scholarly journals Optimal versus approximate channel selection methods for EEG decoding with application to topology-constrained neuro-sensor networks

2020 ◽  
Author(s):  
Abhijith Mundanad Narayanan ◽  
Panagiotis Patrinos ◽  
Alexander Bertrand
Keyword(s):  
Sensor Networks ◽  
Large Scale ◽  
State Of The Art ◽  
Channel Selection ◽  
Mixed Integer ◽  
Electrode Placement ◽  
Quadratic Program ◽  
Neural Decoding ◽  
Selection Methods ◽  
Optimality Gap

AbstractChannel selection or electrode placement for neural decoding is a commonly encountered problem in electroencephalography (EEG). Since evaluating all possible channel combinations is usually infeasible, one usually has to settle for heuristic methods or convex approximations without optimality guarantees. To date, it remains unclear how large the gap is between the selection made by these approximate methods and the truly optimal selection. The goal of this paper is to quantify this optimality gap for several state-of-the-art channel selection methods in the context of least-squares based neural decoding. To this end, we reformulate the channel selection problem as a mixed-integer quadratic program (MIQP), which allows the use of efficient MIQP solvers to find the optimal channel combination in a feasible computation time for up to 100 candidate channels. As this reveals the exact solution to the combinatorial problem, it allows to quantify the performance losses when using state-of-the-art sub-optimal (yet faster) channel selection methods. In a context of auditory attention decoding, we find that a greedy channel selection based on the utility metric does not show a significant optimality gap compared to optimal channel selection, whereas other state-of-the-art greedy or l1-norm penalized methods do show a significant loss in performance. Furthermore, we demonstrate that the MIQP formulation also provides a natural way to incorporate topology constraints in the selection, e.g., for electrode placement in neuro-sensor networks with galvanic separation constraints. Furthermore, a combination of this utility-based greedy selection with an MIQP solver allows to perform a topology constrained electrode placement, even in large scale problems with more than 100 candidate positions.

scholarly journals A Study of Learning Search Approximation in Mixed Integer Branch and Bound: Node Selection in SCIP

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 150-178
Author(s):  
Kaan Yilmaz ◽  
Neil Yorke-Smith
Keyword(s):  
Machine Learning ◽  
Branch And Bound ◽  
State Of The Art ◽  
Heuristic Method ◽  
Imitation Learning ◽  
Mixed Integer ◽  
Node Selection ◽  
Selection Policy ◽  
State Of Practice ◽  
Optimality Gap

In line with the growing trend of using machine learning to help solve combinatorial optimisation problems, one promising idea is to improve node selection within a mixed integer programming (MIP) branch-and-bound tree by using a learned policy. Previous work using imitation learning indicates the feasibility of acquiring a node selection policy, by learning an adaptive node searching order. In contrast, our imitation learning policy is focused solely on learning which of a node’s children to select. We present an offline method to learn such a policy in two settings: one that comprises a heuristic by committing to pruning of nodes; one that is exact and backtracks from a leaf to guarantee finding the optimal integer solution. The former setting corresponds to a child selector during plunging, while the latter is akin to a diving heuristic. We apply the policy within the popular open-source solver SCIP, in both heuristic and exact settings. Empirical results on five MIP datasets indicate that our node selection policy leads to solutions significantly more quickly than the state-of-the-art precedent in the literature. While we do not beat the highly-optimised SCIP state-of-practice baseline node selector in terms of solving time on exact solutions, our heuristic policies have a consistently better optimality gap than all baselines, if the accuracy of the predictive model is sufficient. Further, the results also indicate that, when a time limit is applied, our heuristic method finds better solutions than all baselines in the majority of problems tested. We explain the results by showing that the learned policies have imitated the SCIP baseline, but without the latter’s early plunge abort. Our recommendation is that, despite the clear improvements over the literature, this kind of MIP child selector is better seen in a broader approach to using learning in MIP branch-and-bound tree decisions.

scholarly journals Nonlinear decoding of natural images from large-scale primate retinal ganglion recordings

2020 ◽  
Author(s):  
Young Joon Kim ◽  
Nora Brackbill ◽  
Ella Batty ◽  
JinHyung Lee ◽  
Catalin Mitelut ◽  
...  
Keyword(s):  
Visual Information ◽  
Large Scale ◽  
Ganglion Cells ◽  
State Of The Art ◽  
Fundamental Problem ◽  
The State ◽  
Natural Images ◽  
Retinal Ganglion ◽  
Neural Decoding ◽  
Sensory Stimuli

