The international 10–20 system revisited: Cartesian and spherical co-ordinates

Schizophrenia falls into the small category of diseases that impair the total psychic activity rather than particular brain systems and functions. It is not surprising that researchers have long been interested in the integrative activity of the human brain in schizophrenia. They have reported considerable data on histological and physiological changes in the human brain, providing evidence for disturbance of interrelationships and functional association between different parts of the brain at different stages of schizophrenia [1][2][3]. The most conspicuous data have been obtained for the brain electrical activity [4][5][6][7][8][9][10]. Based on these data, a hypothesis of disintegration of cortical functions (the disconnection hypothesis) has been advanced [11] to explain the schizophrenic disorders [11][12][13].In EEG studies, spectral and correlation analyses are a common method for investigating the integrative activity of the human brain, yielding evidence for the impairment of local and distant synchronies of neuronal networks in schizophrenia [4,5,7,8,14]. However, a number of limitations typical of spectral methods, specifically, of the coherence function [7,[15][16][17], have motivated the development of new techniques to examine the interdependences of EEG paired time series data in schizophrenia, including nonlinear interdependence [9], mutual information transmission measure [18], and phase locking [10], which reflect nonlinear and, in the last case, also in-phase components of the interdependence of cortical electrical processes.The results of the above studies are also in line with Friston's hypothesis of disintegration of neuronal networks in schizophrenia [11].Yet, cortical bioelectrical processes associated with ontological nonstationarity of the EEG signal [19][20][21] are not covered by the traditional or new methods of quantitative analysis of EEG spatiotemporal correlations.EEG nonstationarity implies that the EEG signal consists of quasi-stationary segments that reflect the changes in metastable states of the brain on different time scales [20,21], from microstates, with a duration of no more than several seconds [15,22], to macrostates, with a duration of tens or hundreds of minutes [23]. This concept of EEG nonstationarity provides a means for obtaining new insights into the cooperation of cortical structures. For this purpose, it is possible to estimate the EEG structural synchrony [15], i.e., the temporal synchronization of intersegmentary transitions between different EEG channels. Estimation of the spatiotemporal synchronization of local metastable states of neuronal networks appears to be a new measure of the integrative activity of the human brain.The functional importance of the EEG structural synchrony and segment characteristics has been described in a series of our works performed in several laboratories and with several cognitive and pharmacological paradigms [24][25][26][27]. In our previous work [28], changes in quasi-stationary segments of the EEG α activity were detected i...

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“…3a-3c, respectively). The interelectrode distances were calculated using three-dimensional coordinates for each of the derivations [30,31].…”

Section: Resultsmentioning

confidence: 99%

Analysis of EEG Structural Synchrony in Adolescents with Schizophrenic Disorders

et al. 2005

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“…3. All the electrodes are placed over the entire scalp using International standard 10-10 system (Bocker et al, 1994). The reduced sets of 24 channels are highlighted in Fig.…”

Section: Eeg Data Acquisitionmentioning

confidence: 99%

Discrete Wavelet Transform Based Classification of Human Emotions Using Electroencephalogram Signals

Rizon¹

2010

American Journal of Applied Sciences

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Problem statement:The aim of this study was to report the human emotion assessment using Electroencephalogram (EEG). Approach: An audio-visual induction based protocol was designed for inducing five different emotions (happy, surprise, fear, disgust and neutral) on 20 subjects in the age group of 19~39 years. EEG signals are recorded from 64 channels placed over entire scalp according to International 10-10 system. We firstly applied Spatial Filtering technique to remove the noises and artifacts from the EEG signals. Three wavelet functions ("db8", "sym8" and "coif5") were used to decompose the EEG signal into five different frequency bands namely: delta, theta, alpha, beta and gamma. A set of new statistical features related to energy were extracted from the EEG frequency bands to construct the feature vector for classifying the emotions. Two simple linear classifiers (K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA)) were used for mapping the feature vector into corresponding emotions. Furthermore, we compared the efficacy of emotion classification with a reduced set of channels (24 channels) for evaluating the reliability of the emotion recognition system. Results: In this study, 62 channels outperform 24 channels by giving the maximum average classification accuracy of 79.65% using KNN and 78.52% using LDA. Conclusion: In this study we presented an approach to discrete emotion recognition based on the processing of EEG signals. The preliminary results resented in this study address the classifiability of human emotions using original and reduced set of EEG channels. The results presented in this study indicated that, statistical features extracted from time-frequency analysis (wavelet transform) works well in the context of discrete emotion classification.

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“…Then, was normalized by re-signing it with . Next, each pair was utilized in (8) to compute a 20-element vector at scalp locations defined by the international 10-20 system [22].…”

Section: Pattern Generationmentioning

confidence: 99%

The forward EEG solutions can be computed using artificial neural networks

Sun

Sclabassi

2000

IEEE Trans. Biomed. Eng.

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Abstract-Study of electroencenphalogarphy (EEG) is the one of the most utilized methods in both basic brain research and clinical diagnosis of neurological disorders. Recent technological advances in computer and electronic systems have allowed the EEG to be recorded from large electrode arrays. Modeling the brain waves using a head volume conductor model provides an effective method to localize functional generators within the brain. However, the forward solutions to this model, which represent theoretical potentials in response to current sources within the volume conductor, are difficult to compute because of time-consuming numerical procedures utilized in either the boundary element method (BEM) or the finite element method (FEM). This paper presents a novel computational approach using an artificial neural network (ANN) to map two vectors of forward solutions. These two vectors correspond to different head models but with respect to the same current source. The input vector to the ANN is based on the spherical head model, which can be computed efficiently but involves large errors. The output vector from the ANN is based on the spheroidal model, which is more precise, but difficult to compute directly using the traditional means. Our experiments indicate that this ANN approach provides a remarkable improvement over the BEM and FEM methods: 1) the mean-square error of computation was only approximately 0.3% compared to the exact solution; 2) the online computation was extremely efficient, requiring only 168 floating point operations per channel to compute the forward solution, and 10.2 K-bytes of storage to represent the entire ANN. Using this approach it is possible to perform real-time EEG modeling accurately on personal computers.

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The international 10–20 system revisited: Cartesian and spherical co-ordinates

Cited by 39 publications

References 8 publications

Analysis of EEG Structural Synchrony in Adolescents with Schizophrenic Disorders

Analysis of EEG Structural Synchrony in Adolescents with Schizophrenic Disorders

Discrete Wavelet Transform Based Classification of Human Emotions Using Electroencephalogram Signals

The forward EEG solutions can be computed using artificial neural networks

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