Isolation of ventral stream EEG sources using Steady State EEG and Independent Components Analysis
نویسندگان
چکیده
منابع مشابه
Identification of anti-correlated resting-state networks using simultaneous EEG-fMRI and Independent Components Analysis
In the absence of an explicit task, temporal synchrony is maintained across brain regions. Taking advantage of this synchrony, resting-state fMRI has been used extensively to identify resting state networks (RSN) [1]. Fox et al. have reported that the default mode network (DMN) is anti-correlated with the task positive network (TPN) [2], reflecting the competing demands of these two networks. T...
متن کاملIndependent EEG Sources Are Dipolar
Independent component analysis (ICA) and blind source separation (BSS) methods are increasingly used to separate individual brain and non-brain source signals mixed by volume conduction in electroencephalographic (EEG) and other electrophysiological recordings. We compared results of decomposing thirteen 71-channel human scalp EEG datasets by 22 ICA and BSS algorithms, assessing the pairwise mu...
متن کاملEEG classification using generative independent component analysis
We present an application of independent component analysis (ICA) to the discrimination of mental tasks for EEG-based brain computer interface systems. ICA is most commonly used with EEG for artifact identification with little work on the use of ICA for direct discrimination of different types of EEG signals. By viewing ICA as a generative model, we can use Bayes’ rule to form a classifier. We ...
متن کاملMining EEG-fMRI using independent component analysis.
Independent component analysis (ICA) is a multivariate approach that has become increasingly popular for analyzing brain imaging data. In contrast to the widely used general linear model (GLM) that requires the user to parameterize the brain's response to stimuli, ICA allows the researcher to explore the factors that constitute the data and alleviates the need for explicit spatial and temporal ...
متن کاملEeg Sleep Spindle Processing with Independent Components Analysis
Sleep spindles are bursts of rhythmic activity characterized by progressively increasing, then gradually decreasing amplitude, present predominantly in stages 2, 3 and 4 of the sleep electroencephalogram (EEG). Topographic analyses of sleep spindle incidence suggested the existence of two distinct sleep spindle types, “slow” and “fast” spindles at approximately 12 and 14 Hz respectively. There ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Vision
سال: 2015
ISSN: 1534-7362
DOI: 10.1167/15.12.613