Detecting Awareness in the Vegetative State: Electroencephalographic Evidence for Attempted Movements to Command

Patients in the Vegetative State (VS) do not produce overt motor behavior to command and are therefore considered to be unaware of themselves and of their environments. However, we recently showed that high-density electroencephalography (EEG) can be used to detect covert command-following in some VS patients. Due to its portability and inexpensiveness, EEG assessments of awareness have the potential to contribute to a standard clinical protocol, thus improving diagnostic accuracy. However, this technique requires refinement and optimization if it is to be used widely as a clinical tool. We asked a patient who had been repeatedly diagnosed as VS for 12-years to try to move his left and right hands, between periods of rest, while EEG was recorded from four scalp electrodes. We identified appropriate and statistically reliable modulations of sensorimotor beta rhythms following commands to try to move, which could be significantly classified at a single-trial level. These reliable effects indicate that the patient attempted to follow the commands, and was therefore aware, but was unable to execute an overtly discernable action. The cognitive demands of this novel task are lower than those used previously and, crucially, allow for awareness to be determined on the basis of a 20-minute EEG recording made with only four electrodes. This approach makes EEG assessments of awareness clinically viable, and therefore has potential for inclusion in a standard assessment of awareness in the VS

Change in brain activity through virtual reality-based brain-machine communication in a chronic tetraplegic subject with muscular dystrophy

<p>Abstract</p> <p>Background</p> <p>For severely paralyzed people, a brain-computer interface (BCI) provides a way of re-establishing communication. Although subjects with muscular dystrophy (MD) appear to be potential BCI users, the actual long-term effects of BCI use on brain activities in MD subjects have yet to be clarified. To investigate these effects, we followed BCI use by a chronic tetraplegic subject with MD over 5 months. The topographic changes in an electroencephalogram (EEG) after long-term use of the virtual reality (VR)-based BCI were also assessed. Our originally developed BCI system was used to classify an EEG recorded over the sensorimotor cortex in real time and estimate the user's motor intention (MI) in 3 different limb movements: feet, left hand, and right hand. An avatar in the internet-based VR was controlled in accordance with the results of the EEG classification by the BCI. The subject was trained to control his avatar via the BCI by strolling in the VR for 1 hour a day and then continued the same training twice a month at his home.</p> <p>Results</p> <p>After the training, the error rate of the EEG classification decreased from 40% to 28%. The subject successfully walked around in the VR using only his MI and chatted with other users through a voice-chat function embedded in the internet-based VR. With this improvement in BCI control, event-related desynchronization (ERD) following MI was significantly enhanced (<it>p </it>< 0.01) for feet MI (from -29% to -55%), left-hand MI (from -23% to -42%), and right-hand MI (from -22% to -51%).</p> <p>Conclusions</p> <p>These results show that our subject with severe MD was able to learn to control his EEG signal and communicate with other users through use of VR navigation and suggest that an internet-based VR has the potential to provide paralyzed people with the opportunity for easy communication.</p

A Generalized Framework for Quantifying the Dynamics of EEG Event-Related Desynchronization

Brains were built by evolution to react swiftly to environmental challenges. Thus, sensory stimuli must be processed ad hoc, i.e., independent—to a large extent—from the momentary brain state incidentally prevailing during stimulus occurrence. Accordingly, computational neuroscience strives to model the robust processing of stimuli in the presence of dynamical cortical states. A pivotal feature of ongoing brain activity is the regional predominance of EEG eigenrhythms, such as the occipital alpha or the pericentral mu rhythm, both peaking spectrally at 10 Hz. Here, we establish a novel generalized concept to measure event-related desynchronization (ERD), which allows one to model neural oscillatory dynamics also in the presence of dynamical cortical states. Specifically, we demonstrate that a somatosensory stimulus causes a stereotypic sequence of first an ERD and then an ensuing amplitude overshoot (event-related synchronization), which at a dynamical cortical state becomes evident only if the natural relaxation dynamics of unperturbed EEG rhythms is utilized as reference dynamics. Moreover, this computational approach also encompasses the more general notion of a “conditional ERD,” through which candidate explanatory variables can be scrutinized with regard to their possible impact on a particular oscillatory dynamics under study. Thus, the generalized ERD represents a powerful novel analysis tool for extending our understanding of inter-trial variability of evoked responses and therefore the robust processing of environmental stimuli

Discrimination of Motor Imagery-Induced EEG Patterns in Patients with Complete Spinal Cord Injury

EEG-based discrimination between different motor imagery states has been subject of a number of studies in healthy subjects. We investigated the EEG of 15 patients with complete spinal cord injury during imagined right hand, left hand, and feet movements. In detail we studied pair-wise discrimination functions between the 3 types of motor imagery. The following classification accuracies (mean ± SD) were obtained: left versus right hand 65.03% ± 8.52, left hand versus feet 68.19% ± 11.08, and right hand versus feet 65.05% ± 9.25. In 5 out of 8 paralegic patients, the discrimination accuracy was greater than 70% but in only 1 out of 7 tetraplagic patients. The present findings provide evidence that in the majority of paraplegic patients an EEG-based BCI could achieve satisfied results. In tetraplegic patients, however, it is expected that extensive training-sessions are necessary to achieve a good BCI performance at least in some subjects

Time/space regularization of the inward continuation problem in EEG using the Boundary Element Method

