Improving the magnetoelectric performance of Metglas/PZT laminates by annealing in a magnetic field.

A comprehensive investigation of magnetostriction optimization in Metglas 2605SA1 ribbons is performed to enhance magnetoelectric performance. We explore a range of annealing conditions to relieve remnant stress and align the magnetic domains in the Metglas, while minimizing unwanted crystallization. The magnetostriction coefficient, magnetoelectric coefficient, and magnetic domain alignment are correlated to optimize magnetoelectric performance. We report on direct magnetostriction observed by in-plane Doppler vibrometer and domain imagining using scanning electron microscopy with polarization analysis for a range of annealing conditions. We find that annealing in an oxygen-free environment at 400 °C for 30 min yields an optimal magnetoelectric coefficient, magnetostriction and magnetostriction coefficient. The optimized ribbons had a magnetostriction of 50.6 ± 0.2 µm m-1 and magnetoelectric coefficient of 79.3 ± 1.5 µm m-1 mT-1. The optimized Metglas 2605SA1 ribbons and PZT-5A (d31 mode) sensor achieves a magnetic noise floor of approximately 600 pT Hz-1/2 at 100 Hz and a magnetoelectric coefficient of 6.1 ± 0.03 MV m-1 T-1.

Performance predictors of brain-computer interfaces in patients with amyotrophic lateral sclerosis.

OBJECTIVE: Patients with amyotrophic lateral sclerosis (ALS) may benefit from brain-computer interfaces (BCI), but the utility of such devices likely will have to account for the functional, cognitive, and behavioral heterogeneity of this neurodegenerative disorder. APPROACH: In this study, a heterogeneous group of patients with ALS participated in a study on BCI based on the P300 event related potential and motor-imagery. RESULTS: The presence of cognitive impairment in these patients significantly reduced the quality of the control signals required to use these communication systems, subsequently impairing performance, regardless of progression of physical symptoms. Loss in performance among the cognitively impaired was accompanied by a decrease in the signal-to-noise ratio of task-relevant EEG band power. There was also evidence that behavioral dysfunction negatively affects P300 speller performance. Finally, older participants achieved better performance on the P300 system than the motor-imagery system, indicating a preference of BCI paradigm with age. SIGNIFICANCE: These findings highlight the importance of considering the heterogeneity of disease when designing BCI augmentative and alternative communication devices for clinical applications.

Model-based rational feedback controller design for closed-loop deep brain stimulation of Parkinson's disease.

OBJECTIVE: To explore the use of classical feedback control methods to achieve an improved deep brain stimulation (DBS) algorithm for application to Parkinson's disease (PD). APPROACH: A computational model of PD dynamics was employed to develop model-based rational feedback controller design. The restoration of thalamocortical relay capabilities to patients suffering from PD is formulated as a feedback control problem with the DBS waveform serving as the control input. Two high-level control strategies are tested: one that is driven by an online estimate of thalamic reliability, and another that acts to eliminate substantial decreases in the inhibition from the globus pallidus interna (GPi) to the thalamus. Control laws inspired by traditional proportional-integral-derivative (PID) methodology are prescribed for each strategy and simulated on this computational model of the basal ganglia network. MAIN RESULTS: For control based upon thalamic reliability, a strategy of frequency proportional control with proportional bias delivered the optimal control achieved for a given energy expenditure. In comparison, control based upon synaptic inhibitory output from the GPi performed very well in comparison with those of reliability-based control, with considerable further reduction in energy expenditure relative to that of open-loop DBS. The best controller performance was amplitude proportional with derivative control and integral bias, which is full PID control. We demonstrated how optimizing the three components of PID control is feasible in this setting, although the complexity of these optimization functions argues for adaptive methods in implementation. SIGNIFICANCE: Our findings point to the potential value of model-based rational design of feedback controllers for Parkinson's disease.

Toward a model-based predictive controller design in brain-computer interfaces.

A first step in designing a robust and optimal model-based predictive controller (MPC) for brain-computer interface (BCI) applications is presented in this article. An MPC has the potential to achieve improved BCI performance compared to the performance achieved by current ad hoc, nonmodel-based filter applications. The parameters in designing the controller were extracted as model-based features from motor imagery task-related human scalp electroencephalography. Although the parameters can be generated from any model-linear or non-linear, we here adopted a simple autoregressive model that has well-established applications in BCI task discriminations. It was shown that the parameters generated for the controller design can as well be used for motor imagery task discriminations with performance (with 8-23% task discrimination errors) comparable to the discrimination performance of the commonly used features such as frequency specific band powers and the AR model parameters directly used. An optimal MPC has significant implications for high performance BCI applications.

