International IEEE/EMBS Conference on Neural Engineering : [proceedings]. International IEEE EMBS Conference on Neural Engineering最新文献

In this study, modulation index (MI) features derived from local field potential (LFP) recordings in the subthalamic nucleus (STN) and electroencephalographic recordings (EEGs) from the primary motor cortex are shown to correlate with both the overall motor impairment and motor subscores in a monkey model of parkinsonism. The MI features used are measures of phase-amplitude cross frequency coupling (CFC) between frequency sub-bands. We used complex wavelet transforms to extract six spectral sub-bands within the 3-60 Hz range from LFP and EEG signals. Using the method of canonical correlation, we show that weighted combinations of the MI features in LFP or EEG signals correlate significantly with individual and composite scores on a scale for parkinsonian disability.

We propose a simple modification of the Izhikevich neuron model to restrict firing rates of neurons. We demonstrate how this modification affects overall network activity using a simple artificial network. Such restraint on the Izhikevich neuron model would be especially important in larger scale simulations or when frequency dependent short-term plasticity is one of the network components. Although maximum firing rates are most likely exceeded in simulations of seizure like activity or other conditions that promote excessive excitation, we show that restriction of neuronal firing frequencies has impact even on small networks with moderate levels of input.

The paper presents results of modeling global and focal loss of layers in a multi-columnar model of neocortex. Specifically, the spread of activity across columns in conditions of inhibitory blockade is compared. With very low inhibition activity spreads through all layers, however, deep layers are critical for spread of activity when inhibition is only moderately blocked.

The ability to selectively deposit platinum black (PtB) on iridium microelectrodes and functionalize the surface for the purposes of choline sensing was investigated in this study. Platinum black was deposited by cycling 100-200 times between 0.5 V and -0.7 V in a solution of 1 mM K₂PtCl₆ in 0.1 M KCl. Deposition of PtB showed good chemical stability as well as good adhesion following insertion into agarose gel as a model for brain insertion. Electrode sites were also tested for their oxidative capabilities of hydrogen peroxide during which they showed high current change in response to small concentration changes - attributable to the high surface area of the PtB. Sites were then coated with an enzyme solution containing choline oxidase, and a permselective layer of meta-phenylenediamine was added to filter interferents. Electrode sites yielded a high sensitivity to choline compared to interferents including ascorbic acid and dopamine.

Mindfulness meditation (MM) is an inward mental practice, in which a resting but alert state of mind is maintained. MM intervention was performed for a population of older people with high stress levels. This study assessed signal processing methodologies of electroencephalographic (EEG) and respiration signals during meditation and control condition to aid in quantification of the meditative state. EEG and respiration data were collected and analyzed on 34 novice meditators after a 6-week meditation intervention. Collected data were analyzed with spectral analysis and support vector machine classification to evaluate an objective marker for meditation. We observed meditation and control condition differences in the alpha, beta and theta frequency bands. Furthermore, we established a classifier using EEG and respiration signals with a higher accuracy at discriminating between meditation and control conditions than one using the EEG signal only. EEG and respiration based classifier is a viable objective marker for meditation ability. Future studies should quantify different levels of meditation depth and meditation experience using this classifier. Development of an objective physiological meditation marker will allow the mind-body medicine field to advance by strengthening rigor of methods.

Activity-dependent electrical stimulation can induce cerebrocortical reorganization in vivo by activating brain areas using stimulation derived from the statistics of neural or muscular activity. Due to the nature of synaptic plasticity, network topology is likely to influence the effectiveness of this type of neuromodulation, yet its effect under different network topologies is unclear. To address this issue, we simulated small-scale three-neuron networks to explore topology-dependent network plasticity. The induced neuroplastic changes were evaluated by network coherence and unit-pair mutual information measures. We demonstrated that involvement of monosynaptic feedforward and reciprocal connections is more likely to lead to persistent decreased network coherence and increased network mutual information independent of the global network topology. On the contrary, disynaptic feedforward connections exhibit heterogeneous coherence and unit-pair mutual information sensitivity that depends strongly upon the network context.

