scholarly journals Time domain analysis of electroencephalogram (EEG) signals for word level comprehension in deaf graduates with congenital and acquired hearing loss

2021 ◽  
Vol 1070 (1) ◽  
pp. 012083
Author(s):  
G Shirly ◽  
S Jerritta
Mekatronika ◽  
2019 ◽  
Vol 1 (2) ◽  
pp. 115-121
Author(s):  
Asrul Adam ◽  
Ammar Faiz Zainal Abidin ◽  
Zulkifli Md Yusof ◽  
Norrima Mokhtar ◽  
Mohd Ibrahim Shapiai

In this paper, the developments in the field of EEG signals peaks detection and classification methods based on time-domain analysis have been discussed. The use of peak classification algorithm has end up the most significant approach in several applications. Generally, the peaks detection and classification algorithm is a first step in detecting any event-related for the variation of signals. A review based on the variety of peak models on their respective classification methods and applications have been investigated. In addition, this paper also discusses on the existing feature selection algorithms in the field of peaks classification.


2005 ◽  
Vol 360 (1457) ◽  
pp. 1015-1024 ◽  
Author(s):  
T Koenig ◽  
D Studer ◽  
D Hubl ◽  
L Melie ◽  
W.K Strik

We present an overview of different methods for decomposing a multichannel spontaneous electroencephalogram (EEG) into sets of temporal patterns and topographic distributions. All of the methods presented here consider the scalp electric field as the basic analysis entity in space. In time, the resolution of the methods is between milliseconds (time-domain analysis), subseconds (time- and frequency-domain analysis) and seconds (frequency-domain analysis). For any of these methods, we show that large parts of the data can be explained by a small number of topographic distributions. Physically, this implies that the brain regions that generated one of those topographies must have been active with a common phase. If several brain regions are producing EEG signals at the same time and frequency, they have a strong tendency to do this in a synchronized mode. This view is illustrated by several examples (including combined EEG and functional magnetic resonance imaging (fMRI)) and a selective review of the literature. The findings are discussed in terms of short-lasting binding between different brain regions through synchronized oscillations, which could constitute a mechanism to form transient, functional neurocognitive networks.


Author(s):  
Ammama Furrukh Gill ◽  
Syeda Alishbah Fatima ◽  
Aafreen Nawaz ◽  
Ayesha Nasir ◽  
M. Usman Akram ◽  
...  

2012 ◽  
Vol 182-183 ◽  
pp. 1885-1889
Author(s):  
Jing Zhou ◽  
Li Jun Li ◽  
Ning Shan Li ◽  
Xiao Ming Wu ◽  
Rong Qian Yang

Movement whether it is actual or imaginary can produce different electroencephalogram (EEG) signals. How to extract features of signals and accurately classify them is a key to brain-computer interface(BCI) system. In the paper, BCI competition data downloaded from BCI website are used as study object, through time-domain analysis and frequency-domain analysis, according to the attribute of event-related synchronization (ERS) and event-related desynchronization (ERD) during imagery movement, energy difference of lead C3 and C4 are selected as features and wavelet package is used to extract them. Probabilistic neural networks (PNN) is used as classification method. Compared with other two calssification methods such as support vector method (SVM) and liner classifier, the classification accuracy rate of PNN reaches to 89.2% steadily and is higher than them. It is proved that the method provided in the paper are effective for identifying imaginary movements.


1993 ◽  
Vol 3 (3) ◽  
pp. 581-591 ◽  
Author(s):  
Wojciech Gwarek ◽  
Malgorzata Celuch-Marcysiak

2017 ◽  
Vol 109 (6) ◽  
pp. 3307-3317
Author(s):  
Afshin Hatami ◽  
Rakesh Pathak ◽  
Shri Bhide

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