AbstractDecoding sensory stimuli from neural activity can provide insight into how the nervous system might interpret the physical environment, and facilitates the development of brain-machine interfaces. Nevertheless, the neural decoding problem remains a significant open challenge. Here, we present an efficient nonlinear decoding approach for inferring natural scene stimuli from the spiking activities of retinal ganglion cells (RGCs). Our approach uses neural networks to improve upon existing decoders in both accuracy and scalability. Trained and validated on real retinal spike data from > 1000 simultaneously recorded macaque RGC units, the decoder demonstrates the necessity of nonlinear computations for accurate decoding of the fine structures of visual stimuli. Specifically, high-pass spatial features of natural images can only be decoded using nonlinear techniques, while low-pass features can be extracted equally well by linear and nonlinear methods. Together, these results advance the state of the art in decoding natural stimuli from large populations of neurons.Author summaryNeural decoding is a fundamental problem in computational and statistical neuroscience. There is an enormous literature on this problem, applied to a wide variety of brain areas and nervous systems. Here we focus on the problem of decoding visual information from the retina. The bulk of previous work here has focused on simple linear decoders, applied to modest numbers of simultaneously recorded cells, to decode artificial stimuli. In contrast, here we develop a scalable nonlinear decoding method to decode natural images from the responses of over a thousand simultaneously recorded units, and show that this decoder significantly improves on the state of the art.

scholarly journals An Improved Niche Chaotic Genetic Algorithm for Low-Energy Clustering Problem in Large-Scale Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Min Tian ◽  
Jie Zhou ◽  
Xin Lv
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Large Scale ◽  
Low Energy ◽  
Wireless Sensor ◽  
Mixed Integer ◽  
Clustering Problem ◽  
Systems Simulation

Large-scale wireless sensor networks consist of a large number of tiny sensors that have sensing, computation, wireless communication, and free-infrastructure abilities. The low-energy clustering scheme is usually designed for large-scale wireless sensor networks to improve the communication energy efficiency. However, the low-energy clustering problem can be formulated as a nonlinear mixed integer combinatorial optimization problem. In this paper, we propose a low-energy clustering approach based on improved niche chaotic genetic algorithm (INCGA) for minimizing the communication energy consumption. We formulate our objective function to minimize the communication energy consumption under multiple constraints. Although suboptimal for LSWSN systems, simulation results show that the proposed INCGA algorithm allows to reduce the communication energy consumption with lower complexity compared to the QEA (quantum evolutionary algorithm) and PSO (particle swarm optimization) approaches.

Welfare-maximizing transmission capacity expansion under uncertainty

2021 ◽  
Vol 379 (2202) ◽  
pp. 20190436
Author(s):  
S. Wogrin ◽  
D. Tejada-Arango ◽  
A. Downward ◽  
A.B. Philpott
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Capacity Expansion ◽  
Computational Effort ◽  
Transmission Capacity ◽  
Mixed Integer ◽  
Deterministic Equivalent ◽  
Time Period ◽  
Expansion Plan ◽  
Programming Package

We apply the JuDGE optimization package to a multistage stochastic leader–follower model that determines a transmission capacity expansion plan to maximize expected social welfare of consumers and producers who act as Cournot oligopolists in each time period. The problem is formulated as a large-scale mixed integer programme and applied to a 5-bus instance over scenario trees of varying size. The computational effort required by JuDGE is compared with solving the deterministic equivalent mixed integer programme using a state-of-the-art integer programming package. This article is part of the theme issue ‘The mathematics of energy systems’.

scholarly journals Generalized neural decoders for transfer learning across participants and recording modalities

2020 ◽  
Author(s):  
Steven M. Peterson ◽  
Zoe Steine-Hanson ◽  
Nathan Davis ◽  
Rajesh P. N. Rao ◽  
Bingni W. Brunton
Keyword(s):  
State Of The Art ◽  
Spectral Power ◽  
Brain Regions ◽  
Fine Tuning ◽  
Electrode Placement ◽  
Neural Decoding ◽  
Computer Interfaces ◽  
Current State ◽  
Level Data ◽  
Minimal Data