Tracking spectral changes in neural signals, such as local field potentials (LFPs) and scalp or intracranial electroencephalograms (EEG, iEEG), is an important problem in early detection and prediction of seizures. Most approaches have focused on either parametric or nonparametric spectral estimation methods based on moving time windows. Here, we explore an adaptive (time-varying) parametric ARMA approach for tracking spectral changes in neural signals based on the fixed-interval Kalman smoother. We apply the method to seizure detection based on spectral features of intracortical LFPs recorded from a person with pharmacologically intractable focal epilepsy. We also devise and test an approach for real-time tracking of spectra based on the adaptive parametric method with the fixed-interval Kalman smoother. The order of ARMA models is determined via the AIC computed in moving time windows. We quantitatively demonstrate the advantages of using the adaptive parametric estimation method in seizure detection over nonparametric alternatives based exclusively on moving time windows. Overall, the adaptive parametric approach significantly improves the statistical separability of interictal and ictal epochs.

The unpredictability of re-occurring seizures dramatically impacts the quality of life and autonomy of people with epilepsy. Reliable early seizure detection could open new therapeutic possibilities and thus substantially improve quality of life and autonomy. Though many seizure detection studies have shown the potential of scalp electroencephalogram (EEG) and intracranial EEG (iEEG) signals, reliable early detection of human seizures remains elusive in practice. Here, we examined the use of intracortical local field potentials (LFPs) recorded from 4×4-mm² 96-microelectrode arrays (MEA) for early detection of human epileptic seizures. We adopted a framework consisting of (1) sampling of intracortical LFPs; (2) denoising of LFPs with the Kalman filter; (3) spectral power estimation in specific frequency bands using 1-sec moving time windows; (4) extraction of statistical features, such as the mean, variance, and Fano factor (calculated across channels) of the power in each frequency band; and (5) cost-sensitive support vector machine (SVM) classification of ictal and interictal samples. We tested the framework in one-participant dataset, including 4 seizures and corresponding interictal recordings preceding each seizure. The participant was a 52-year-old woman suffering from complex partial seizures. LFPs were recorded from an MEA implanted in the participant's left middle temporal gyrus. In this participant, spectral power in 0.3-10 Hz, 20-55 Hz, and 125-250 Hz changed significantly between ictal and interictal epochs. The examined seizure detection framework provided an event-wise sensitivity of 100% (4/4) and only one 20-sec-long false positive event in interictal recordings (likely an undetected subclinical event under further visual inspection), and a detection latency of 4.35 ± 2.21 sec (mean ± std) with respect to iEEG-identified seizure onsets. These preliminary results indicate that intracortical MEA recordings may provide key signals to quickly and reliably detect human seizures.

In this paper, we summarize a novel approach to robotic rehabilitation that capitalizes on the benefits of patient intent and real-time assessment of impairment. Specifically, an upper-limb, physical human-robot interface (the MAHI EXO-II robotic exoskeleton) is augmented with a non-invasive brain-machine interface (BMI) to include the patient in the control loop, thereby making the therapy 'active' and engaging patients across a broad spectrum of impairment severity in the rehabilitation tasks. Robotic measures of motor impairment are derived from real-time sensor data from the MAHI EXO-II and the BMI. These measures can be validated through correlation with widely used clinical measures and used to drive patient-specific therapy sessions adapted to the capabilities of the individual, with the MAHI EXO-II providing assistance or challenging the participant as appropriate to maximize rehabilitation outcomes. This approach to robotic rehabilitation takes a step towards the seamless integration of BMIs and intelligent exoskeletons to create systems that can monitor and interface with brain activity and movement. Such systems will enable more focused study of various issues in development of devices and rehabilitation strategies, including interpretation of measurement data from a variety of sources, exploration of hypotheses regarding large scale brain function during robotic rehabilitation, and optimization of device design and training programs for restoring upper limb function after stroke.