Multiple channel direct digital recording of EEG data

1971 ◽  
Vol 16 (3) ◽  
pp. 239-243 ◽  
Author(s):  
Duilio Giannitrapani ◽  
Villard T. Rast ◽  
Burton J. Shulhafer
Author(s):  
W. Kunath ◽  
E. Zeitler ◽  
M. Kessel

The features of digital recording of a continuous series (movie) of singleelectron TV frames are reported. The technique is used to investigate structural changes in negatively stained glutamine synthetase molecules (GS) during electron irradiation and, as an ultimate goal, to look for the molecules' “undamaged” structure, say, after a 1 e/Å2 dose.The TV frame of fig. la shows an image of 5 glutamine synthetase molecules exposed to 1/150 e/Å2. Every single electron is recorded as a unit signal in a 256 ×256 field. The extremely low exposure of a single TV frame as dictated by the single-electron recording device including the electron microscope requires accumulation of 150 TV frames into one frame (fig. lb) thus achieving a reasonable compromise between the conflicting aspects of exposure time per frame of 3 sec. vs. object drift of less than 1 Å, and exposure per frame of 1 e/Å2 vs. rate of structural damage.


2016 ◽  
Vol 30 (3) ◽  
pp. 102-113 ◽  
Author(s):  
Chun-Hao Wang ◽  
Chun-Ming Shih ◽  
Chia-Liang Tsai

Abstract. This study aimed to assess whether brain potentials have significant influences on the relationship between aerobic fitness and cognition. Behavioral and electroencephalographic (EEG) data was collected from 48 young adults when performing a Posner task. Higher aerobic fitness is related to faster reaction times (RTs) along with greater P3 amplitude and shorter P3 latency in the valid trials, after controlling for age and body mass index. Moreover, RTs were selectively related to P3 amplitude rather than P3 latency. Specifically, the bootstrap-based mediation model indicates that P3 amplitude mediates the relationship between fitness level and attention performance. Possible explanations regarding the relationships among aerobic fitness, cognitive performance, and brain potentials are discussed.


1956 ◽  
Vol 103 (2S) ◽  
pp. 346-353
Author(s):  
A.A. Robinson ◽  
F. McAulay ◽  
A.H. Banks ◽  
D. Hogg

1989 ◽  
Vol 28 (03) ◽  
pp. 160-167 ◽  
Author(s):  
P. Penczek ◽  
W. Grochulski

Abstract:A multi-level scheme of syntactic reduction of the epileptiform EEG data is briefly discussed and the possibilities it opens up in describing the dynamic behaviour of a multi-channel system are indicated. A new algorithm for the inference of a Markov network from finite sets of sample symbol strings is introduced. Formulae for the time-dependent state occupation probabilities, as well as joint probability functions for pairs of channels, are given. An exemplary case of analysis in these terms, taken from an investigation of anticonvulsant drug effects on EEG seizure patterns, is presented.


Author(s):  
M. Jeyanthi ◽  
C. Velayutham

In Science and Technology Development BCI plays a vital role in the field of Research. Classification is a data mining technique used to predict group membership for data instances. Analyses of BCI data are challenging because feature extraction and classification of these data are more difficult as compared with those applied to raw data. In this paper, We extracted features using statistical Haralick features from the raw EEG data . Then the features are Normalized, Binning is used to improve the accuracy of the predictive models by reducing noise and eliminate some irrelevant attributes and then the classification is performed using different classification techniques such as Naïve Bayes, k-nearest neighbor classifier, SVM classifier using BCI dataset. Finally we propose the SVM classification algorithm for the BCI data set.


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