A square root ensemble Kalman filter application to a motor-imagery brain-computer interface.

We here investigated a non-linear ensemble Kalman filter (SPKF) application to a motor imagery brain computer interface (BCI). A square root central difference Kalman filter (SR-CDKF) was used as an approach for brain state estimation in motor imagery task performance, using scalp electroencephalography (EEG) signals. Healthy human subjects imagined left vs. right hand movements and tongue vs. bilateral toe movements while scalp EEG signals were recorded. Offline data analysis was conducted for training the model as well as for decoding the imagery movements. Preliminary results indicate the feasibility of this approach with a decoding accuracy of 78%-90% for the hand movements and 70%-90% for the tongue-toes movements. Ongoing research includes online BCI applications of this approach as well as combined state and parameter estimation using this algorithm with different system dynamic models.

Feature selection on movement imagery discrimination and attention detection.

Noninvasive brain-computer interfaces (BCI) translate subject's electroencephalogram (EEG) features into device commands. Large feature sets should be down-selected for efficient feature translation. This work proposes two different feature down-selection algorithms for BCI: (a) a sequential forward selection; and (b) an across-group variance. Power rar ratios (PRs) were extracted from the EEG data for movement imagery discrimination. Event-related potentials (ERPs) were employed in the discrimination of cue-evoked responses. While center-out arrows, commonly used in calibration sessions, cued the subjects in the first experiment (for both PR and ERP analyses), less stimulating arrows that were centered in the visual field were employed in the second experiment (for ERP analysis). The proposed algorithms outperformed other three popular feature selection algorithms in movement imagery discrimination. In the first experiment, both algorithms achieved classification errors as low as 12.5% reducing the feature set dimensionality by more than 90%. The classification accuracy of ERPs dropped in the second experiment since centered cues reduced the amplitude of cue-evoked ERPs. The two proposed algorithms effectively reduced feature dimensionality while increasing movement imagery discrimination and detected cue-evoked ERPs that reflect subject attention.

Optimization of electrode channels in Brain Computer Interfaces.

What is the optimal number of electrodes one can use in discrimination of tasks for a Brain Computer Interface (BCI)? To address this question, the number and location of scalp electrodes in the acquisition of human electroencephalography (EEG) and discrimination of motor imagery tasks were optimized by using a systematic optimization approach. The systematic analysis results in the most reliable procedure in electrode optimization as well as a validating means for the other feature selection techniques. We acquired human scalp EEG in response to cue-based motor imagery tasks. We employed a systematic analysis by using all possible combinations of the channels and calculating task discrimination errors for each of these combinations by using linear discriminant analysis (LDA) for feature classification. Channel combination that resulted in the smallest discrimination error was selected as the optimum number of channels to be used in BCI applications. Results from the systematic analysis were compared with another feature selection algorithm: forward stepwise feature selection combined with LDA feature classification. Our results demonstrate the usefulness of the fully optimized technique for a reliable selection of scalp electrodes in BCI applications.

Model-based responses and features in Brain Computer Interfaces.

Novel model based features are introduced in the discrimination of motor imagery tasks using human scalp electroencephalography (EEG) towards the development of Brain Computer Interfaces (BCI). We have acquired human scalp EEG under open-loop and feedback conditions in response to cue-based motor imagery tasks. EEG signals, transformed into frequency specific bands such as mu, beta and movement related potentials, were used for feature extraction with the aim to discriminate tasks. Data were classified using features such as power spectrum and model-based parameters. Two different feature selection methods: stepwise and principal component analysis (PCA), were combined with linear discriminant analysis (LDA). Different training/validation criteria were applied for classification of task related features. Results show that the scalp EEG correlate of the imagery tasks of hands/toes/tongue movements under open-loop conditions and left/right hand movements under feedback conditions, can be well discriminated with classification errors below 20%. Model based techniques, which resulted in classification errors in the range of 2%-30%, have the potential to use advanced control systems theory in the development of BCI to achieve improved performance compared to the performance achieved by currently applied proportional control or filter algorithms.

Comparison of EEG pattern classification methods for brain-computer interfaces.

The aim of this study is to compare 2 EEG pattern classification methods towards the development of BCI. The methods are: (1) discriminant stepwise, and (2) Principal Component Analysis (PCA)-Linear Discriminant Analysis (LDA) joint method. Both methods use Fisher's LDA approach, but differ in the data dimensionality reduction procedure. Data were recorded from 3 male subjects 20-30 years old. Three runs per subject took place. The classification methods were tested in 240 trials per subject after merging all runs for the same subject. The mental tasks performed were feet, tongue, left hand and right hand movement imagery. In order to avoid previous assumptions on preferable channel locations and frequency ranges, 105 (21 electrodesx5 frequency ranges) electroencephalogram (EEG) features were extracted from the data. The best performance for each classification method was taken into account. The discriminant stepwise method showed better performance than the PCA based method. The classification error by the stepwise method varied between 31.73% and 38.5% for all subjects whereas the error range using the PCA based method was 39.42% to 54%.

