Ensemble Fractional Sensitivity: A Quantitative Approach to Neuron Selection for Decoding Motor Tasks

A robust method to help identify the population of neurons used for decoding motor tasks is developed. We use sensitivity analysis to develop a new metric for quantifying the relative contribution of a neuron towards the decoded output, called “fractional sensitivity.” Previous model-based approaches for neuron ranking have been shown to largely depend on the collection of training data. We suggest the use of an ensemble of models that are trained on random subsets of trials to rank neurons. For this work, we tested a decoding algorithm on neuronal data recorded from two male rhesus monkeys while they performed a reach to grasp a bar at three orientations (45°, 90°, or 135°). An ensemble approach led to a statistically significant increase of 5% in decoding accuracy and 25% increase in identification accuracy of simulated noisy neurons, when compared to a single model. Furthermore, ranking neurons based on the ensemble fractional sensitivities resulted in decoding accuracies 10%–20% greater than when randomly selecting neurons or ranking based on firing rates alone. By systematically reducing the size of the input space, we determine the optimal number of neurons needed for decoding the motor output. This selection approach has practical benefits for other BMI applications where limited number of electrodes and training datasets are available, but high decoding accuracies are desirable

Hypertension Detector for Developing Countries

For low-income countries, hypertension is the leading cause of death. Preeclampsia, a disorder often characterized by high blood pressure, is the second leading killer of pregnant women globally. Preeclampsia can be treated cheaply and effectively but very few women receive appropriate prenatal care. There are many different devices to measure blood pressure but they are poorly suited for use in developing countries. Great care has to be taken to engineer a device that incorporates the human-factors involved while maintaining affordability. A prototype of a low-cost device engineered specifically for semi-literate volunteers in developing countries has been created. Preliminary testing has shown reliable hypertension detection and plans have been made for field testing in rural communities this August 2010 in Nepal

Cost-effective therapeutic hypothermia treatment device for hypoxic ischemic encephalopathy

Despite recent advances in neonatal care and monitoring, asphyxia globally accounts for 23% of the 4 million annual deaths of newborns, and leads to hypoxic-ischemic encephalopathy (HIE). Occurring in five of 1000 live-born infants globally and even more in developing countries, HIE is a serious problem that causes death in 25%–50% of affected neonates and neurological disability to at least 25% of survivors. In order to prevent the damage caused by HIE, our invention provides an effective whole-body cooling of the neonates by utilizing evaporation and an endothermic reaction. Our device is composed of basic electronics, clay pots, sand, and urea-based instant cold pack powder. A larger clay pot, lined with nearly 5 cm of sand, contains a smaller pot, where the neonate will be placed for therapeutic treatment. When the sand is mixed with instant cold pack urea powder and wetted with water, the device can extract heat from inside to outside and maintain the inner pot at 17°C for more than 24 hours with monitoring by LED lights and thermistors. Using a piglet model, we confirmed that our device fits the specific parameters of therapeutic hypothermia, lowering the body temperature to 33.5°C with a 1°C margin of error. After the therapeutic hypothermia treatment, warming is regulated by adjusting the amount of water added and the location of baby inside the device. Our invention uniquely limits the amount of electricity required to power and operate the device compared with current expensive and high-tech devices available in the United States. Our device costs a maximum of 40 dollars and is simple enough to be used in neonatal intensive care units in developing countries

A brain-computer interface with vibrotactile biofeedback for haptic information

<p>Abstract</p> <p>Background</p> <p>It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only <it>vibrotactile feedback</it>, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy.</p> <p>Methods</p> <p>A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance.</p> <p>Results and Conclusion</p> <p>Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.</p

Ensemble Fractional Sensitivity: A Quantitative Approach to Neuron Selection for Decoding Motor Tasks

A robust method to help identify the population of neurons used for decoding motor tasks is developed. We use sensitivity analysis to develop a new metric for quantifying the relative contribution of a neuron towards the decoded output, called &quot;fractional sensitivity.&quot; Previous model-based approaches for neuron ranking have been shown to largely depend on the collection of training data. We suggest the use of an ensemble of models that are trained on random subsets of trials to rank neurons. For this work, we tested a decoding algorithm on neuronal data recorded from two male rhesus monkeys while they performed a reach to grasp a bar at three orientations (45 • , 90 • , or 135 • ). An ensemble approach led to a statistically significant increase of 5% in decoding accuracy and 25% increase in identification accuracy of simulated noisy neurons, when compared to a single model. Furthermore, ranking neurons based on the ensemble fractional sensitivities resulted in decoding accuracies 10%-20% greater than when randomly selecting neurons or ranking based on firing rates alone. By systematically reducing the size of the input space, we determine the optimal number of neurons needed for decoding the motor output. This selection approach has practical benefits for other BMI applications where limited number of electrodes and training datasets are available, but high decoding accuracies are desirable

Coarse Electrocorticographic Decoding of Ipsilateral Reach in Patients with Brain Lesions

<div><p>In patients with unilateral upper limb paralysis from strokes and other brain lesions, strategies for functional recovery may eventually include brain-machine interfaces (BMIs) using control signals from residual sensorimotor systems in the damaged hemisphere. When voluntary movements of the contralateral limb are not possible due to brain pathology, initial training of such a BMI may require use of the unaffected ipsilateral limb. We conducted an offline investigation of the feasibility of decoding ipsilateral upper limb movements from electrocorticographic (ECoG) recordings in three patients with different lesions of sensorimotor systems associated with upper limb control. We found that the first principal component (PC) of unconstrained, naturalistic reaching movements of the upper limb could be decoded from ipsilateral ECoG using a linear model. ECoG signal features yielding the best decoding accuracy were different across subjects. Performance saturated with very few input features. Decoding performances of 0.77, 0.73, and 0.66 (median Pearson's <i>r</i> between the predicted and actual first PC of movement using nine signal features) were achieved in the three subjects. The performance achieved here with small numbers of electrodes and computationally simple decoding algorithms suggests that it may be possible to control a BMI using ECoG recorded from damaged sensorimotor brain systems.</p></div