8 research outputs found
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks
A change of the prevalent supervised learning techniques is foreseeable in
the near future: from the complex, computational expensive algorithms to more
flexible and elementary training ones. The strong revitalization of randomized
algorithms can be framed in this prospect steering. We recently proposed a
model for distributed classification based on randomized neural networks and
hyperdimensional computing, which takes into account cost of information
exchange between agents using compression. The use of compression is important
as it addresses the issues related to the communication bottleneck, however,
the original approach is rigid in the way the compression is used. Therefore,
in this work, we propose a more flexible approach to compression and compare it
to conventional compression algorithms, dimensionality reduction, and
quantization techniques.Comment: 12 pages, 3 figure
Optimized Biosignals Processing Algorithms for New Designs of Human Machine Interfaces on Parallel Ultra-Low Power Architectures
The aim of this dissertation is to explore Human Machine Interfaces (HMIs) in a variety of biomedical scenarios. The research addresses typical challenges in wearable and implantable devices for diagnostic, monitoring, and prosthetic purposes, suggesting a methodology for tailoring such applications to cutting edge embedded architectures.
The main challenge is the enhancement of high-level applications, also introducing Machine Learning (ML) algorithms, using parallel programming and specialized hardware to improve the performance.
The majority of these algorithms are computationally intensive, posing significant challenges for the deployment on embedded devices, which have several limitations in term of memory size, maximum operative frequency, and battery duration.
The proposed solutions take advantage of a Parallel Ultra-Low Power (PULP) architecture, enhancing the elaboration on specific target architectures, heavily optimizing the execution, exploiting software and hardware resources.
The thesis starts by describing a methodology that can be considered a guideline to efficiently implement algorithms on embedded architectures.
This is followed by several case studies in the biomedical field, starting with the analysis of a Hand Gesture Recognition, based on the Hyperdimensional Computing algorithm, which allows performing a fast on-chip re-training, and a comparison with the state-of-the-art Support Vector Machine (SVM); then a Brain Machine Interface (BCI) to detect the respond of the brain to a visual stimulus follows in the manuscript. Furthermore, a seizure detection application is also presented, exploring different solutions for the dimensionality reduction of the input signals. The last part is dedicated to an exploration of typical modules for the development of optimized ECG-based applications
Brain-Computer Interfaces using Electrocorticography and Surface Stimulation
The brain connects to, modulates, and receives information from every organ in the body. As such, brain-computer interfaces (BCIs) have vast potential for diagnostics, medical therapies, and even augmentation or enhancement of normal functions. BCIs provide a means to explore the furthest corners of what it means to think, to feel, and to act—to experience the world and to be who you are. This work focuses on the development of a chronic bi-directional BCI for sensorimotor restoration through the use of separable frequency bands for recording motor intent and providing sensory feedback via electrocortical stimulation. Epidural cortical surface electrodes are used to both record electrocorticographic (ECoG) signals and provide stimulation without adverse effects associated with penetration through the protective dural barrier of brain. Chronic changes in electrode properties and signal characteristics are discussed, which inform optimal electrode designs and co-adaptive algorithms for decoding high-dimensional information. Additionally, a multi-layered approach to artifact suppression is presented, which includes a systems-level design of electronics, signal processing, and stimulus waveforms. The results of this work are relevant to a wider range of applications beyond ECoG and BCIs that involve closed-loop recording and stimulation throughout the body. By enabling simultaneous recording and stimulation through the techniques described here, responsive therapies can be developed that are tuned to individual patients and provide precision therapies at exactly the right place and time. This has the potential to improve targeted therapeutic outcomes while reducing undesirable side effects
Compressing Subject-specific Brain-Computer Interface Models into One Model by Superposition in Hyperdimensional Space
Accurate multiclass classification of electroencephalography (EEG) signals is still a challenging task towards the development of reliable motor imagery brain-computer interfaces (MI-BCIs). Deep learning algorithms have been recently used in this area to deliver a compact and accurate model. Reaching high-level of accuracy requires to store subjects-specific trained models that cannot be achieved with an otherwise compact model trained globally across all subjects. In this paper, we propose a new methodology that closes the gap between these two extreme modeling approaches: we reduce the overall storage requirements by superimposing many subject-specific models into one single model such that it can be reliably decomposed, after retraining, to its constituent models while providing a trade-off between compression ratio and accuracy. Our method makes the use of unexploited capacity of trained models by orthogonalizing parameters in a hyperdimensional space, followed by iterative retraining to compensate noisy decomposition. This method can be applied to various layers of deep inference models. Experimental results on the 4-class BCI competition IV-2a dataset show that our method exploits unutilized capacity for compression and surpasses the accuracy of two state-of-the-art networks: (1) it compresses the smallest network, EEGNet [1], by 1.9×, and increases its accuracy by 2.41% (74.73% vs. 72.32%); (2) using a relatively larger Shallow ConvNet [2], our method achieves 2.95 x compression as well as 1.4% higher accuracy (75.05% vs. 73.59%). © 2020 EDAA
