Inverse Solutions for Brain Computer Interface

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Artificial Intelligence has revolutionized numerous fields, driving advancements in healthcare, finance, and autonomous systems. At the core of this revolution lies Deep Learning---algorithms that learn representations from data through computational layer ...

"Soft"muons with a transverse momentum below 10 GeV are featured in many processes studied by the CMS experiment, such as decays of heavy-flavor hadrons or rare tau lepton decays. Maximizing the selection efficiency for these muons, while simultaneously su ...

The electronic density of states (DOS) provides information regarding the distribution of electronic energy levels in a material, and can be used to approximate its optical and electronic properties and therefore guide computational materials design. Given ...

Deep reinforcement learning (DRL) is a powerful method for local motion planning in automated driving. However, training of DRL agents is difficult and subject to instability. We propose Regularized Adversarial Fine-Tuning (RAFT), an adversarial DRL traini ...

Objective. A key challenge of virtual reality (VR) applications is to maintain a reliable human-avatar mapping. Users may lose the sense of controlling (sense of agency), owning (sense of body ownership), or being located (sense of self-location) inside th ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

The rapid development of wearable biomedical systems now enables real-time monitoring of electroencephalography (EEG) signals. Acquisition of these signals relies on electrodes. These systems must meet the design challenge of selecting an optimal set of el ...

Traditionally, studies in schizophrenia research employ a single experimental paradigm. The results typically demonstrate a significant difference between patients and controls. Subsequent studies aim to describe the underlying abnormal mechanism at the ge ...

In this research paper, we conducted a study to investigate the connection between three objective measures: Electrocardiogram(ECG), Electrogastrogram (EGG), and Electroencephalogram (EEG), and individuals' susceptibility to cybersickness. Our primary obje ...

Neuroprostheses have been used clinically for decades, to help restore or preserve brain functions, when pharmaceutical treatments are inefficient. Although great progress in the field has been made over the years to interface with the nervous system, surf ...

This paper studies kernel ridge regression in high dimensions under covariate shifts and analyzes the role of importance re-weighting. We first derive the asymptotic expansion of high dimensional kernels under covariate shifts. By a bias-variance decomposi ...

Recent research shows prominent effects of pregnancy and the parenthood transition on structural brain characteristics in humans. Here, we present a comprehensive study of how parental status and number of children born/fathered links to markers of brain a ...

Extracting maximal information from experimental data requires access to the likelihood function, which however is never directly available for complex experiments like those performed at high energy colliders. Theoretical predictions are obtained in this ...

Epilepsy, a major neurological disease, requires careful diagnosis and treatment. However, the detection of epileptic seizures remains a significant challenge. Current clinical practice relies on expert analysis of EEG signals, a process that is time-consu ...

Catastrophic overfitting (CO) in single-step adversarial training (AT) results in abrupt drops in the adversarial test accuracy (even down to 0%). For models trained with multi-step AT, it has been observed that the loss function behaves locally linearly w ...

A key challenge across many disciplines is to extract meaningful information from data which is often obscured by noise. These datasets are typically represented as large matrices. Given the current trend of ever-increasing data volumes, with datasets grow ...

Recently, cutting-edge brain-machine interfaces (BMIs) have revealed the potential of decoders such as recurrent neural networks (RNNs) in predicting attempted handwriting [1] or speech [2], enabling rapid communication recovery after paralysis. However, c ...

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Inverse Solutions for Brain Computer Interface

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How data structures affect generalization in Kernel Methods and Deep Learning

Identification of low-momentum muons in the CMS detector using multivariate techniques in proton-proton collisions at √s = 13.6 TeV

Adaptive energy reference for machine-learning models of the electronic density of states

RAFT: Regularized Adversarial Fine-Tuning to Enhance Deep Reinforcement Learning for Self-Parking

Customizing the human-avatar mapping based on EEG error related potentials during avatar-based interaction

Towards Trustworthy Deep Learning for Image Reconstruction

Understanding generalization and robustness in modern deep learning

FETCH: A Fast and Efficient Technique for Channel Selection in EEG Wearable Systems

Variability is the rule: Neurophysiology and contextual visual processing in schizophrenia

Who says you are so sick? An investigation on individual susceptibility to cybersickness triggers using EEG, EGG and ECG

Unraveling behavior and cortical signals to guide the development of soft neuroprostheses for auditory restoration and spreading depolarization

High-Dimensional Kernel Methods under Covariate Shift: Data-Dependent Implicit Regularization

Parental status and markers of brain and cellular age: A 3D convolutional network and classification study

Boosting likelihood learning with event reweighting

Resource-Efficient Continual Learning for Personalized Online Seizure Detection

Efficient local linearity regularization to overcome catastrophic overfitting

Spectral Estimators for High-Dimensional Matrix Inference

33.3 MiBMI: A 192/512-Channel 2.46mm² Miniaturized Brain-Machine Interface Chipset Enabling 31-Class Brain-to-Text Conversion Through Distinctive Neural Codes

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse