@article {4460, title = {Effects of active and sham tDCS on the soleus H-reflex during standing.}, journal = {Exp Brain Res}, volume = {241}, year = {2023}, month = {06/2023}, pages = {1611-1622}, abstract = {

Weak transcranial direct current stimulation (tDCS) is known to affect corticospinal excitability and enhance motor skill acquisition, whereas its effects on spinal reflexes in actively contracting muscles are yet to be established. Thus, in this study, we examined the acute effects of Active and Sham tDCS on the soleus H-reflex during standing. In fourteen adults without known neurological conditions, the soleus H-reflex was repeatedly elicited at just above M-wave threshold throughout 30~min of Active (N = 7) or Sham (N = 7) 2-mA tDCS over the primary motor cortex in standing. The maximum H-reflex (H) and M-wave (M) were also measured before and immediately after 30~min of tDCS. The soleus H-reflex amplitudes became significantly larger (by 6\%) ≈1~min into Active or Sham tDCS and gradually returned toward the pre-tDCS values, on average, within 15~min. With Active tDCS, the amplitude reduction from the initial increase appeared to occur more swiftly than with Sham tDCS. An acute temporary increase in the soleus H-reflex amplitude within the first minute of Active and Sham tDCS found in this study indicates a previously unreported effect of tDCS on the H-reflex excitability. The present study suggests that neurophysiological characterization of Sham tDCS effects is just as important as investigating Active tDCS effects in understanding and defining acute effects of tDCS on the excitability of spinal reflex pathways.

}, keywords = {Adult, Evoked Potentials, Motor, H-Reflex, Humans, Muscle, Skeletal, Standing Position, Transcranial Direct Current Stimulation}, issn = {1432-1106}, doi = {10.1007/s00221-023-06624-7}, author = {McCane, Lynn M and Wolpaw, Jonathan R and Thompson, Aiko K} } @article {4451, title = {The human motor cortex contributes to gravity compensation to maintain posture and during reaching.}, journal = {J Neurophysiol}, volume = {129}, year = {2023}, month = {01/2023}, pages = {83-101}, abstract = {

The neural control of posture and movement is interdependent. During voluntary movement, the neural motor command is executed by the motor cortex through the corticospinal tract and its collaterals and subcortical targets. Here we address the question of whether the control mechanism for the postural adjustments at nonmoving joints is also involved in overcoming gravity at the moving joints. We used single-pulse transcranial magnetic stimulation to measure the corticospinal excitability in humans during postural and reaching tasks. We hypothesized that the corticospinal excitability is proportional to background muscle activity and the gravity-related joint moments during both static postures and reaching movements. To test this hypothesis, we used visual targets in virtual reality to instruct five postures and three movements with or against gravity. We then measured the amplitude and gain of motor evoked potentials in multiple arm and hand muscles at several phases of the reaching motion and during static postures. The stimulation caused motor evoked potentials in all muscles that were proportional to the muscle activity. During both static postures and reaching movements, the muscle activity and the corticospinal contribution to these muscles changed in proportion with the postural moments needed to support the arm against gravity, supporting the hypothesis. Notably, these changes happened not only in antigravity muscles. Altogether, these results provide evidence that the changes in corticospinal excitability cause muscle cocontraction that modulates limb stiffness. This suggests that the motor cortex is involved in producing postural adjustments that support the arm against gravity during posture maintenance and reaching. Animal studies suggest that the corticospinal tract and its collaterals are crucial for producing postural adjustments that accompany movement in limbs other than the moving limb. Here we provide evidence for a similar control schema for both arm posture maintenance and gravity compensation during movement of the same limb. The observed interplay between the postural and movement control signals within the corticospinal tract may help explain the underlying neural motor deficits after stroke.

}, keywords = {Electromyography, Evoked Potentials, Motor, Humans, Motor Cortex, Movement, Muscle, Skeletal, Posture, Pyramidal Tracts, Transcranial Magnetic Stimulation}, issn = {1522-1598}, doi = {10.1152/jn.00367.2021}, author = {Hardesty, Russell L and Ellaway, Peter H and Gritsenko, Valeriya} } @article {3395, title = {Adaptive spatio-temporal filtering for movement related potentials in EEG-based brain-computer interfaces.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {22}, year = {2014}, month = {07/2014}, pages = {847-57}, abstract = {Movement related potentials (MRPs) are used as features in many brain-computer interfaces (BCIs) based on electroencephalogram (EEG). MRP feature extraction is challenging since EEG is noisy and varies between subjects. Previous studies used spatial and spatio-temporal filtering methods to deal with these problems. However, they did not optimize temporal information or may have been susceptible to overfitting when training data are limited and the feature space is of high dimension. Furthermore, most of these studies manually select data windows and low-pass frequencies. We propose an adaptive spatio-temporal (AST) filtering method to model MRPs more accurately in lower dimensional space. AST automatically optimizes all parameters by employing a Gaussian kernel to construct a low-pass time-frequency filter and a linear ridge regression (LRR) algorithm to compute a spatial filter. Optimal parameters are simultaneously sought by minimizing leave-one-out cross-validation error through gradient descent. Using four BCI datasets from 12 individuals, we compare the performances of AST filter to two popular methods: the discriminant spatial pattern filter and regularized spatio-temporal filter. The results demonstrate that our AST filter can make more accurate predictions and is computationally feasible.}, keywords = {Algorithms, Artificial Intelligence, brain-computer interfaces, Data Interpretation, Statistical, Electroencephalography, Evoked Potentials, Motor, Humans, Imagination, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Signal Processing, Computer-Assisted, Spatio-Temporal Analysis}, issn = {1558-0210}, doi = {10.1109/TNSRE.2014.2315717}, url = {http://www.ncbi.nlm.nih.gov/pubmed/24723632}, author = {Lu, Jun and Xie, Kan and Dennis J. McFarland} } @article {2132, title = {Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery.}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {06/2011}, pages = {036005}, abstract = {

