Local field potential-based brain-machine interface to inhibit epileptic seizures by spinal cord electrical stimulation.

This study proposes a closed-loop brain-machine interface (BMI) based on spinal cord stimulation to inhibit epileptic seizures, applying a semi-supervised machine learning approach that learns from Local Field Potential (LFP) patterns acquired on the pre-ictal (preceding the seizure) condition.LFP epochs from the hippocampus and motor cortex are band-pass filtered from 1 to 13 Hz, to obtain the time-frequency representation using the continuous Wavelet transform, and successively calculate the phase lock values (PLV). As a novelty, the-score-based PLV normalization using both modified-means and Davies-Bouldin's measure for clustering is proposed here.

Spinal Cord Stimulation Modulates Rat Cortico-Basal Ganglia Locomotor Circuit.

Objective: The cortico-basal ganglia circuit is crucial to understanding locomotor behavior and movement disorders. Spinal cord stimulation modulates that circuit, which is a promising approach to restoring motor functions. However, the effects of electrical spinal cord stimulation in the healthy brain motor circuit in pre- and postgait are poorly understood.

Brain connectivity analysis in preictal phases of seizure induced by pentylenetetrazol in rats.

Abnormal patterns of brain connectivity characterize epilepsy. However, little is known about these patterns during the stages preceding a seizure induced by pentylenetetrazol (PTZ). To investigate brain connectivity in male Wistar rats during the preictal phase of PTZ-induced seizures (60 mg/kg), we recorded local field potentials in the primary motor (M1) cortex, the ventral anterior (VA) nucleus of the thalamus, the hippocampal CA1 area, and the dentate gyrus (DG) during the baseline period and after PTZ administration.

Empirical comparison of deep learning methods for EEG decoding.

Electroencephalography (EEG) is a technique that can be used in non-invasive brain-machine interface (BMI) systems to register brain electrical activity. The EEG signals are non-linear and non-stationary, making the decoding procedure a complex task. Deep learning techniques have been successfully applied in several research fields, often improving the results compared with traditional approaches.

PyRAT: An Open-Source Python Library for Animal Behavior Analysis.

Here we developed an open-source Python-based library called Python rodent Analysis and Tracking (PyRAT). Our library analyzes tracking data to classify distinct behaviors, estimate traveled distance, speed and area occupancy. To classify and cluster behaviors, we used two unsupervised algorithms: hierarchical agglomerative clustering and t-distributed stochastic neighbor embedding (t-SNE).

Transcranial Direct Current Stimulation on Parkinson's Disease: Systematic Review and Meta-Analysis.

Clinical impact of transcranial direct current stimulation (tDCS) alone for Parkinson's disease (PD) is still a challenge. Thus, there is a need to synthesize available results, analyze methodologically and statistically, and provide evidence to guide tDCS in PD. Investigate isolated tDCS effect in different brain areas and number of stimulated targets on PD motor symptoms.

Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation.

The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software.

Electrical stimulation in animal models of epilepsy: A review on cellular and electrophysiological aspects.

Epilepsy is a debilitating condition, primarily refractory individuals, leading to the search for new efficient therapies. Electrical stimulation is an important method used for years to treat several neurological disorders. Currently, electrical stimulation is used to reduce epileptic crisis in patients and shows promising results.