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/abstract02146nas a2200397   4500008004100000022001400041245009000055210006900145260001200214300001100226490000600237520112800243653001501371653001001386653001701396653001001413653002101423653001301444653001101457653001201468653001101480653000901491653002001500653001601520653001901536653000901555653001001564653001701574653001601591100001601607700002001623700001701643700002101660700001901681856004801700       2009                        eng d  a1741-255200aDecoding flexion of individual fingers using electrocorticographic signals in humans.0 aDecoding flexion of individual fingers using electrocorticograph  c12/2009  a0660010 v63 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Brain signals can provide the basis for a non-muscular communication and control system, a brain-computer interface (BCI), for people with motor disabilities. A common approach to creating BCI devices is to decode kinematic parameters of movements using signals recorded by intracortical microelectrodes. Recent studies have shown that kinematic parameters of hand movements can also be accurately decoded from signals recorded by electrodes placed on the surface of the brain (electrocorticography (ECoG)). In the present study, we extend these results by demonstrating that it is also possible to decode the time course of the flexion of individual fingers using ECoG signals in humans, and by showing that these flexion time courses are highly specific to the moving finger. These results provide additional support for the hypothesis that ECoG could be the basis for powerful clinically practical BCI systems, and also indicate that ECoG is useful for studying cortical dynamics related to motor function.</span></p>10aAdolescent10aAdult10aBiomechanics10aBrain10aElectrodiagnosis10aEpilepsy10aFemale10aFingers10aHumans10aMale10aMicroelectrodes10aMiddle Aged10aMotor Activity10aRest10aThumb10aTime Factors10aYoung Adult1 aKubánek, J1 aMiller, John, W1 aOjemann, J G1 aWolpaw, Jonathan1 aSchalk, Gerwin  uhttp://www.ncbi.nlm.nih.gov/pubmed/1979423702357nas a2200385   4500008004100000022001400041245009800055210006900153260001200222300001100234490000600245520131000251653001001561653001501571653000801586653001801594653002001612653002701632653002901659653001101688653001101699653000901710653001301719100001901732700001601751700002001767700002601787700001901813700001701832700001601849700001301865700002401878700002101902856004801923       2007                        eng d  a1741-256000aDecoding two-dimensional movement trajectories using electrocorticographic signals in humans.0 aDecoding twodimensional movement trajectories using electrocorti  c09/2007  a264-750 v43 a<p><span style="font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;">Signals from the brain could provide a non-muscular communication and control system, a brain-computer interface (BCI), for people who are severely paralyzed. A common BCI research strategy begins by decoding kinematic parameters from brain signals recorded during actual arm movement. It has been assumed that these parameters can be derived accurately only from signals recorded by intracortical microelectrodes, but the long-term stability of such electrodes is uncertain. The present study disproves this widespread assumption by showing in humans that kinematic parameters can also be decoded from signals recorded by subdural electrodes on the cortical surface (ECoG) with an accuracy comparable to that achieved in monkey studies using intracortical microelectrodes. A new ECoG feature labeled the local motor potential (LMP) provided the most information about movement. Furthermore, features displayed cosine tuning that has previously been described only for signals recorded within the brain. These results suggest that ECoG could be a more stable and less invasive alternative to intracortical electrodes for BCI systems, and could also prove useful in studies of motor function.</span></p>10aAdult10aAlgorithms10aArm10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEvoked Potentials, Motor10aFemale10aHumans10aMale10aMovement1 aSchalk, Gerwin1 aKubánek, J1 aMiller, John, W1 aAnderson, Nicholas, R1 aLeuthardt, E C1 aOjemann, J G1 aLimbrick, D1 aMoran, D1 aGerhardt, Lester, A1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/17873429