02231nas a2200205   4500008004100000245007600041210006900117260001200186520158000198653001401778653000801792653001001800653002201810653003101832100001601863700002501879700002501904700001901929856007701948       2015                        eng d00aCortical alpha activity predicts the confidence in an impending action.0 aCortical alpha activity predicts the confidence in an impending   c07/20153 aWhen we make a decision, we experience a degree of confidence that our choice may lead to a desirable outcome. Recent studies in animals have probed the subjective aspects of the choice confidence using confidence-reporting tasks. These studies showed that estimates of the choice confidence substantially modulate neural activity in multiple regions of the brain. Building on these findings, we investigated the neural representation of the confidence in a choice in humans who explicitly reported the confidence in their choice. Subjects performed a perceptual decision task in which they decided between choosing a button press or a saccade while we recorded EEG activity. Following each choice, subjects indicated whether they were sure or unsure about the choice. We found that alpha activity strongly encodes a subject's confidence level in a forthcoming button press choice. The neural effect of the subjects' confidence was independent of the reaction time and independent of the sensory input modeled as a decision variable. Furthermore, the effect is not due to a general cognitive state, such as reward expectation, because the effect was specifically observed during button press choices and not during saccade choices. The neural effect of the confidence in the ensuing button press choice was strong enough that we could predict, from independent single trial neural signals, whether a subject was going to be sure or unsure of an ensuing button press choice. In sum, alpha activity in human cortex provides a window into the commitment to make a hand movement.10acertainty10aEEG10ahuman10aneural correlates10aperceptual decision-making1 aKubánek, J1 aHill, Jeremy, Jeremy1 aSnyder, Lawrence, H.1 aSchalk, Gerwin  uhttp://journal.frontiersin.org/article/10.3389/fnins.2015.00243/abstract02945nas a2200289   4500008004100000022001400041245010600055210006900161260001200230300000700242490000600249520210500255653001102360653002502371653001802396653001002414653001102424100001902435700001802454700002402472700002202496700001802518700002902536700002302565700001902588856004802607       2012                        eng d  a1662-516100aTemporal evolution of gamma activity in human cortex during an overt and covert word repetition task.0 aTemporal evolution of gamma activity in human cortex during an o  c05/2012  a990 v63 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Several scientists have proposed different models for cortical processing of speech. Classically, the regions participating in language were thought to be modular with a linear sequence of activations. More recently, modern theoretical models have posited a more hierarchical and distributed interaction of anatomic areas for the various stages of speech processing. Traditional imaging techniques can only define the location or time of cortical activation, which impedes the further evaluation and refinement of these models. In this study, we take advantage of recordings from the surface of the brain [electrocorticography (ECoG)], which can accurately detect the location and timing of cortical activations, to study the time course of ECoG high gamma (HG) modulations during an overt and covert word repetition task for different cortical areas. For overt word production, our results show substantial perisylvian cortical activations early in the perceptual phase of the task that were maintained through word articulation. However, this broad activation is attenuated during the expressive phase of covert word repetition. Across the different repetition tasks, the utilization of the different cortical sites within the perisylvian region varied in the degree of activation dependent on which stimulus was provided (</span><span class="highlight" style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">auditory</span><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">or visual cue) and whether the word was to be spoken or imagined. Taken together, the data support current models of speech that have been previously described with functional imaging. Moreover, this study demonstrates that the broad perisylvian speech network activates early and maintains suprathreshold activation throughout the word repetition task that appears to be modulated by the demands of different conditions.</span></p>10acortex10aElectrocorticography10agamma rhythms10ahuman10aSpeech1 aLeuthardt, E C1 aPei, Xiao-Mei1 aBreshears, Jonathan1 aGaona, Charles, M1 aSharma, Mohit1 aFreudenberg, Zachary, V.1 aBarbour, Dennis, L1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/2256331102079nas a2200205   4500008004100000022001400041245010100055210006900156260001200225300001400237490000700251520145100258653002901709653002801738653000801766653001001774653002001784100002101804856004801825       2010                        eng d  a1940-102700aBrain-computer interface research comes of age: traditional assumptions meet emerging realities.0 aBraincomputer interface research comes of age traditional assump  c11/2010  a351–3530 v423 aBrain-computer interfaces (BCIs) could provide important new communication and control options for people with severe motor disabilities. Most BCI research to date has been based on 4 assumptions that: (a) intended actions are fully represented in the cerebral cortex; (b) neuronal action potentials can provide the best picture of an intended action; (c) the best BCI is one that records action potentials and decodes them; and (d) ongoing mutual adaptation by the BCI user and the BCI system is not very important. In reality, none of these assumptions is presently defensible. Intended actions are the products of many areas, from the cortex to the spinal cord, and the contributions of each area change continually as the CNS adapts to optimize performance. BCIs must track and guide these adaptations if they are to achieve and maintain good performance. Furthermore, it is not yet clear which category of brain signals will prove most effective for BCI applications. In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. In sum, BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. Thus, the central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users' intentions.10abrain-computer interface10abrain-machine interface10aEEG10ahuman10aneuroprosthesis1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/21184352