Publications associées à Adaptive Filtering Methods for Identifying Cross-Frequency Couplings in Human EEG

Publications associées (29)

Subcortical correlates of consciousness with human single neuron recordings

Olaf Blanke, Fosco Bernasconi, Nathan Quentin Faivre, Michael Eric Anthony Pereira

Subcortical brain structures such as the basal ganglia or the thalamus are involved in regulating motor and cognitive behavior. However, their contribution to perceptual consciousness is still unclear, due to the inherent difficulties of recording subcorti ...

2024

Small-scale robotic devices for medical interventions in the brain

Mahmut Selman Sakar, Lorenzo Francesco John Noseda

This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...

2024

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Zenodo2024

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

Dario Alejandro Gordillo Lopez

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 ...

EPFL2024

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

Ronan Boulic, Nana Tian

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 ...

2024

An immersive virtual reality tool for assessing left and right unilateral spatial neglect

Olaf Blanke, Andrea Serino, Roberta Ronchi

The reported rate of the occurrence of unilateral spatial neglect (USN) is highly variable likely due to the lack of validity and low sensitivity of classical tools used to assess it. Virtual reality (VR) assessments try to overcome these limitations by pr ...

Wiley2024

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

Friedhelm Christoph Hummel, Takuya Morishita, Maximilian Jonas Wessel, Elena Beanato, Traian Popa, Pierre Theopistos Vassiliadis, Fabienne Windel, Esra Neufeld

Reinforcement feedback can improve motor learning, but the underlying brain mechanisms remain underexplored. In particular, the causal contribution of specific patterns of oscillatory activity within the human striatum is unknown. To address this question, ...

Nature Portfolio2024

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

Silvestro Micera

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, ...

Ieee-Inst Electrical Electronics Engineers Inc2024

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

Emilie Cornelia Maria Revol

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 ...

EPFL2024

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

Mahsa Shoaran, Uisub Shin, Mohammad Ali Shaeri, Amitabh Yadav

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 ...

IEEE2024

Driving and suppressing the human language network using large language models

Martin Schrimpf

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Nature Portfolio2024

Integrated Wireless Power, Data Communication, and Thermal Sensing System for Autonomous Multisite Brain Implants

Mohammad Javad Karimi

Implanted medical devices (IMDs) have been widely developed to support the monitoring and recording of biological data inside the body or brain. Wirelessly powered IMDs, a subset of implantable electronics, have been proposed to eliminate the limitations r ...

EPFL2024

Modulation of Visually Induced Self-motion Illusions by α Transcranial Electric Stimulation over the Superior Parietal Cortex

Sylvain Jean-François Harquel

The growing popularity of virtual reality systems has led to a renewed interest in understanding the neurophysiological correlates of the illusion of self-motion (vection), a phenomenon that can be both intentionally induced or avoided in such systems, dep ...

Mit Press2024

Modeling, fabrication and validation of 3D neural interfaces for peripheral nerves and brain organoids

Outman Akouissi

This thesis presents an extensive exploration of neuroelectronic interfaces, focusing on microfabrication, in silico modeling, and their applications in designing and fabricating devices for neural interfacing. The research encompasses both peripheral nerv ...

EPFL2024

Fast adaptation to rule switching using neuronal surprise

Wulfram Gerstner, Martin Louis Lucien Rémy Barry

In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signa ...

2024

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

David Atienza Alonso, Amir Aminifar, José Angel Miranda Calero, Alireza Amirshahi, Jonathan Dan

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 ...

2024

Unveiling the complexity of learning and decision-making

Wei-Hsiang Lin

Reinforcement learning (RL) is crucial for learning to adapt to new environments. In RL, the prediction error is an important component that compares the expected and actual rewards. Dopamine plays a critical role in encoding these prediction errors. In my ...

EPFL2024

Non-invasive neuromodulation of the right temporoparietal junction using theta-burst stimulation in functional neurological disorder

Serafeim Loukas

Background Disrupted sense of agency (SoA)-the sense of being the agent of one's own actions-has been demonstrated in patients with functional neurological disorder (FND), and a key area of the corresponding neuronal network is the right temporoparietal ju ...

Bmj Publishing Group2024

Decoding electroencephalographic responses to visual stimuli compatible with electrical stimulation

Silvestro Micera, Laura Toni, Fiorenzo Artoni, Simone Romeni

Electrical stimulation of the visual nervous system could improve the quality of life of patients affected by acquired blindness by restoring some visual sensations, but requires careful optimization of stimulation parameters to produce useful perceptions. ...

Aip Publishing2024

Biophysically accurate and machine learning-based surrogate models to optimize neuroprosthesis design and operation

Simone Romeni

Electrical stimulation of the nervous system has emerged as a promising assistive technology in case of many injuries and illnesses across various parts of the nervous system. In particular, the invasive neuromodulation of the peripheral nervous system see ...

EPFL2024