Flexible electroencephalogram (EEG) headband

Headband incorporates sensors which are embedded in sponges and are exposed only on surface that touches skin. Electrode sponge system is continually fed electrolyte through forced feed vacuum system. Headband may be used for EEG testing in hospitals, clinical laboratories, rest homes, and law enforcement agencies

EEG and EMG Sensorimotor Measurements to Assess Proprioception Following ACL Reconstruction

The Anterior Cruciate Ligament (ACL) is the primary source of rotational stability in the knee by preventing the tibia from sliding in front of the femur. When the ACL is torn, it typically must be repaired through reconstructive surgery which results in proprioceptive deficiencies in the knee. Proprioception plays an important role in understanding where one’s knee is in space, sensing movement and reacting accordingly. This study examines an alternative method of measuring proprioceptive responses to a stimulus (motion) by using electromyogram (EMG) and electroencephalogram (EEG) signals to observe muscle and brain activity. Two participants (one with an ACL reconstruction and a second with healthy knees) were tested three times over a six week period. Repeated measures allowed for an initial examination of how proprioception may vary over time in an individual with healthy knees and with an ACL reconstruction. This measurement strategy can examine the process of proprioception recovery after an ACL reconstruction. It has the potential to help physicians and physical therapists decide when a person can return to normal or strenuous activity as well as provide insight into whether uninjured patients have a proprioceptive deficit which may indicate an increased risk of injury

Investigation of Machine Learning Approaches for Traumatic Brain Injury Classification via EEG Assessment in Mice.

Due to the difficulties and complications in the quantitative assessment of traumatic brain injury (TBI) and its increasing relevance in today's world, robust detection of TBI has become more significant than ever. In this work, we investigate several machine learning approaches to assess their performance in classifying electroencephalogram (EEG) data of TBI in a mouse model. Algorithms such as decision trees (DT), random forest (RF), neural network (NN), support vector machine (SVM), K-nearest neighbors (KNN) and convolutional neural network (CNN) were analyzed based on their performance to classify mild TBI (mTBI) data from those of the control group in wake stages for different epoch lengths. Average power in different frequency sub-bands and alpha:theta power ratio in EEG were used as input features for machine learning approaches. Results in this mouse model were promising, suggesting similar approaches may be applicable to detect TBI in humans in practical scenarios

Systems, methods and devices for treating tinnitus

Systems, methods and devices for paired training include timing controls so that training and neural stimulation can be provided simultaneously. Paired trainings may include therapies, rehabilitation and performance enhancement training. Stimulations of nerves such as the vagus nerve that affect subcortical regions such as the nucleus basalis, locus coeruleus or amygdala induce plasticity in the brain, enhancing the effects of a variety of therapies, such as those used to treat tinnitus, stroke, traumatic brain injury and post-traumatic stress disorder.Board of Regents, University of Texas Syste

Analyzing Thought-related Electroencephalographic Data Using Nonlinear Techniques

A unique method is presented for collecting, studying and interpreting thought-related electroencephalogram (EEG) data. The use of a chaos based nonlinear analysis technique is shown to be promising in providing insight into relating conscious thought to specific EEG data. A discussion of the practical limitations of this technique is also included

Efficient Methods for Calculating Sample Entropy in Time Series Data Analysis

Recently, different algorithms have been suggested to improve Sample Entropy (SE) performance. Although new methods for calculating SE have been proposed, so far improving the efficiency (computational time) of SE calculation methods has not been considered. This research shows such an analysis of calculating a correlation between Electroencephalogram(EEG) and Heart Rate Variability(HRV) based on their SE values. Our results indicate that the parsimonious outcome of SE calculation can be achieved by exploiting a new method of SE implementation. In addition, it is found that the electrical activity in the frontal lobe of the brain appears to be correlated with the HRV in a time domain.Peer reviewe

Slow Sphering to Suppress Non-Stationaries in the EEG

Non-stationary signals are ubiquitous in electroencephalogram (EEG) signals and pose a problem for robust application of brain-computer interfaces (BCIs). These non-stationarities can be caused by changes in neural background activity. We present a dynamic spatial filter based on time local whitening that significantly reduces the detrimental influence of covariance changes during event-related desynchronization classification of an imaginary movement task

Gamma and beta frequency oscillations in response to novel auditory stimuli: A comparison of human electroencephalogram (EEG) data with in vitro models

Investigations using hippocampal slices maintained in vitro have demonstrated that bursts of oscillatory field potentials in the gamma frequency range (30-80 Hz) are followed by a slower oscillation in the beta 1 range (12-20 Hz). In this study, we demonstrate that a comparable gamma-to-beta transition is seen in the human electroencephalogram (EEG) in response to novel auditory stimuli. Correlations between gamma and beta 1 activity revealed a high degree of interdependence of synchronized oscillations in these bands in the human EEG. Evoked (stimulus-locked) gamma oscillations preceded beta 1 oscillations in response to novel stimuli, suggesting that this may be analogous to the gamma-to-beta shift observed in vitro. Beta 1 oscillations were the earliest discriminatory responses to show enhancement to novel stimuli, preceding changes in the broad-band event-related potential (mismatch negativity). Later peaks of induced beta activity over the parietal cortex were always accompanied by an underlying gamma frequency oscillation as seen in vitro. A further analogy between in vitro and human recordings was that both gamma and beta oscillations habituated markedly after the initial novel stimulus presentation

Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

Non-motor symptoms in Parkinson's disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brain's electrical activity