Single trial decoding of belief decision making from EEG and fMRI data using independent components features
نویسندگان
چکیده
منابع مشابه
Single trial decoding of belief decision making from EEG and fMRI data using independent components features
The complex task of assessing the veracity of a statement is thought to activate uniquely distributed brain regions based on whether a subject believes or disbelieves a given assertion. In the current work, we present parallel machine learning methods for predicting a subject's decision response to a given propositional statement based on independent component (IC) features derived from EEG and...
متن کاملDecoding Sequence Learning from Single-Trial Intracranial EEG in Humans
We propose and validate a multivariate classification algorithm for characterizing changes in human intracranial electroencephalographic data (iEEG) after learning motor sequences. The algorithm is based on a Hidden Markov Model (HMM) that captures spatio-temporal properties of the iEEG at the level of single trials. Continuous intracranial iEEG was acquired during two sessions (one before and ...
متن کاملEEG-informed fMRI reveals spatiotemporal characteristics of perceptual decision making.
Single-unit and multiunit recordings in primates have already established that decision making involves at least two general stages of neural processing: representation of evidence from early sensory areas and accumulation of evidence to a decision threshold from decision-related regions. However, the relay of information from early sensory to decision areas, such that the accumulation process ...
متن کاملPerformance comparison of machine learning algorithms and number of independent components used in fMRI decoding of belief vs. disbelief
Machine learning (ML) has become a popular tool for mining functional neuroimaging data, and there are now hopes of performing such analyses efficiently in real-time. Towards this goal, we compared accuracy of six different ML algorithms applied to neuroimaging data of persons engaged in a bivariate task, asserting their belief or disbelief of a variety of propositional statements. We performed...
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 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: Frontiers in Human Neuroscience
سال: 2013
ISSN: 1662-5161
DOI: 10.3389/fnhum.2013.00392