@article {4360, title = {Neural oscillations during motor imagery of complex gait: an HdEEG study.}, journal = {Sci Rep}, volume = {12}, year = {2022}, month = {03/2022}, pages = {4314}, abstract = {

The aim of this study was to investigate differences between usual and complex gait motor imagery (MI) task in healthy subjects using high-density electroencephalography (hdEEG) with a MI protocol. We characterized the spatial distribution of α- and β-bands oscillations extracted from hdEEG signals recorded during MI of usual walking (UW) and walking by avoiding an obstacle (Dual-Task, DT). We applied a source localization algorithm to brain regions selected from a large cortical-subcortical network, and then we analyzed α and β bands Event-Related Desynchronizations (ERDs). Nineteen healthy subjects visually imagined walking on a path with (DT) and without (UW) obstacles. Results showed in both gait MI tasks, α- and β-band ERDs in a large cortical-subcortical network encompassing mostly frontal and parietal regions. In most of the regions, we found α- and β-band ERDs in the DT compared with the UW condition. Finally, in the β band, significant correlations emerged between ERDs and scores in imagery ability tests. Overall we detected MI gait-related α- and β-band oscillations in cortical and subcortical areas and significant differences between UW and DT MI conditions. A better understanding of gait neural correlates may lead to a better knowledge of pathophysiology of gait disturbances in neurological diseases.

}, keywords = {Brain, Electroencephalography, Gait, Humans, Imagery, Psychotherapy, Imagination, Walking}, issn = {2045-2322}, doi = {10.1038/s41598-022-07511-x}, author = {Putzolu, Martina and Samogin, Jessica and Cosentino, Carola and Mezzarobba, Susanna and Bonassi, Gaia and Lagravinese, Giovanna and Vato, Alessandro and Mantini, Dante and Avanzino, Laura and Pelosin, Elisa} } @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 {2134, title = {Causal influence of gamma oscillations on the sensorimotor rhythm.}, journal = {Neuroimage}, volume = {56}, year = {2011}, month = {05/2011}, pages = {837-42}, abstract = {

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{\textquoteright}s intention, our findings entail that gamma oscillations have a causal influence on a subject{\textquoteright}s capability to utilize a\ BCI\ for means of communication.

}, keywords = {Adult, Cerebral Cortex, Electroencephalography, Female, Humans, Imagination, Male, Signal Processing, Computer-Assisted, User-Computer Interface}, issn = {1095-9572}, doi = {10.1016/j.neuroimage.2010.04.265}, url = {http://www.ncbi.nlm.nih.gov/pubmed/20451626}, author = {Grosse-Wentrup, Moritz and Sch{\"o}lkopf, B and Jeremy Jeremy Hill} } @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 {2206, title = {Using the electrocorticographic speech network to control a brain-computer interface in humans.}, journal = {J Neural Eng}, volume = {8}, year = {2011}, month = {06/2011}, pages = {036004}, abstract = {

Electrocorticography (ECoG) has emerged as a new signal platform for\ brain-computer interface\ (BCI) systems. Classically, the cortical physiology that has been commonly investigated and utilized for device control in humans has been\ brain\ signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We demonstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional\ computer\ cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency oscillations and enabled users to achieve final target accuracies between 68\% and 91\% within 15 min. Additionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could provide an additional cognitive and physiologic substrate for\ BCI\ operation and that these signals can be acquired from a cortical array that is small and minimally invasive.

}, keywords = {Adult, Brain, Brain Mapping, Computer Peripherals, Electroencephalography, Evoked Potentials, Feedback, Physiological, Female, Humans, Imagination, Male, Middle Aged, Nerve Net, Speech Production Measurement, User-Computer Interface}, issn = {1741-2552}, doi = {10.1088/1741-2560/8/3/036004}, url = {http://www.ncbi.nlm.nih.gov/pubmed/21471638}, author = {Leuthardt, E C and Charles M Gaona and Sharma, Mohit and Szrama, Nicholas and Roland, Jarod and Zachary V. Freudenberg and Solisb, Jamie and Breshears, Jonathan and Gerwin Schalk} } @article {2137, title = {Voluntary brain regulation and communication with electrocorticogram signals.}, journal = {Epilepsy Behav}, volume = {13}, year = {2008}, month = {08/2008}, pages = {300-6}, abstract = {

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.

}, keywords = {Adult, Biofeedback, Psychology, Cerebral Cortex, Communication Aids for Disabled, Dominance, Cerebral, Electroencephalography, Epilepsies, Partial, Female, Humans, Imagination, Male, Middle Aged, Motor Activity, Motor Cortex, Signal Processing, Computer-Assisted, Software, Somatosensory Cortex, Theta Rhythm, User-Computer Interface, Writing}, issn = {1525-5069}, doi = {10.1016/j.yebeh.2008.03.014}, url = {http://www.ncbi.nlm.nih.gov/pubmed/18495541}, author = {Hinterberger, T. and Widman, Guido and Lal, T.N and Jeremy Jeremy Hill and Tangermann, Michael and Rosenstiel, W. and Sch{\"o}lkopf, B and Elger, Christian and Niels Birbaumer} } @article {2174, title = {A {\textmu}-rhythm Matched Filter for Continuous Control of a Brain-Computer Interface.}, journal = {IEEE Trans Biomed Eng}, volume = {54}, year = {2007}, month = {02/2007}, pages = {273-80}, abstract = {

