Identifying musical pieces from fMRI data using encoding and decoding models
نویسندگان
چکیده
منابع مشابه
Capturing the musical brain with Lasso: Dynamic decoding of musical features from fMRI data
We investigated neural correlates of musical feature processing with a decoding approach. To this end, we used a method that combines computational extraction of musical features with regularized multiple regression (LASSO). Optimal model parameters were determined by maximizing the decoding accuracy using a leave-one-out cross-validation scheme. The method was applied to functional magnetic re...
متن کاملEncoding and decoding in fMRI
Over the past decade fMRI researchers have developed increasingly sensitive techniques for analyzing the information represented in BOLD activity. The most popular of these techniques is linear classification, a simple technique for decoding information about experimental stimuli or tasks from patterns of activity across an array of voxels. A more recent development is the voxel-based encoding ...
متن کاملDecoding Cognitive States from fMRI Data Using Support Vector Regression
In this paper we describe a method based on Support Vector machines for Regression (SVR) to decode cognitive states from functional Magnetic Resonance Imaging (fMRI) data. In the context of the Pittsburgh Brain Activity Interpretation Competition (PBAIC, 2007), three participants were scanned during three runs of 20-minute immersion in a Virtual Reality Environment (VRE) where they played a gam...
متن کاملA design of genetic encoding for breeding short musical pieces
This paper describes a design of genetic encoding of short score of music for Interactive Evolutionary Computation. A genome includes three types of chromosomes, rhythm, pitch and velocity. Each chromosome is a two dimensional array of sixteen beats by 23 parts. Each element for rhythm is interpreted as indicating play, rest and continuation. Elements of pitches and velocity chromosomes are use...
متن کاملData-driven HRF estimation for encoding and decoding models
Despite the common usage of a canonical, data-independent, hemodynamic response function (HRF), it is known that the shape of the HRF varies across brain regions and subjects. This suggests that a data-driven estimation of this function could lead to more statistical power when modeling BOLD fMRI data. However, unconstrained estimation of the HRF can yield highly unstable results when the numbe...
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: Scientific Reports
سال: 2018
ISSN: 2045-2322
DOI: 10.1038/s41598-018-20732-3