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Electrical microstimulation and near-infrared (NIR) laser light irradiation were combined to control neural activities in cerebral cortex in vitro. Spatio-temporal patterns of suprathreshold neural excitations in the primary visual cortex of mouse cerebral slices were visualized by the Ca2+-sensitive dye imaging. In response to a single pulse of the biphasic stimulus current delivered to the layer...
We present here a browser-based application for visualizing patterns of connectivity in 3D stacked data matrices with large numbers of pairwise relations. Visualizing a connectivity matrix, looking for trends and patterns, and dynamically manipulating these values is a challenge for scientists from diverse fields, including neuroscience and genomics. In particular, high-dimensional neural data include...
Recently, SSVEP detection from EEG signals has attracted the interest of the research community, leading to a number of well-tailored methods, such as Canonical Correlation Analysis (CCA) and a number of variants. Despite their effectiveness, due to their strong dependence on the correct calculation of correlations, these methods may prove to be inadequate in front of potential deficiency in the number...
Understanding how multiple muscles are recruited across a joint has typically been approached from relating muscle activity to resultant forces. New tools are now available to look deeper into a muscle's recruitment by observing groups of single motor units. Identifying changes in the motor unit activity both within a muscle and between muscles the redundancies in motor control can be better understood...
We assessed the impact of different motor unit action potential (MUAP) components in dynamic muscle contractions on decomposition of high-density surface electromyograms (hdEMG). In particular, hypothesis that nontravelling MUAP components, originating from the tendon regions, are less sensitive to changes in geometry of fusiform muscles than travelling MUAP components has been tested on synthetic...
Accurate estimation of volumes for cerebrospinal fluid (CSF) and brain before and after surgery (pre-op and post-op) plays an important role in analyzing treatment for hydrocephalus. This in turn, relies upon segmentation of brain imagery into brain tissue and CSF. Segmentation of preop images is a relatively straightforward problem and has been well researched. However, segmenting post-op CT-scans...
Welcome to Shanghai! It is our great pleasure to welcome you to the 8th International IEEE EMBS Conference on Neural Engineering (NER), which for the first time is being held in Asia and China. The conference will highlight engineering innovations towards understanding and restoring the functions of the peripheral and central nervous systems. NER has the become the forum to report the latest developments...
Lists six panel discussion titles and their participants. A record of the panel discussions was not made available for publication as part of the conference proceedings.
Motor imagery based BCIs are one of the most important BCI paradigms. Although it has been studied for a long time, the EEG features for kinetic information of motor imagery are still less known. In this paper, we explored EEG patterns of hand force motor imagery. Six subjects participated in this study, who were required to imagine clenching their hands with two different levels of force during the...
How the brain maintains the stability of visual perception across saccade is a central question in systems neuroscience; accurately characterizing visual responses in the perisaccadic period is an important step towards understanding how the visual world is represented during saccades. Here, we develop a probabilistic model in the Generalized Linear Model framework to characterize and predict the...
Working memory processing is central for higher-order cognitive functions. Although the ability to access and extract working memory load has been proven feasible, the temporal resolution is low and cross-task generalization is poor. In this study, EEG oscillatory activity was recorded from sixteen healthy subjects while they performed two versions of the visual n-back task. Observed effects in the...
This paper reports how to train a Deep Belief Network (DBN) using only 8-bit fixed-point parameters. We propose a dynamic-point stochastic rounding algorithm that provides enhanced results compared to the existing stochastic rounding. We show that by using a variable scaling factor, the fixed-point parameter updates are enhanced. To be more hardware amenable, the use of common scaling factor at each...
Despite the recent increasing interest in biometric identification using electroencephalogram (EEG) signals, the state of the art still lacks a simple and robust model that is useful in real applications. This work proposes a new approach based on convolutional neural network CNN. The proposed CNN works directly on raw EEG data, thus alleviating the need for engineering features. We investigate the...
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