TY - JOUR T1 - Cortical activity during motor execution, motor imagery, and imagery-based online feedback. JF - Proc Natl Acad Sci U S A Y1 - 2010 A1 - Miller, K.J. A1 - Gerwin Schalk A1 - Fetz, Eberhard E A1 - den Nijs, Marcel A1 - Ojemann, J G A1 - Rao, Rajesh P N KW - Adolescent KW - Adult KW - Biofeedback, Psychology KW - Cerebral Cortex KW - Child KW - Electric Stimulation KW - Electrocardiography KW - Female KW - Humans KW - Male KW - Middle Aged KW - Motor Activity KW - Young Adult AB -

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.

VL - 107 UR - http://www.ncbi.nlm.nih.gov/pubmed/20160084 IS - 9 ER - TY - JOUR T1 - Decoding flexion of individual fingers using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2009 A1 - Kubánek, J A1 - Miller, John W A1 - Ojemann, J G A1 - Jonathan Wolpaw A1 - Gerwin Schalk KW - Adolescent KW - Adult KW - Biomechanics KW - Brain KW - Electrodiagnosis KW - Epilepsy KW - Female KW - Fingers KW - Humans KW - Male KW - Microelectrodes KW - Middle Aged KW - Motor Activity KW - Rest KW - Thumb KW - Time Factors KW - Young Adult AB -

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.

VL - 6 UR - http://www.ncbi.nlm.nih.gov/pubmed/19794237 IS - 6 ER - TY - JOUR T1 - Real-time detection of event-related brain activity. JF - Neuroimage Y1 - 2008 A1 - Gerwin Schalk A1 - Leuthardt, E C A1 - Peter Brunner A1 - Ojemann, J G A1 - Lester A Gerhardt A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Brain Mapping KW - Computer Systems KW - Diagnosis, Computer-Assisted KW - Electroencephalography KW - Epilepsy KW - Evoked Potentials KW - Female KW - Humans KW - Male KW - Pattern Recognition, Automated KW - Reproducibility of Results KW - Sensitivity and Specificity AB -

The complexity and inter-individual variation of brain signals impedes real-time detection of events in raw signals. To convert these complex signals into results that can be readily understood, current approaches usually apply statistical methods to data from known conditions after all data have been collected. The capability to provide meaningful visualization of complex brain signals without the requirement to initially collect data from all conditions would provide a new tool, essentially a new imaging technique, that would open up new avenues for the study of brain function. Here we show that a new analysis approach, called SIGFRIED, can overcome this serious limitation of current methods. SIGFRIED can visualize brain signal changes without requiring prior data collection from all conditions. This capacity is particularly well suited to applications in which comprehensive prior data collection is impossible or impractical, such as intraoperative localization of cortical function or detection of epileptic seizures.

VL - 43 UR - http://www.ncbi.nlm.nih.gov/pubmed/18718544 IS - 2 ER - TY - Generic T1 - Three cases of feature correlation in an electrocorticographic BCI. T2 - Conf Proc IEEE Eng Med Biol Soc Y1 - 2008 A1 - Miller, John W A1 - Blakely, Timothy A1 - Gerwin Schalk A1 - den Nijs, Marcel A1 - Rao, Rajesh P N A1 - Ojemann, J G KW - Adolescent KW - Adult KW - Algorithms KW - Electrocardiography KW - Evoked Potentials, Motor KW - Female KW - Humans KW - Male KW - Middle Aged KW - Motor Cortex KW - Pattern Recognition, Automated KW - Statistics as Topic KW - Task Performance and Analysis KW - User-Computer Interface AB - 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. JF - Conf Proc IEEE Eng Med Biol Soc ER - TY - JOUR T1 - Two-dimensional movement control using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2008 A1 - Gerwin Schalk A1 - Miller, K.J. A1 - Nicholas R Anderson A1 - Adam J Wilson A1 - Smyth, Matt A1 - Ojemann, J G A1 - Moran, D A1 - Jonathan Wolpaw A1 - Leuthardt, E C KW - Adolescent KW - Adult KW - Brain Mapping KW - Data Interpretation, Statistical KW - Drug Resistance KW - Electrocardiography KW - Electrodes, Implanted KW - Electroencephalography KW - Epilepsy KW - Female KW - Humans KW - Male KW - Movement KW - User-Computer Interface AB -

We show here that a brain-computer interface (BCI) using electrocorticographic activity (ECoG) and imagined or overt motor tasks enables humans to control a computer cursor in two dimensions. Over a brief training period of 12-36 min, each of five human subjects acquired substantial control of particular ECoG features recorded from several locations over the same hemisphere, and achieved average success rates of 53-73% in a two-dimensional four-target center-out task in which chance accuracy was 25%. Our results support the expectation that ECoG-based BCIs can combine high performance with technical and clinical practicality, and also indicate promising directions for further research.

VL - 5 UR - http://www.ncbi.nlm.nih.gov/pubmed/18310813 IS - 1 ER - TY - JOUR T1 - Decoding two-dimensional movement trajectories using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2007 A1 - Gerwin Schalk A1 - Kubánek, J A1 - Miller, John W A1 - Nicholas R Anderson A1 - Leuthardt, E C A1 - Ojemann, J G A1 - Limbrick, D A1 - Moran, D A1 - Lester A Gerhardt A1 - Jonathan Wolpaw KW - Adult KW - Algorithms KW - Arm KW - Brain Mapping KW - Cerebral Cortex KW - Electroencephalography KW - Evoked Potentials, Motor KW - Female KW - Humans KW - Male KW - Movement AB -

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.

