03372nas a2200409 4500008004100000022001400041245011600055210006900171260001300240300001000253490000700263520216200270653001002432653000902442653003002451653002002481653002702501653001102528653001702539653001102556653000902567653001602576653001902592653001202611653002402623653002502647653001102672653002602683653001702709653002002726100001902746700002002765700002002785700002202805700002302827856011202850 2025 eng d a1552-684400aIpsiHand Brain-Computer Interface Therapy Induces Broad Upper Extremity Motor Rehabilitation in Chronic Stroke.0 aIpsiHand BrainComputer Interface Therapy Induces Broad Upper Ext c2025 Jan a74-860 v393 a

BACKGROUND: Chronic hemiparetic stroke patients have very limited benefits from current therapies. Brain-computer interface (BCI) engaging the unaffected hemisphere has emerged as a promising novel therapeutic approach for chronic stroke rehabilitation.

OBJECTIVES: This study investigated the effectiveness of contralesionally-controlled BCI therapy in chronic stroke patients with impaired upper extremity motor function. We further explored neurophysiological features of motor recovery driven by BCI. We hypothesized that BCI therapy would induce a broad motor recovery in the upper extremity, and there would be corresponding changes in baseline theta and gamma oscillations, which have been shown to be associated with motor recovery.

METHODS: Twenty-six prospectively enrolled chronic hemiparetic stroke patients performed a therapeutic BCI task for 12 weeks. Motor function assessment data and resting state electroencephalogram signals were acquired before initiating BCI therapy and across BCI therapy sessions. The Upper Extremity Fugl-Meyer assessment served as a primary motor outcome assessment tool. Theta-gamma cross-frequency coupling (CFC) was computed and correlated with motor recovery.

RESULTS: Chronic stroke patients achieved significant motor improvement in both proximal and distal upper extremity with BCI therapy. Motor function improvement was independent of Botox application. Theta-gamma CFC enhanced bilaterally over the C3/C4 motor electrodes and positively correlated with motor recovery across BCI therapy sessions.

CONCLUSIONS: BCI therapy resulted in significant motor function improvement across the proximal and distal upper extremities of patients, which significantly correlated with theta-gamma CFC increases in the motor regions. This may represent rhythm-specific cortical oscillatory mechanism for BCI-driven rehabilitation in chronic stroke patients.

TRIAL REGISTRATION: Advarra Study: https://classic.clinicaltrials.gov/ct2/show/NCT04338971 and Washington University Study: https://classic.clinicaltrials.gov/ct2/show/NCT03611855.

10aAdult10aAged10abrain-computer interfaces10aChronic Disease10aElectroencephalography10aFemale10aGamma Rhythm10aHumans10aMale10aMiddle Aged10aMotor Activity10aParesis10aProspective Studies10aRecovery of Function10aStroke10aStroke Rehabilitation10aTheta Rhythm10aUpper Extremity1 aRustamov, Nabi1 aSouders, Lauren1 aSheehan, Lauren1 aCarter, Alexandre1 aLeuthardt, Eric, C uhttps://neurotechcenter.org/publications/2025/ipsihand-brain-computer-interface-therapy-induces-broad-upper07936nas a2200361 4500008004100000022001400041245009600055210006900151260001200220300001100232490000800243520694900251653001507200653001007215653002807225653002007253653001007273653002507283653002407308653001107332653001107343653000907354653001607363653001907379653001607398100001607414700001907430700002207449700001707471700001707488700002107505856004807526 2010 eng d a1091-649000aCortical activity during motor execution, motor imagery, and imagery-based online feedback.0 aCortical activity during motor execution motor imagery and image c03/2010 a4430-50 v1073 a

Imagery of motor movement plays an important role in learning of complex motor skills, from learning to serve in tennis to perfecting a pirouette in ballet. What and where are the neural substrates that underlie motor imagery-based learning? We measured electrocorticographic cortical surface potentials in eight human subjects during overt action and kinesthetic imagery of the same movement, focusing on power in "high frequency" (76-100 Hz) and "low frequency" (8-32 Hz) ranges. We quantitatively establish that the spatial distribution of local neuronal population activity during motor imagery mimics the spatial distribution of activity during actual motor movement. By comparing responses to electrocortical stimulation with imagery-induced cortical surface activity, we demonstrate the role of primary motor areas in movement imagery. The magnitude of imagery-induced cortical activity change was approximately 25% of that associated with actual movement. However, when subjects learned to use this imagery to control a computer cursor in a simple feedback task, the imagery-induced activity change was significantly augmented, even exceeding that of overt movement.

10aAdolescent10aAdult10aBiofeedback, Psychology10aCerebral Cortex10aChild10aElectric Stimulation10aElectrocardiography10aFemale10aHumans10aMale10aMiddle Aged10aMotor Activity10aYoung Adult1 aMiller, K J1 aSchalk, Gerwin1 aFetz, Eberhard, E1 aNijs, Marcel1 aOjemann, J G1 aRao, Rajesh, P N uhttp://www.ncbi.nlm.nih.gov/pubmed/2016008402146nas 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

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.

10aAdolescent10aAdult10aBiomechanics10aBrain10aElectrodiagnosis10aEpilepsy10aFemale10aFingers10aHumans10aMale10aMicroelectrodes10aMiddle Aged10aMotor Activity10aRest10aThumb10aTime Factors10aYoung Adult1 aKubánek, J1 aMiller, John, W1 aOjemann, J G1 aWolpaw, Jonathan1 aSchalk, Gerwin uhttp://www.ncbi.nlm.nih.gov/pubmed/1979423702457nas a2200349 4500008004100000022001400041245012600055210006900181260001200250300001200262490000700274520141700281653001501698653001001713653001801723653002601741653002101767653001301788653002401801653000901825653001101834653001801845653001901863653003101882653002301913653004101936100002001977700002301997700002002020700001902040856004802059 2009 eng d a1879-278200aMapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.0 aMapping broadband electrocorticographic recordings to twodimensi c11/2009 a1257-700 v223 a

Brain-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (<200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.

10aAlgorithms10aBrain10aBrain Mapping10aElectrodes, Implanted10aElectrodiagnosis10aEpilepsy10aFeasibility Studies10aHand10aHumans10aLinear Models10aMotor Activity10aNeural Networks (Computer)10aNonlinear Dynamics10aSignal Processing, Computer-Assisted1 aGunduz, Aysegul1 aSanchez, Justin, C1 aCarney, Paul, R1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/1964798103153nas 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 a

Brain-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/18495541