The 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.

}, keywords = {Brain, Evoked Potentials, Motor, Evoked Potentials, Somatosensory, Feedback, Physiological, Female, Humans, Imagination, Male, Movement, Robotics, Touch, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/3/036005}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21474878}, author = {Gomez-Rodriguez, M and Peters, J and Jeremy Jeremy Hill and Sch{\"o}lkopf, B and Gharabaghi, A and Grosse-Wentrup, Moritz} } @article {2107, title = {Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {2008}, year = {2008}, month = {08/2008}, pages = {5224-7}, abstract = {

Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within the\ data\ through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.

}, keywords = {Algorithms, Artifacts, Diagnosis, Computer-Assisted, Electroencephalography, Epilepsy, Evoked Potentials, Motor, Motor Cortex, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {1557-170X}, doi = {10.1109/IEMBS.2008.4650392}, url = {http://www.ncbi.nlm.nih.gov/pubmed/19163895}, author = {Gunduz, Aysegul and Sanchez, Justin C and Principe, Jose} } @article {2185, title = {Non-invasive brain-computer interface system: towards its application as assistive technology.}, journal = {Brain Res Bull}, volume = {75}, year = {2008}, month = {04/2008}, pages = {796-803}, abstract = {

The quality of life of people suffering from severe motor disabilities can benefit from the use of current assistive technology capable of ameliorating communication, house-environment management and mobility, according to the user{\textquoteright}s residual motor abilities.\ Brain-computer interfaces\ (BCIs) are systems that can translate\ brain\ activity into signals that control external devices. Thus they can represent the only technology for severely paralyzed patients to increase or maintain their communication and control options. Here we report on a pilot study in which a system was implemented and validated to allow disabled persons to improve or recover their mobility (directly or by emulation) and communication within the surrounding environment. The system is based on a software controller that offers to the user a communication interface that is matched with the individual{\textquoteright}s residual motor abilities. Patients (n=14) with severe motor disabilities due to progressive neurodegenerative disorders were trained to use the system prototype under a rehabilitation program carried out in a house-like furnished space. All users utilized regular assistive control options (e.g., microswitches or head trackers). In addition, four subjects learned to operate the system by means of a non-invasive EEG-based BCI. This system was controlled by the subjects{\textquoteright} voluntary modulations of EEG sensorimotor rhythms recorded on the scalp; this skill was learnt even though the subjects have not had control over their limbs for a long time. We conclude that such a prototype system, which integrates several different assistive technologies including a BCI system, can potentially facilitate the translation from pre-clinical\ demonstrations to a\ clinical\ useful BCI.

}, keywords = {Activities of Daily Living, Adolescent, Adult, Brain, Child, Electroencephalography, Evoked Potentials, Motor, Female, Humans, Learning, Male, Middle Aged, Motor Skills, Muscular Dystrophy, Duchenne, Pilot Projects, Prostheses and Implants, Robotics, Self-Help Devices, Software, Spinal Muscular Atrophies of Childhood, User-Computer Interface, Volition}, issn = {0361-9230}, doi = {10.1016/j.brainresbull.2008.01.007}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18394526}, author = {Cincotti, F and Mattia, Donatella and Aloise, Fabio and Bufalari, Simona and Gerwin Schalk and Oriolo, Giuseppe and Cherubini, Andrea and Marciani, Maria Grazia and Babiloni, Fabio} } @proceedings {2240, title = {Three cases of feature correlation in an electrocorticographic BCI.}, year = {2008}, month = {2008}, pages = {5318-21}, abstract = {Three human subjects participated in a closed-loop brain computer interface cursor control experiment mediated by implanted subdural electrocorticographic arrays. The paradigm consisted of several stages: baseline recording, hand and tongue motor tasks as the basis for feature selection, two closed-loop one-dimensional feedback experiments with each of these features, and a two-dimensional feedback experiment using both of the features simultaneously. The two selected features were simple channel and frequency band combinations associated with change during hand and tongue movement. Inter-feature correlation and cross-correlation between features during different epochs of each task were quantified for each stage of the experiment. Our anecdotal, three subject, result suggests that while high correlation between horizontal and vertical control signal can initially preclude successful two-dimensional cursor control, a feedback-based learning strategy can be successfully employed by the subject to overcome this limitation and progressively decorrelate these control signals.}, keywords = {Adolescent, Adult, Algorithms, Electrocardiography, Evoked Potentials, Motor, Female, Humans, Male, Middle Aged, Motor Cortex, Pattern Recognition, Automated, Statistics as Topic, Task Performance and Analysis, User-Computer Interface}, issn = {1557-170X}, doi = {10.1109/IEMBS.2008.4650415}, author = {Miller, John W and Blakely, Timothy and Gerwin Schalk and den Nijs, Marcel and Rao, Rajesh P N and Ojemann, J G} } @article {2182, title = {Decoding two-dimensional movement trajectories using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {4}, year = {2007}, month = {09/2007}, pages = {264-75}, abstract = {

