Electroencephalography

Electroencephalography (EEG) is a method to record an electrogram of the spontaneous electrical activity of the brain. The biosignals detected by EEG have been shown to represent the postsynaptic potentials of pyramidal neurons in the neocortex and allocortex. It is typically non-invasive, with the EEG electrodes placed along the scalp (commonly called "scalp EEG") using the International 10–20 system, or variations of it. Electrocorticography, involving surgical placement of electrodes, is sometimes called "intracranial EEG". Clinical interpretation of EEG recordings is most often performed by visual inspection of the tracing or quantitative EEG analysis. Voltage fluctuations measured by the EEG bioamplifier and electrodes allow the evaluation of normal brain activity. As the electrical activity monitored by EEG originates in neurons in the underlying brain tissue, the recordings made by the electrodes on the surface of the scalp vary in accordance with their orientation and distance

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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

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

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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An epileptic seizure, informally known as a seizure, is a period of symptoms due to abnormally excessive or synchronous neuronal activity in the brain. Outward effects vary from uncontrolled shaking

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