A brain-computer interface (BCI) is a system that provides an alternate nonmuscular communication/control channel for individuals with severe neuromuscular disabilities. With proper training, individuals can learn to modulate the amplitude of specific electroencephalographic (EEG) components (e.g., the 8-12 Hz mu rhythm and 18-26 Hz beta rhythm) over the sensorimotor cortex and use them to control a cursor on a computer screen. Conventional spectral techniques for monitoring the\ continuousamplitude fluctuations fail to capture essential amplitude/phase relationships of the mu and beta rhythms in a compact fashion and, therefore, are suboptimal. By extracting the characteristic mu rhythm for a user, the exact morphology can be characterized and exploited as a matched filter. A simple, parameterized model for the characteristic mu rhythm is proposed and its effectiveness as a matched filter is examined online for a one-dimensional cursor control task. The results suggest that amplitude/phase coupling exists between the mu and beta bands during event-related desynchronization, and that an appropriate matched filter can provide improved performance.

}, keywords = {Algorithms, Cerebral Cortex, Cortical Synchronization, Electroencephalography, Evoked Potentials, Humans, Imagination, Pattern Recognition, Automated, User-Computer Interface}, issn = {0018-9294}, doi = {10.1109/TBME.2006.886661}, url = {http://www.ncbi.nlm.nih.gov/pubmed/17278584}, author = {Krusienski, Dean J and Gerwin Schalk and Dennis J. McFarland and Jonathan Wolpaw} } @article {2143, title = {Classifying EEG and ECoG signals without subject training for fast BCI implementation: comparison of nonparalyzed and completely paralyzed subjects.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {183-6}, abstract = {

We summarize results from a series of related studies that aim to develop a motor-imagery-basedbrain-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.

}, keywords = {Algorithms, Artificial Intelligence, Cluster Analysis, Computer User Training, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Middle Aged, Paralysis, Pattern Recognition, Automated, User-Computer Interface}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875548}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792289}, author = {Jeremy Jeremy Hill and Lal, T.N and Schr{\"o}der, Michael and Hinterberger, T. and Wilhelm, Barbara and Nijboer, F and Mochty, Ursula and Widman, Guido and Elger, Christian and Sch{\"o}lkopf, B and K{\"u}bler, A. and Niels Birbaumer} } @article {2175, title = {ECoG factors underlying multimodal control of a brain-computer interface.}, journal = {IEEE Trans Neural Syst Rehabil Eng}, volume = {14}, year = {2006}, month = {06/2006}, pages = {246-50}, abstract = {

Most current brain-computer interface (BCI) systems for humans use electroencephalographic activity recorded from the scalp, and may be limited in many ways. Electrocorticography (ECoG) is believed to be a minimally-invasive alternative to electroencephalogram (EEG) for BCI systems, yielding superior signal characteristics that could allow rapid user training and faster communication rates. In addition, our preliminary results suggest that brain regions other than the sensorimotor cortex, such as auditory cortex, may be trained to control a BCI system using similar methods as those used to train motor regions of the brain. This could prove to be vital for users who have neurological disease, head trauma, or other conditions precluding the use of sensorimotor cortex for BCI control.

}, keywords = {Adult, Brain Mapping, Cerebral Cortex, Communication Aids for Disabled, Computer Peripherals, Evoked Potentials, Female, Humans, Imagination, Male, Man-Machine Systems, Neuromuscular Diseases, Systems Integration, User-Computer Interface, Volition}, issn = {1534-4320}, doi = {10.1109/TNSRE.2006.875570}, url = {http://www.ncbi.nlm.nih.gov/pubmed/16792305}, author = {Adam J Wilson and Felton, Elizabeth A and Garell, P Charles and Gerwin Schalk and Williams, Justin C} } @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} } @article {2168, title = {A brain-computer interface using electrocorticographic signals in humans.}, journal = {J Neural Eng}, volume = {1}, year = {2004}, month = {06/2004}, pages = {63-71}, abstract = {

Brain-computer interfaces\ (BCIs) enable users to control devices with electroencephalographic (EEG) activity from the scalp or with single-neuron activity from within the\ brain. Both methods have disadvantages: EEG has limited resolution and requires extensive training, while single-neuron recording entails significant clinical risks and has limited stability. We demonstrate here for the first time that electrocorticographic (ECoG) activity recorded from the surface of the\ brain\ can enable users to control a one-dimensional\ computer\ cursor rapidly and accurately. We first identified ECoG signals that were associated with different types of motor and speech imagery. Over brief training periods of 3-24 min, four patients then used these signals to master closed-loop control and to achieve success rates of 74-100\% in a one-dimensional binary task. In additional open-loop experiments, we found that ECoG signals at frequencies up to 180 Hz encoded substantial information about the direction of two-dimensional joystick movements. Our results suggest that an ECoG-based BCI could provide for people with severe motor disabilities a non-muscular communication and control option that is more powerful than EEG-based BCIs and is potentially more stable and less traumatic than BCIs that use electrodes penetrating the\ brain.

}, keywords = {Adult, Brain, Communication Aids for Disabled, Computer Peripherals, Diagnosis, Computer-Assisted, Electrodes, Implanted, Electroencephalography, Evoked Potentials, Female, Humans, Imagination, Male, Movement Disorders, User-Computer Interface}, issn = {1741-2560}, doi = {10.1088/1741-2560/1/2/001}, url = {http://www.ncbi.nlm.nih.gov/pubmed/15876624}, author = {Leuthardt, E C and Gerwin Schalk and Jonathan Wolpaw and Ojemann, J G and Moran, D} }