Seizure modulation with applied electric fields in chronically implanted animals.

Low Frequency (<<100Hz) applied electric fields have been shown to modulate neuronal activity both In Vitro and in acute whole animal studies. We have been working to apply this technology for seizure control in chronically implanted animals. We have developed electronics for simultaneously recording neural activity while stimulating with low frequency fields. We have observed transient entrainment of spike and wave activity during spontaneous seizures with open loop sinusoidal stimulation with frequencies between 9-15 Hz. This is the first demonstration of low frequency field modulation of neural activity in chronically implanted mammalian brain.

Early seizure detection.

For patients with medically intractable epilepsy, there have been few effective alternatives to resective surgery, a destructive, irreversible treatment. A strategy receiving increased attention is using interictal spike patterns and continuous EEG measurements from epileptic patients to predict and ultimately control seizure activity via chemical or electrical control systems. This work compares results of seven linear and nonlinear methods (analysis of power spectra, cross-correlation, principal components, phase, wavelets, correlation integral, and mutual prediction) in detecting the earliest dynamical changes preceding 12 intracranially-recorded seizures from 4 patients. A method of counting standard deviations was used to compare across methods, and the earliest departures from thresholds determined from non-seizure EEG were compared to a neurologist's judgement. For these data, the nonlinear methods offered no predictive advantage over the linear methods. All the methods described here were successful in detecting changes leading to a seizure between one and two minutes before the first changes noted by the neurologist, although analysis of phase correlation proved the most robust. The success of phase analysis may be due in part to its complete insensitivity to amplitude, which may provide a significant source of error.

Adaptive electric field control of epileptic seizures.

We describe a novel method of adaptively controlling epileptic seizure-like events in hippocampal brain slices using electric fields. Extracellular neuronal activity is continuously recorded during field application through differential extracellular recording techniques, and the applied electric field strength is continuously updated using a computer-controlled proportional feedback algorithm. This approach appears capable of sustained amelioration of seizure events in this preparation when used with negative feedback. Seizures can be induced or enhanced by using fields of opposite polarity through positive feedback. In negative feedback mode, such findings may offer a novel technology for seizure control. In positive feedback mode, adaptively applied electric fields may offer a more physiological means of neural modulation for prosthetic purposes than previously possible.

From generalized synchrony to topological decoherence: emergent sets in coupled chaotic systems.

We consider the evolution of the unstable periodic orbit structure of coupled chaotic systems. This involves the creation of a complicated set outside of the synchronization manifold (the emergent set). We quantitatively identify a critical transition point in its development (the decoherence transition). For asymmetric systems we also describe a migration of unstable periodic orbits that is of central importance in understanding these systems. Our framework provides an experimentally measurable transition, even in situations where previously described bifurcation structures are inapplicable.

Brain chirps: spectrographic signatures of epileptic seizures.

OBJECTIVE: A chirp is a brief signal within which the frequency content changes rapidly. Spectrographic chirps are found in signals produced from many biological and physical phenomena. In radar and sonar engineering, signals with chirps are used to localize direction and range to the signal source. Although characteristic frequency changes during epileptic seizures have long been observed, the correlation with chirps and chirp technology seems never to have been made. METHODS: We analyzed 19404 s (1870 s of which were from 43 seizures) of intracranially (subdural and depth electrode) recorded digital EEG from 6 patients for the presence of spectral chirps. Matched filters were constructed from methods in routine use in non-medical signal processing applications. RESULTS: We found that chirps are very sensitive detectors of seizures (83%), and highly specific as markers (no false positive detections). The feasibility of using spectral chirps as matched filters was demonstrated. CONCLUSIONS: Chirps are highly specific and sensitive spectrographic signatures of epileptic seizure activity. In addition, chirps may serve as templates for matched filter design to detect seizures, and as such, can demonstrate localization and propagation of seizures from an epileptic focus.

Randomized trial of cerebrospinal fluid shunt valve design in pediatric hydrocephalus.