VL - 4 UR - http://www.ncbi.nlm.nih.gov/pubmed/17873429 IS - 3 ER - TY - JOUR T1 - Electrocorticographic Frequency Alteration Mapping: A Clinical Technique for Mapping the Motor Cortex. JF - Neurosurgery Y1 - 2007 A1 - Leuthardt, E C A1 - Miller, John W A1 - Nicholas R Anderson A1 - Gerwin Schalk A1 - Dowling, Joshua A1 - Miller, John W A1 - Moran, D A1 - Ojemann, J G KW - Adult KW - Biological Clocks KW - Brain Mapping KW - Electric Stimulation KW - Electrodes, Implanted KW - Electroencephalography KW - Female KW - Hand KW - Humans KW - Male KW - Middle Aged KW - Motor Cortex KW - Oscillometry KW - Signal Processing, Computer-Assisted KW - Tongue AB -

OBJECTIVE: 

Electrocortical stimulation (ECS) has been well established for delineating the eloquent cortex. However, ECS is still coarse and inefficient in delineating regions of the functional cortex and can be hampered by after-discharges. Given these constraints, an adjunct approach to defining the motor cortex is the use of electrocorticographic signal changes associated with active regions of the cortex. The broad range of frequency oscillations are categorized into two main groups with respect to the sensorimotor cortex: low and high frequency bands. The low frequency bands tend to show a power reduction with cortical activation, whereas the high frequency bands show power increases. These power changes associated with the activated cortex could potentially provide a powerful tool in delineating areas of the motor cortex. We explore electrocorticographic signal alterations as they occur with activated regions of the motor cortex, as well as its potential in clinical brain mapping applications.

METHODS: 

We evaluated seven patients who underwent invasive monitoring for seizure localization. Each patient had extraoperative ECS mapping to identify the motor cortex. All patients also performed overt hand and tongue motor tasks to identify associated frequency power changes in regard to location and degree of concordance with ECS results that localized either hand or tongue motor function.

RESULTS: 

The low frequency bands had a high sensitivity (88.9-100%) and a lower specificity (79.0-82.6%) for identifying electrodes with either hand or tongue ECS motor responses. The high frequency bands had a lower sensitivity (72.7-88.9%) and a higher specificity (92.4-94.9%) in correlation with the same respective ECS positive electrodes.

CONCLUSION: 

The concordance between stimulation and spectral power changes demonstrate the possible utility of electrocorticographic frequency alteration mapping as an adjunct method to improve the efficiency and resolution of identifying the motor cortex.

VL - 60 UR - http://www.ncbi.nlm.nih.gov/pubmed/17415162 IS - 4 Suppl 2 ER - TY - JOUR T1 - Spectral Changes in Cortical Surface Potentials During Motor Movement. JF - J Neurosci Y1 - 2007 A1 - Miller, John W A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Rao, Rajesh P N A1 - Nicholas R Anderson A1 - Moran, D A1 - Miller, John W A1 - Ojemann, J G KW - Adult KW - Brain Mapping KW - Female KW - Humans KW - Male KW - Middle Aged KW - Motor Cortex KW - Movement AB -

In the first large study of its kind, we quantified changes in electrocorticographic signals associated with motor movement across 22 subjects with subdural electrode arrays placed for identification of seizure foci. Patients underwent a 5-7 d monitoring period with array placement, before seizure focus resection, and during this time they participated in the study. An interval-based motor-repetition task produced consistent and quantifiable spectral shifts that were mapped on a Talairach-standardized template cortex. Maps were created independently for a high-frequency band (HFB) (76-100 Hz) and a low-frequency band (LFB) (8-32 Hz) for several different movement modalities in each subject. The power in relevant electrodes consistently decreased in the LFB with movement, whereas the power in the HFB consistently increased. In addition, the HFB changes were more focal than the LFB changes. Sites of power changes corresponded to stereotactic locations in sensorimotor cortex and to the results of individual clinical electrical cortical mapping. Sensorimotor representation was found to be somatotopic, localized in stereotactic space to rolandic cortex, and typically followed the classic homunculus with limited extrarolandic representation.

VL - 27 UR - http://www.ncbi.nlm.nih.gov/pubmed/17329441 IS - 9 ER - TY - JOUR T1 - A brain-computer interface using electrocorticographic signals in humans. JF - J Neural Eng Y1 - 2004 A1 - Leuthardt, E C A1 - Gerwin Schalk A1 - Jonathan Wolpaw A1 - Ojemann, J G A1 - Moran, D KW - Adult KW - Brain KW - Communication Aids for Disabled KW - Computer Peripherals KW - Diagnosis, Computer-Assisted KW - Electrodes, Implanted KW - Electroencephalography KW - Evoked Potentials KW - Female KW - Humans KW - Imagination KW - Male KW - Movement Disorders KW - User-Computer Interface AB -

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.

VL - 1 UR - http://www.ncbi.nlm.nih.gov/pubmed/15876624 IS - 2 ER -