acoherence10aECoG10aEEG10afunctional connectivity10aMEG1 aStephen, Emily, P1 aLepage, Kyle, Q1 aEden, Uri, T1 aBrunner, Peter1 aSchalk, Gerwin1 aBrumberg, Jonathan, S1 aGuenther, Frank, H1 aKramer, Mark, A  uhttp://www.ncbi.nlm.nih.gov/pubmed/2467829501311nas a2200193   4500008004100000022002500041245005000066210004900116260002800165520065200193653001800845653003000863653000900893653003700902100002100939700002100960700001900981856011701000       2012                        eng d  aISBN: 978-019538885500aBCIs That Use Electrocorticographic Activity.0 aBCIs That Use Electrocorticographic Activity  bOxford University Press3 aThis chapter discusses the potential of electrocorticography (ECoG) as a clinically useful brain-computer interface signal modality. ECoG has greater amplitude, higher topographical resolution, and a much broader frequency range than scalp-recorded electroencephalography and is less susceptible to artifacts. With current and foreseeable recording methodologies, ECoG is likely to have greater long-term stability than intracortically recorded signals. Furthermore, it can more readily be recorded from larger cortical areas, and it requires much lower digitization rates, thus greatly reducing the power requirements of wholly implanted systems.10abrain signals10abrain-computer interfaces10aECoG10aintracortically recorded signals1 aWolpaw, Jonathan1 aWinter-Wolpaw, E1 aSchalk, Gerwin  uhttp://www.oxfordscholarship.com/view/10.1093/acprof:oso/9780195388855.001.0001/acprof-9780195388855-chapter-01501958nas a2200253   4500008004100000245010000041210006900141260001200210490000600222520117500228653002901403653000901432653003401441653003001475653002401505653002901529100001201558700002001570700001901590700001801609700001001627700001901637856004801656       2012                        eng d00aDecoding Onset and Direction of Movements using Electrocorticographic (ECoG) Signals in Humans.0 aDecoding Onset and Direction of Movements using Electrocorticogr  c08/20120 v53 aCommunication of intent usually requires motor function. This requirement can be limiting when a person is engaged in a task, or prohibitive for some people suffering from neuromuscular disorders. Determining a person's intent, e.g., where and when to move, from brain signals rather than from muscles would have important applications in clinical or other domains. For example, detection of the onset and direction of intended movements may provide the basis for restoration of simple grasping function in people with chronic stroke, or could be used to optimize a user's interaction with the surrounding environment. Detecting the onset and direction of actual movements are a first step in this direction. In this study, we demonstrate that we can detect the onset of intended movements and their direction using electrocorticographic (ECoG) signals recorded from the surface of the cortex in humans. We also demonstrate in a simulation that the information encoded in ECoG about these movements may improve performance in a targeting task. In summary, the results in this paper suggest that detection of intended movement is possible, and may serve useful functions.10abrain computer interface10aECoG10amovement direction prediction10amovement onset prediction10aneurorehabilitation10aperformance augmentation1 aWang, Z1 aGunduz, Aysegul1 aBrunner, Peter1 aRitaccio, A L1 aJi, Q1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/22891058