OBJECTIVE: Forty percent of standard cerebrospinal fluid shunts implanted for the treatment of pediatric hydrocephalus fail within the first year. Two new shunt valves designed to limit excess flow, particularly in upright positions, were studied to compare treatment failure rates with those for standard differential-pressure valves. METHODS: Three hundred-forty-four hydrocephalic children (age, birth to 18 yr) undergoing their first cerebrospinal fluid shunt insertion were randomized at 12 North American or European pediatric neurosurgical centers. Patients received one of three valves, i.e., a standard differential-pressure valve; a Delta valve (Medtronic PS Medical, Goleta, CA), which contains a siphon-control component designed to reduce siphoning in upright positions; or an Orbis-Sigma valve (Cordis, Miami, FL), with a variable-resistance, flow-limiting component. Patients were monitored for a minimum of 1 year. Endpoints were defined as shunt failure resulting from shunt obstruction, overdrainage, loculations of the cerebral ventricles, or infection. Outcome events were assessed by blinded independent case review. RESULTS: One hundred-fifty patients reached an endpoint; shunt obstruction occurred in 108 (31.4%), overdrainage in 12 (3.5%), loculated ventricles in 2 (0.6%), and infection in 28 (8.1%). Sixty-one percent were shunt failure-free at 1 year and 47% at 2 years, with a median shunt failure-free duration of 656 days. There was no difference in shunt failure-free duration among the three valves (P = 0.24). CONCLUSION: Cerebrospinal fluid shunt failure, predominantly from shunt obstruction and infection, remains a persistent problem in pediatric hydrocephalus. Two new valve designs did not significantly affect shunt failure rates.

Periodic orbits: a new language for neuronal dynamics.

A new nonlinear dynamical analysis is applied to complex behavior from neuronal systems. The conceptual foundation of this analysis is the abstraction of observed neuronal activities into a dynamical landscape characterized by a hierarchy of "unstable periodic orbits" (UPOs). UPOs are rigorously identified in data sets representative of three different levels of organization in mammalian brain. An analysis based on UPOs affords a novel alternative method of decoding, predicting, and controlling these neuronal systems.

Noninvasive assessment of language dominance in children and adolescents with functional MRI: a preliminary study.

BACKGROUND: Assessment of language organization is crucial in patients considered for epilepsy surgery. In children, the current techniques, intra-carotid amobarbital test (IAT) for language dominance, and cortical electrostimulation mapping (ESM), are invasive and risky. Functional magnetic resonance imaging (fMRI) is an alternative method for noninvasive functional mapping, through the detection of the hemodynamic changes associated with neuronal activation. We used fMRI, to assess language dominance in children with partial epilepsy. METHODS: Eleven right handed children and adolescents performed a word generation task during fMRI acquisition focused on the frontal lobes. Areas where the signal time course correlated with the test paradigm (r = 0.7) were considered activated. Extent and magnitude of signal changes were used to calculate asymmetry indices. Seven patients had IAT, ESM, or surgery outcome available for comparison. RESULTS: fMRI language dominance always agreed with IAT (6 cases) and ESM (1 case), showing left dominance in six and bilateral language in one. fMRI demonstrated left dominance in three additional children, and right dominance in one with early onset of left temporal epilepsy. Four children whose initial studies were equivocal due to noncompliance or motion artifacts were restudied successfully. CONCLUSIONS: fMRI can be used to assess language lateralization noninvasively in children. It has the potential to replace current functional mapping techniques in patients, and to provide important data on brain development.

Electric field suppression of epileptiform activity in hippocampal slices.

1. The effects of relatively small external DC electric fields on synchronous activity in CA1 and CA3 from transverse and longitudinal type hippocampal slices were studied. 2. To record neuronal activity during significant field changes, differential DC amplification was employed with a reference electrode aligned along an isopotential with the recording electrode. 3. Suppression of epileptiform activity was observed in 31 of 33 slices independent of region studied and type of slice but was highly dependent on field orientation with respect to the apical dendritic-somatic axis. 4. Modulation of neuronal activity in these experiments was readily observed at field strengths < or = 5-10 mV/mm. Suppression was seen with the field oriented (positive to negative potential) from the soma to the apical dentrites. 5. In vivo application of these results may be feasible.

Predictability of EEG interictal spikes.

To determine whether EEG spikes are predictable, time series of EEG spike intervals were generated from subdural and depth electrode recordings from four patients. The intervals between EEG spikes were hand edited to ensure high accuracy and eliminate false positive and negative spikes. Spike rates (per minute) were generated from longer time series, but for these data hand editing was usually not feasible. Linear and nonlinear models were fit to both types of data. One patient had no linear or nonlinear predictability, two had predictability that could be well accounted for with a linear stochastic model, and one had a degree of nonlinear predictability for both interval and rate data that no linear model could adequately account for.