04066nas a2200277 4500008004100000022001400041245007100055210006900126260001200195300001100207490000700218520327200225653001003497653002003507653002703527653001103554653001103565653001603576653000903592653004103601653002803642100002703670700001803697700002503715856004803740 2011 eng d a1095-957200aCausal influence of gamma oscillations on the sensorimotor rhythm.0 aCausal influence of gamma oscillations on the sensorimotor rhyth c05/2011 a837-420 v563 a
Gamma oscillations of the electromagnetic field of the brain are known to be involved in a variety of cognitive processes, and are believed to be fundamental for information processing within the brain. While gamma oscillations have been shown to be correlated with brain rhythms at different frequencies, to date no empirical evidence has been presented that supports a causal influence of gamma oscillations on other brain rhythms. In this work, we study the relation of gamma oscillations and the sensorimotor rhythm (SMR) in healthy human subjects using electroencephalography. We first demonstrate that modulation of the SMR, induced by motor imagery of either the left or right hand, is positively correlated with the power of frontal and occipital gamma oscillations, and negatively correlated with the power of centro-parietal gamma oscillations. We then demonstrate that the most simple causal structure, capable of explaining the observed correlation of gamma oscillations and the SMR, entails a causal influence of gamma oscillations on the SMR. This finding supports the fundamental role attributed to gamma oscillations for information processing within the brain, and is of particular importance for brain-computer interfaces (BCIs). As modulation of the SMR is typically used in BCIs to infer a subject's intention, our findings entail that gamma oscillations have a causal influence on a subject's capability to utilize a BCI for means of communication.
10aAdult10aCerebral Cortex10aElectroencephalography10aFemale10aHumans10aImagination10aMale10aSignal Processing, Computer-Assisted10aUser-Computer Interface1 aGrosse-Wentrup, Moritz1 aSchölkopf, B1 aHill, Jeremy, Jeremy uhttp://www.ncbi.nlm.nih.gov/pubmed/2045162602734nas a2200349 4500008004100000022001400041245009000055210006900145260001200214300001100226490000600237520175300243653001001996653002902006653003702035653002802072653001102100653001102111653001602122653000902138653001302147653001302160653001002173653002802183100002302211700001402234700002502248700001802273700001802291700002702309856004802336 2011 eng d a1741-255200aClosing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery.0 aClosing the sensorimotor loop haptic feedback facilitates decodi c06/2011 a0360050 v83 aThe combination of brain-computer interfaces (BCIs) with robot-assisted physical therapy constitutes a promising approach to neurorehabilitation of patients with severe hemiparetic syndromes caused by cerebrovascular brain damage (e.g. stroke) and other neurological conditions. In such a scenario, a key aspect is how to reestablish the disrupted sensorimotor feedback loop. However, to date it is an open question how artificially closing the sensorimotor feedback loop influences the decoding performance of a BCI. In this paper, we answer this issue by studying six healthy subjects and two stroke patients. We present empirical evidence that haptic feedback, provided by a seven degrees of freedom robotic arm, facilitates online decoding of arm movement intention. The results support the feasibility of future rehabilitative treatments based on the combination of robot-assisted physical therapy with BCIs.
10aBrain10aEvoked Potentials, Motor10aEvoked Potentials, Somatosensory10aFeedback, Physiological10aFemale10aHumans10aImagination10aMale10aMovement10aRobotics10aTouch10aUser-Computer Interface1 aGomez-Rodriguez, M1 aPeters, J1 aHill, Jeremy, Jeremy1 aSchölkopf, B1 aGharabaghi, A1 aGrosse-Wentrup, Moritz uhttp://www.ncbi.nlm.nih.gov/pubmed/2147487803153nas a2200481 4500008004100000022001400041245008200055210006900137260001200206300001000218490000700228520180600235653001002041653002802051653002002079653003602099653002402135653002702159653002402186653001102210653001102221653001602232653000902248653001602257653001902273653001702292653004102309653001302350653002502363653001702388653002802405653001202433100002002445700001802465700001302483700002502496700002402521700001802545700001802563700002102581700002102602856004802623 2008 eng d a1525-506900aVoluntary brain regulation and communication with electrocorticogram signals.0 aVoluntary brain regulation and communication with electrocortico c08/2008 a300-60 v133 aBrain-computer interfaces (BCIs) can be used for communication in writing without muscular activity or for learning to control seizures by voluntary regulation of brain signals such as the electroencephalogram (EEG). Three of five patients with epilepsy were able to spell their names with electrocorticogram (ECoG) signals derived from motor-related areas within only one or two training sessions. Imagery of finger or tongue movements was classified with support-vector classification of autoregressive coefficients derived from the ECoG signals. After training of the classifier, binary classification responses were used to select letters from a computer-generated menu. Offline analysis showed increased theta activity in the unsuccessful patients, whereas the successful patients exhibited dominant sensorimotor rhythms that they could control. The high spatial resolution and increased signal-to-noise ratio in ECoG signals, combined with short training periods, may offer an alternative for communication in complete paralysis, locked-in syndrome, and motor restoration.
10aAdult10aBiofeedback, Psychology10aCerebral Cortex10aCommunication Aids for Disabled10aDominance, Cerebral10aElectroencephalography10aEpilepsies, Partial10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aMotor Activity10aMotor Cortex10aSignal Processing, Computer-Assisted10aSoftware10aSomatosensory Cortex10aTheta Rhythm10aUser-Computer Interface10aWriting1 aHinterberger, T1 aWidman, Guido1 aLal, T N1 aHill, Jeremy, Jeremy1 aTangermann, Michael1 aRosenstiel, W1 aSchölkopf, B1 aElger, Christian1 aBirbaumer, Niels uhttp://www.ncbi.nlm.nih.gov/pubmed/1849554102785nas a2200445 4500008004100000022001400041245015300055210006900208260001200277300001000289490000700299520147600306653001501782653002801797653002101825653002701846653002701873653002201900653001101922653001101933653001601944653000901960653001601969653001401985653003501999653002802034100002502062700001302087700002302100700002002123700002102143700001502164700001902179700001802198700002102216700001802237700001502255700002102270856004802291 2006 eng d a1534-432000aClassifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.0 aClassifying EEG and ECoG signals without subject training for fa c06/2006 a183-60 v143 aWe summarize results from a series of related studies that aim to develop a motor-imagery-based brain-computer interface using a single recording session of electroencephalogram (EEG) or electrocorticogram (ECoG) signals for each subject. We apply the same experimental and analytical methods to 11 nonparalysed subjects (eight EEG, three ECoG), and to five paralyzed subjects (four EEG, one ECoG) who had been unable to communicate for some time. While it was relatively easy to obtain classifiable signals quickly from most of the nonparalyzed subjects, it proved impossible to classify the signals obtained from the paralyzed patients by the same methods. This highlights the fact that though certain BCI paradigms may work well with healthy subjects, this does not necessarily indicate success with the target user group. We outline possible reasons for this failure to transfer.
10aAlgorithms10aArtificial Intelligence10aCluster Analysis10aComputer User Training10aElectroencephalography10aEvoked Potentials10aFemale10aHumans10aImagination10aMale10aMiddle Aged10aParalysis10aPattern Recognition, Automated10aUser-Computer Interface1 aHill, Jeremy, Jeremy1 aLal, T N1 aSchröder, Michael1 aHinterberger, T1 aWilhelm, Barbara1 aNijboer, F1 aMochty, Ursula1 aWidman, Guido1 aElger, Christian1 aSchölkopf, B1 aKübler, A1 aBirbaumer, Niels uhttp://www.ncbi.nlm.nih.gov/pubmed/16792289