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.

}, keywords = {Adult, Algorithms, Arm, Brain Mapping, Cerebral Cortex, Electroencephalography, Evoked Potentials, Motor, Female, Humans, Male, Movement}, issn = {1741-2560}, doi = {10.1088/1741-2560/4/3/012}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17873429}, author = {Gerwin Schalk and Kub{\'a}nek, J and Miller, John W and Nicholas R Anderson and Leuthardt, E C and Ojemann, J G and Limbrick, D and Moran, D and Lester A Gerhardt and Jonathan Wolpaw} } @article {2106, title = {Analysis of the correlation between local field potentials and neuronal firing rate in the motor cortex.}, journal = {Conf Proc IEEE Eng Med Biol Soc}, volume = {1}, year = {2006}, month = {09/2006}, pages = {6185-8}, abstract = {

Neuronal firing rate has been the signal of choice for invasive motor\ brain\ machine\ interfaces\ (BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals.

}, keywords = {Action Potentials, Animals, Brain, Brain Mapping, Electric Stimulation, Electrodes, Evoked Potentials, Motor, Male, Models, Statistical, Motor Cortex, Neurons, Rats, Rats, Sprague-Dawley, Signal Processing, Computer-Assisted, Synaptic Transmission}, issn = {1557-170X}, doi = {10.1109/IEMBS.2006.260516}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17946745}, author = {Wang, Yiwen and Sanchez, Justin C and Principe, Jose and Mitzelfelt, Jeremiah D and Gunduz, Aysegul} } @article {2153, title = {Multi-channel linear descriptors for event-related EEG collected in brain computer interface.}, journal = {J Neural Eng}, volume = {3}, year = {2006}, month = {03/2006}, pages = {52-8}, abstract = {

By three multi-channel linear descriptors, i.e. spatial\ complexity\ (omega), field power (sigma) and frequency of field changes (phi),\ event-relatedEEGdata\ within 8-30 Hz were investigated during imagination of left or right hand movement. Studies on the\ event-relatedEEGdata\ indicate that a two-channel version of omega, sigma and phi could reflect the antagonistic ERD/ERS patterns over contralateral and ipsilateral areas and also characterize different phases of the changing brain states in the\ event-related\ paradigm. Based on the selective two-channel linear descriptors, the left and right hand motor imagery tasks are classified to obtain satisfactory results, which testify the validity of the three linear descriptors omega, sigma and phi for characterizing\ event-relatedEEG. The preliminary results show that omega, sigma together with phi have good separability for left and right hand motor imagery tasks, which could be considered for classification of two classes of\ EEG\ patterns in the application of brain computer interfaces.

}, keywords = {Algorithms, Electroencephalography, Evoked Potentials, Motor, Humans, Imagination, Motor Cortex, Movement, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/3/1/006}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16510942}, author = {Pei, Xiao-Mei and Zheng, Shi Dong and Xu, Jin and Bin, Guang-yu and Zuoguan Wang} } @article {2169, title = {Patients with ALS can use sensorimotor rhythms to operate a brain-computer interface.}, journal = {Neurology}, volume = {64}, year = {2005}, month = {05/2005}, pages = {1775-7}, abstract = {

People with severe motor disabilities can maintain an acceptable quality of life if they can communicate.\ Brain-computer interfaces\ (BCIs), which do not depend on muscle control, can provide communication. Four people severely disabled by ALS learned to operate a BCI with EEG rhythms recorded over sensorimotor cortex. These results suggest that a sensorimotor rhythm-based\ BCI could help maintain quality of life for people with ALS.

}, keywords = {Aged, Amyotrophic Lateral Sclerosis, Electroencephalography, Evoked Potentials, Motor, Evoked Potentials, Somatosensory, Female, Humans, Imagination, Male, Middle Aged, Motor Cortex, Movement, Paralysis, Photic Stimulation, Prostheses and Implants, Somatosensory Cortex, Treatment Outcome, User-Computer Interface}, issn = {1526-632X}, doi = {10.1212/01.WNL.0000158616.43002.6D}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15911809}, author = {K{\"u}bler, A. and Nijboer, F and Mellinger, J{\"u}rgen and Theresa M Vaughan and Pawelzik, H and Gerwin Schalk and Dennis J. McFarland and Niels Birbaumer and Jonathan Wolpaw} }