Spatio-temporal deep learning for EEG-fNIRS brain computer interface.

In this paper the classification of motor imagery brain signals is addressed. The innovative idea is to use both temporal and spatial knowledge of the input data to increase the performance. Definitely, the electrode locations on the scalp is as important as the acquired temporal signals from every individual electrode.

Semiblind Spectral Factorization Approach for Magnetic Resonance Spectroscopy Quantification.

An observed magnetic resonance (MR) spectrum is composed of a set of metabolites spectrum, baseline, and noise. Quantification of metabolites of interest in the MR spectrum provides great opportunity for early diagnosis of dangerous disease such as brain tumors. In this paper, a novel spectral factorization approach based on singular spectrum analysis (SSA) is proposed to quantify magnetic resonance spectroscopy (MRS).

Simultaneous BOLD detection and incomplete fMRI data reconstruction.

The problem of simultaneous blood oxygenation level dependent (BOLD) detection and data completion is addressed in this paper. It is assumed that a set of fMRI data with significant number of missing samples is available and the aim is to recover those samples with least possible quality degradation. At the same time, BOLD should be detected.

A new informed tensor factorization approach to EEG-fMRI fusion.

Background: In this paper exploitation of correlation between post-movement beta rebound in EEG and blood oxygenation level dependent (BOLD) in fMRI is addressed. Brain studies do not reveal any clear relationship between synchronous neuronal activity and BOLD signal. Simultaneous recording of EEG and fMRI provides a great opportunity to recognize different areas of the brain involved in EEG events.

A predictive modeling approach to analyze data in EEG-fMRI experiments.

In this paper, a novel technique based on blind source extraction (BSE) using linear prediction is proposed to extract rolandic beta rhythm from electroencephalogram (EEG) recorded in a simultaneous EEG-fMRI experiment. We call this method CLP-BSE standing for constrained-linear-prediction BSE. Extracting event-related oscillations is a crucial task due to nonphase-locked nature and inter-trial variability of this event.

Removing ballistocardiogram artifact from EEG using short- and long-term linear predictor.

In this paper, a novel source extraction method is proposed for removing ballistocardiogram (BCG) artifact from EEG. BCG appears in EEG signals recorded simultaneously with functional magnetic resonance imaging. The proposed method is a semiblind source extraction algorithm based on linear prediction technique.

Blind separation of image sources via adaptive dictionary learning.

Sparsity has been shown to be very useful in source separation of multichannel observations. However, in most cases, the sources of interest are not sparse in their current domain and one needs to sparsify them using a known transform or dictionary. If such a priori about the underlying sparse domain of the sources is not available, then the current algorithms will fail to successfully recover the sources.

A new spatially constrained NMF with application to fMRI.

In this paper the problem of BOLD detection is addressed. The focus here is on non-negative matrix factorization (NMF), which is a data driven method and able to provide part-based representation of data. A new constrained optimization problem is proposed for the purpose of BOLD detection.