AbstractObjectiveAdvances in neural decoding have enabled brain-computer interfaces to perform increasingly complex and clinically-relevant tasks. However, such decoders are often tailored to specific participants, days, and recording sites, limiting their practical long-term usage. Therefore, a fundamental challenge is to develop neural decoders that can robustly train on pooled, multi-participant data and generalize to new participants.ApproachWe introduce a new decoder, HTNet, which uses a convolutional neural network with two innovations: (1) a Hilbert transform that computes spectral power at data-driven frequencies and (2) a layer that projects electrode-level data onto predefined brain regions. The projection layer critically enables applications with intracranial electrocorticography (ECoG), where electrode locations are not standardized and vary widely across participants. We trained HTNet to decode arm movements using pooled ECoG data from 11 of 12 participants and tested performance on unseen ECoG or electroencephalography (EEG) participants; these pretrained models were also subsequently fine-tuned to each test participant.Main resultsHTNet outperformed state-of-the-art decoders when tested on unseen participants, even when a different recording modality was used. By fine-tuning these generalized HTNet decoders, we achieved performance approaching the best tailored decoders with as few as 50 ECoG or 20 EEG events. We were also able to interpret HTNet’s trained weights and demonstrate its ability to extract physiologically-relevant features.SignificanceBy generalizing to new participants and recording modalities, robustly handling variations in electrode placement, and allowing participant-specific fine-tuning with minimal data, HTNet is applicable across a broader range of neural decoding applications compared to current state-of-the-art decoders.

Optimal Test Assembly in Practice

2013 ◽  
Vol 221 (3) ◽  
pp. 190-200 ◽  
Author(s):  
Jörg-Tobias Kuhn ◽  
Thomas Kiefer
Keyword(s):  
Particle Physics ◽  
Large Scale ◽  
Block Design ◽  
Linear Optimization ◽  
Mixed Integer ◽  
Optimal Test ◽  
Automated Test Assembly ◽  
Test Assembly ◽  
Block Level ◽  
Mixed Integer Linear Optimization

Several techniques have been developed in recent years to generate optimal large-scale assessments (LSAs) of student achievement. These techniques often represent a blend of procedures from such diverse fields as experimental design, combinatorial optimization, particle physics, or neural networks. However, despite the theoretical advances in the field, there still exists a surprising scarcity of well-documented test designs in which all factors that have guided design decisions are explicitly and clearly communicated. This paper therefore has two goals. First, a brief summary of relevant key terms, as well as experimental designs and automated test assembly routines in LSA, is given. Second, conceptual and methodological steps in designing the assessment of the Austrian educational standards in mathematics are described in detail. The test design was generated using a two-step procedure, starting at the item block level and continuing at the item level. Initially, a partially balanced incomplete item block design was generated using simulated annealing, whereas in a second step, items were assigned to the item blocks using mixed-integer linear optimization in combination with a shadow-test approach.

A Novel Static Path Planning Method for Mobile Anchor-Assisted Localization in Wireless Sensor Networks

2020 ◽  
Vol 10 ◽  
Author(s):  
Abdelhady M. Naguib ◽  
Shahzad Ali
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Path Planning ◽  
Sensor Node ◽  
Large Scale ◽  
Performance Metrics ◽  
Wireless Sensor ◽  
Anchor Node ◽  
Mobile Anchor ◽  
Planning Methods

Background: Many applications of Wireless Sensor Networks (WSNs) require awareness of sensor node’s location but not every sensor node can be equipped with a GPS receiver for localization, due to cost and energy constraints especially for large-scale networks. For localization, many algorithms have been proposed to enable a sensor node to be able to determine its location by utilizing a small number of special nodes called anchors that are equipped with GPS receivers. In recent years a promising method that significantly reduces the cost is to replace the set of statically deployed GPS anchors with one mobile anchor node equipped with a GPS unit that moves to cover the entire network. Objectives: This paper proposes a novel static path planning mechanism that enables a single anchor node to follow a predefined static path while periodically broadcasting its current location coordinates to the nearby sensors. This new path type is called SQUARE_SPIRAL and it is specifically designed to reduce the collinearity during localization. Results: Simulation results show that the performance of SQUARE_SPIRAL mechanism is better than other static path planning methods with respect to multiple performance metrics. Conclusion: This work includes an extensive comparative study of the existing static path planning methods then presents a comparison of the proposed mechanism with existing solutions by doing extensive simulations in NS-2.

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