Compressing Subject-specific Brain-Computer Interface Models into One Model by Superposition in Hyperdimensional Space
Accurate multiclass classification of electroencephalography (EEG) signals is still a challenging task towards the development of reliable motor imagery brain-computer interfaces (MI-BCIs). Deep learning algorithms have been recently used in this area to deliver a compact and accurate model. Reaching high-level of accuracy requires to store subjects-specific trained models that cannot be achieved with an otherwise compact model trained globally across all subjects. In this paper, we propose a new methodology that closes the gap between these two extreme modeling approaches: we reduce the overall storage requirements by superimposing many subject-specific models into one single model such that it can be reliably decomposed, after retraining, to its constituent models while providing a trade-off between compression ratio and accuracy. Our method makes the use of unexploited capacity of trained models by orthogonalizing parameters in a hyperdimensional space, followed by iterative retraining to compensate noisy decomposition. This method can be applied to various layers of deep inference models. Experimental results on the 4-class BCI competition IV-2a dataset show that our method exploits unutilized capacity for compression and surpasses the accuracy of two state-of-the-art networks: (1) it compresses the smallest network, EEGNet [1], by 1.9
7, and increases its accuracy by 2.41% (74.73% vs. 72.32%); (2) using a relatively larger Shallow ConvNet [2], our method achieves 2.95 x compression as well as 1.4% higher accuracy (75.05% vs. 73.59%)
From sequences to cognitive structures : neurocomputational mechanisms
Ph. D. Thesis.Understanding how the brain forms representations of structured information distributed in time is
a challenging neuroscientific endeavour, necessitating computationally and neurobiologically
informed study. Human neuroimaging evidence demonstrates engagement of a fronto-temporal
network, including ventrolateral prefrontal cortex (vlPFC), during language comprehension.
Corresponding regions are engaged when processing dependencies between word-like items in
Artificial Grammar (AG) paradigms. However, the neurocomputations supporting dependency
processing and sequential structure-building are poorly understood. This work aimed to clarify these
processes in humans, integrating behavioural, electrophysiological and computational evidence.
I devised a novel auditory AG task to assess simultaneous learning of dependencies between adjacent
and non-adjacent items, incorporating learning aids including prosody, feedback, delineated
sequence boundaries, staged pre-exposure, and variable intervening items. Behavioural data obtained
in 50 healthy adults revealed strongly bimodal performance despite these cues. Notably, however,
reaction times revealed sensitivity to the grammar even in low performers. Behavioural and
intracranial electrode data was subsequently obtained in 12 neurosurgical patients performing this
task. Despite chance behavioural performance, time- and time-frequency domain
electrophysiological analysis revealed selective responsiveness to sequence grammaticality in regions
including vlPFC. I developed a novel neurocomputational model (VS-BIND: “Vector-symbolic
Sequencing of Binding INstantiating Dependencies”), triangulating evidence to clarify putative
mechanisms in the fronto-temporal language network. I then undertook multivariate analyses on the
AG task neural data, revealing responses compatible with the presence of ordinal codes in vlPFC,
consistent with VS-BIND. I also developed a novel method of causal analysis on multivariate
patterns, representational Granger causality, capable of detecting flow of distinct representations
within the brain. This alluded to top-down transmission of syntactic predictions during the AG task,
from vlPFC to auditory cortex, largely in the opposite direction to stimulus encodings, consistent
with predictive coding accounts. It finally suggested roles for the temporoparietal junction and
frontal operculum during grammaticality processing, congruent with prior literature.
This work provides novel insights into the neurocomputational basis of cognitive structure-building,
generating hypotheses for future study, and potentially contributing to AI and translational efforts.Wellcome
Trust, European Research Counci
Harmonic duality : from interval ratios and pitch distance to spectra and sensory dissonance
Dissonance curves are the starting
point for an investigation into a psychoacoustically informed harmony.
Its main hypothesis is that harmony consists of two independent but
intertwined aspects operating simultaneously, namely proportionality and
linear pitch distance. The former aspect is related to intervallic
characters, the latter to ‘high’, ‘low’, ‘bright’ and ‘dark’, therefore
to timbre. This research derives from the development of tools for
algorithmic composition which extract pitch materials from sound
signals, analyzing them according to their timbral and harmonic
properties, putting them into motion through diverse rhythmic and
textural procedures. The tools and the reflections derived from their
use offer fertile ideas for the generation of instrumental scores,
electroacoustic soundscapes and interactive live-electronic systems.LEI Universiteit LeidenResearch in and through artistic practic
Summaries of the Third Annual JPL Airborne Geoscience Workshop. Volume 1: AVIRIS Workshop
This publication contains the preliminary agenda and summaries for the Third Annual JPL Airborne Geoscience Workshop, held at the Jet Propulsion Laboratory, Pasadena, California, on 1-5 June 1992. This main workshop is divided into three smaller workshops as follows: (1) the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) workshop, on June 1 and 2; (2) the Thermal Infrared Multispectral Scanner (TIMS) workshop, on June 3; and (3) the Airborne Synthetic Aperture Radar (AIRSAR) workshop, on June 4 and 5. The summaries are contained in Volumes 1, 2, and 3, respectively