Électroencéphalographie

L'électroencéphalographie (EEG) est une méthode d'exploration cérébrale qui mesure l'activité électrique du cerveau par des électrodes placées sur le cuir chevelu souvent représentée sous la forme d'un tracé appelé électroencéphalogramme. Comparable à l'électrocardiogramme qui permet d'étudier le fonctionnement du cœur, l'EEG est un examen indolore et non invasif qui renseigne sur l'activité neurophysiologique du cerveau au cours du temps et en particulier du cortex cérébral soit dans un but diagnostique en neurologie, soit dans la recherche en neurosciences cognitives. Le signal électrique à l'origine de l'EEG est la résultante de la sommation des potentiels post-synaptiques synchrones issus d'un grand nombre de neurones. On parle aussi d'électroencéphalographie intracrânienne (iEEG), sous-durale ou stéréotaxique (sEEG) pour désigner des mesures de l'activité électrique du cerveau effectuées à partir d'électrodes implantées sous la surface du crâne, soit à la surface soit en profonde

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BIOENG-448: Fundamentals of neuroengineering

Neuroengineering is at the frontier between neuroscience and engineering: understanding how the brain works allows developing engineering applications and therapies of high impact, while the design of new measurement and data analysis techniques contributes to advance our knowledge about the brain.

MICRO-513: Signal processing for functional brain imaging

Computational methods for the analysis of human brain imaging data

EE-594: Smart sensors for IoT

This lecture provides insights in the design and technologies of Internet-of-Things sensor nodes, with focus on low power technologies. The lectures alternate every two weeks between sensing technologies of various kinds (prof. Ionescu) and their integrated circuit readouts (prof. Enz).

Épilepsie

L', aussi appelée mal comitial, est une affection neurologique qui touche plus de 50 millions de personnes à travers le monde. Elle est définie comme une famille de maladies dont le point commun es

Crise d'épilepsie

Une crise d'épilepsie ou crise épileptique est caractérisée par divers symptômes dus à une activité neuronale anormalement excessive ou synchrone dans le cerveau. Les effets extérieurs varient de m

Rythme cérébral

Un rythme cérébral (appelé aussi activité neuro-électrique) désigne l'oscillation électromagnétique émise par le cerveau des êtres humains, mais également de tout être vivant. Le cortex frontal qui

Studying Phase Synchrony for Classification of Mental Tasks in Brain Machine Interfaces

Silvia Chiappa

Electroencephalogram recordings during imagination of mental tasks allow for developing a new communication device for, e.g., motor disabled people. 32-channel EEG was recorded from 5 healthy subjects while performing, after instruction and in random order, repetitive left-hand movement imagination, right-hand movement imagination and word generation. In 3 consecutive days, 5 sessions of 4 minutes were acquired, without feedback. Extracted features were based on the Phase Locking Value (PLV), coherence, and the spectral power in the a-, b1-, b2- and 8-30Hz frequency bands. Different feature subsets were considered for classification. The classifier consisted of a combination of Support Vector Machines (SVMs), allowing for classifying the 3 mental tasks. Results were obtained from 5-fold crossvalidation. We analyzed (offline) data from the last day of recording, when subjects had a training experience of only 40 minutes from the two previous days. Selecting for every subject the best feature subset, often a combination of PLV and power features, we obtained for two subjects correct classification rates of 67.81% and 62.58%, distinguishing the 3 mental tasks. For the other subjects, classification rates were 52.77%, 44.66%, and 41.24% only, maybe due to not enough practice. The involvement of the motor cortex manifested itself by a good performance of the features extracted from electrodes located in the centro-parietal region. Sole use of PLV or coherence features yielded good classification, proving phase synchronization appropriate for classifying mental tasks. This shows the importance of studying functional relations between brain regions for further improvement of Brain-Machine Interfaces.

2003

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Relevant EEG Features for the Classification of Spontaneous Motor-Related Tasks

There is a growing interest in the use of physiological signals for communication and operation of devices for the severely motor disabled as well as for healthy people. A few groups around the world have developed brain-computer interfaces (BCIs) that rely upon the recognition of motor-related tasks (i.e.,imagination of movements) from on-line EEG signals. In this paper we seek to find and analyze the set of relevant EEG features that best differentiate spontaneous motorrelated mental tasks from each other. This study empirically demonstrates the benefits of heuristic feature selection methods for EEG-based classification of mental tasks. In particular,it is shown that the classifier performance improves for all the considered subjects with only a small proportion of features. Thus,the use of just those relevant features increases the efficiency of the brain interfaces and,most importantly,enables a greater level of adaptation of the personal BCI to the individual user.

Springer Verlag2002

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The EEG multiverse of schizophrenia

Ophélie Gladys Favrod, Patricia Figueiredo, Dario Alejandro Gordillo Lopez, Michael Herzog, Wei-Hsiang Lin, Janir Nuno Ramos Antunes Da Cruz, Maya Roinishvili

Research on schizophrenia typically focuses on one paradigm for which clear-cut differences between patients and controls are established. Great efforts are made to understand the underlying genetical, neurophysiological, and cognitive mechanisms, which eventually may explain the clinical outcome. One tacit assumption of these "deep rooting" approaches is that paradigms tap into common and representative aspects of the disorder. Here, we analyzed the resting-state electroencephalogram (EEG) of 121 schizophrenia patients and 75 controls. Using multiple signal processing methods, we extracted 194 EEG features. Sixty-nine out of the 194 EEG features showed a significant difference between patients and controls, indicating that these features detect an important aspect of schizophrenia. Surprisingly, the correlations between these features were very low. We discuss several explanations to our results and propose that complementing "deep" with "shallow" rooting approaches might help in understanding the underlying mechanisms of the disorder.

OXFORD UNIV PRESS INC2022

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