The inward continuation problem in EEG consists in the computation of the cortical potential distribution from the measured potential distribution on the scalp. Although unique this inverse problem is ill-posed. That is, low-level noise in the scalp potential data or a small error in the geometrical data can lead to unbounded errors in the solution. Regularization techniques have to be used to minimize these effects. The inverse problem is solved in two steps. First Tikhonov regularization is applied yielding a solution of the potential on the inside of the skull surface for every timestep. Than the solution of the first step is used for Twomey regularization. At each moment in time a new solution is found by using as a priori estimate the average of the first solution one timestep prior and one timestep after. This combination of spatial (Tikhonov) and temporal (Twomey) regularization improves the solution and smoothes the solution in space and time. Both simulations and the application to EEG data of a Median Nerve stimulation experiment yield encouraging results. Further comparative studies have to be carried out to evaluate the application of time/space regularization of the inward continuation problem in EEG

Post-movement Beta synchronization studied with linear estimation

Event-related desynchronization (ERS) describes a short-lasting and localized amplitude enhancement of specific frequency components. The spatial distribution of a post-movement beta ERS can be visualized by computing the local average reference (LAR). The Linear estimation (LE) method can also be applied to study the spatiotemporal ERS patterns. As source space an hemisphere was used with equally distributed radially oriented current dipoles. The lead field matrix is normalized to make sure that all dipoles have the same average impact on the sensors. A distributed source solution is found for each timestep and for each trial. Event-related Desynchronization calculations are carried out for every dipole (squaring of amplitude, averaging over all trials and time averaging over 16 time points). Both methods were conducted for the study of voluntary hand movement. The results are similar but in contrast to the LAR maps, the LE maps show a better spatial resolution. This is not surprising since the LAR method is limited to the electrode sites whereas with LE the EEG activity is projected onto the source space. Furthermore, the LE method counteracts the deblurring caused by the poorly conducting skull. Linear Estimation depends on several assumptions about the source space, volume conductor and the regularization parameter. Further investigation is needed to evaluate the application of LE for the study of Event-Related EEG phenomena

Cortical Imaging Based on an Analytic High Resolution EEG

It is well known that the EEG is a blurred and spatially low-pass filtered representation of the cortical activity. Since additional recording electrodes will not necessarily lead to an improved spatial resolution, further steps have to be taken. We derived the analytic downward continuation of the scalp potential field to an arbitrary inner surface for a spherical volume conductor model with piecewise constant conductivities. The basic idea of the Analytic High Resolution BEG (AHREEG) is the fact that a function defined on a sphere can be expressed as a weighted sum of spherical harmonics. Considering the spatial transfer function between the cortex and the scalp surface, the potential distribution on the cortex is theoretically computed by the application of the inverse transfer function to the scalp potential field. Compared to source localization procedures, the AHREEG is not based on any source distribution or on the nature of the sources. Furthermore the proposed method is unique, though ill-posed. Due to this fact and the fact that real world data are always contaminated by noise, a regularization based on general cross validation is performed to stabilize the inverse solution. Simulation results as well as the application of the AHREEG to EEG recorded during median nerve stimulation will be presented and critically discussed

BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project

This paper highlights aspects related to current research and thinking about ethical issues in relation to Brain Computer Interface (BCI) and Brain-Neuronal Computer Interfaces (BNCI) research through the experience of one particular project, BrainAble, which is exploring and developing the potential of these technologies to enable people with complex disabilities to control computers. It describes how ethical practice has been developed both within the multidisciplinary research team and with participants. Results: The paper presents findings in which participants shared their views of the project prototypes, of the potential of BCI/BNCI systems as an assistive technology, and of their other possible applications. This draws attention to the importance of ethical practice in projects where high expectations of technologies, and representations of “ideal types” of disabled users may reinforce stereotypes or drown out participant “voices”. Conclusions: Ethical frameworks for research and development in emergent areas such as BCI/BNCI systems should be based on broad notions of a “duty of care” while being sufficiently flexible that researchers can adapt project procedures according to participant needs. They need to be frequently revisited, not only in the light of experience, but also to ensure they reflect new research findings and ever more complex and powerful technologies

The Dynamics of Sensorimotor Cortical Oscillations during the Observation of Hand Movements: An EEG Study

Background The observation of action done by others determines a desynchronization of the rhythms recorded from cortical central regions. Here, we examined whether the observation of different types of hand movements (target directed, non-target directed, cyclic and non-cyclic) elicits different EEG cortical temporal patterns. Methodology Video-clips of four types of hand movements were shown to right-handed healthy participants. Two were target directed (grasping and pointing) motor acts; two were non-target directed (supinating and clenching) movements. Grasping and supinating were performed once, while pointing and clenching twice (cyclic movements). High-density EEG was recorded and analyzed by means of wavelet transform, subdividing the time course in time bins of 200 ms. The observation of all presented movements produced a desynchronization of alpha and beta rhythms in central and parietal regions. The rhythms desynchronized as soon as the hand movement started, the nadir being reached around 700 ms after movement onset. At the end of the movement, a large power rebound occurred for all bands. Target and non-target directed movements produced an alpha band desynchronization in the central electrodes at the same time, but with a stronger desynchronization and a prolonged rebound for target directed motor acts. Most interestingly, there was a clear correlation between the velocity profile of the observed movements and beta band modulation. Significance Our data show that the observation of motor acts determines a modulation of cortical rhythm analogous to that occurring during motor act execution. In particular, the cortical motor system closely follows the velocity of the observed movements. This finding provides strong evidence for the presence in humans of a mechanism (mirror mechanism) mapping action observation on action execution motor programs