6 research outputs found
Cortical representations of auditory perception using graph independent component analysis on EEG
Recent studies indicate that the neurons involved in a cognitive task aren't locally limited but span out to multiple human brain regions. We obtain network components and their locations for the task of listening to music. The recorded EEG data is modeled as a graph, and it is assumed that the overall activity is a contribution of several independent subnetworks. To identify these intrinsic cognitive subnetworks corresponding to music perception, we propose to decompose the whole brain graph-network into multiple subnetworks. We perform this decomposition to a group of brain networks by performing Graph-Independent Component Analysis. Graph-ICA is a variant of ICA that decomposes the measured graph into independent source graphs. Having obtained independent subnetworks, we calculate the electrode positions by computing the local maxima of these subnetwork matrices. We observe that the computed electrodes' location corresponds to the temporal lobes and the Broca's area, which are indeed involved in the task of auditory processing and perception. The computed electrodes also span the brain's frontal lobe, which is involved in attention and generating a stimulus-evoked response. The weight of the subnetwork that corresponds to the aforementioned brain regions increases with the increase in the music recording's tempo. The results suggest that whole-brain networks can be decomposed into independent subnetworks and analyze cognitive responses to music stimulus
Indoor Distance Estimation using LSTMs over WLAN Network
The Global Navigation Satellite Systems (GNSS) like GPS suffer from accuracy
degradation and are almost unavailable in indoor environments. Indoor
positioning systems (IPS) based on WiFi signals have been gaining popularity.
However, owing to the strong spatial and temporal variations of wireless
communication channels in the indoor environment, the achieved accuracy of
existing IPS is around several tens of centimeters. We present the detailed
design and implementation of a self-adaptive WiFi-based indoor distance
estimation system using LSTMs. The system is novel in its method of estimating
with high accuracy the distance of an object by overcoming possible causes of
channel variations and is self-adaptive to the changing environmental and
surrounding conditions. The proposed design has been developed and physically
realized over a WiFi network consisting of ESP8266 (NodeMCU) devices. The
experiment were conducted in a real indoor environment while changing the
surroundings in order to establish the adaptability of the system. We introduce
and compare different architectures for this task based on LSTMs, CNNs, and
fully connected networks (FCNs). We show that the LSTM based model performs
better among all the above-mentioned architectures by achieving an accuracy of
5.85 cm with a confidence interval of 93% on the scale of (4.14 m * 2.86 m). To
the best of our knowledge, the proposed method outperforms other methods
reported in the literature by a significant margin.Comment: Published in IEEE 16th Workshop on Positioning, Navigation and
Communications (WPNC 2019, Germany
Mutual Information Scoring: Increasing Interpretability in Categorical Clustering Tasks with Applications to Child Welfare Data
Youth in the American foster care system are significantly more likely than
their peers to face a number of negative life outcomes, from homelessness to
incarceration. Administrative data on these youth have the potential to provide
insights that can help identify ways to improve their path towards a better
life. However, such data also suffer from a variety of biases, from missing
data to reflections of systemic inequality. The present work proposes a novel,
prescriptive approach to using these data to provide insights about both data
biases and the systems and youth they track. Specifically, we develop a novel
categorical clustering and cluster summarization methodology that allows us to
gain insights into subtle biases in existing data on foster youth, and to
provide insight into where further (often qualitative) research is needed to
identify potential ways of assisting youth
A qualitative, network-centric method for modeling socio-technical systems, with applications to evaluating interventions on social media platforms to increase social equality
We propose and extend a qualitative, complex systems methodology from cognitive engineering, known as theabstraction hierarchy, to model how potential interventions that could be carried out by social media platforms might impact social equality. Social media platforms have come under considerable ire for their role in perpetuating social inequality. However, there is also significant evidence that platforms can play a role inreducingsocial inequality, e.g. through the promotion of social movements. Platforms’ role in producing or reducing social inequality is, moreover, not static; platforms can and often do take actions targeted at positive change. How can we develop tools to help us determine whether or not a potential platform change might actually work to increase social equality? Here, we present the abstraction hierarchy as a tool to help answer this question. Our primary contributions are two-fold. First, methodologically, we extend existing research on the abstraction hierarchy in cognitive engineering with principles from Network Science. Second, substantively, we illustrate the utility of this approach by using it to assess the potential effectiveness of a set of interventions, proposed in prior work, for how online dating websites can help mitigate social inequality
Pranav Sankhe's Quick Files
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