Classifying Single-Trial EEG During Motor Imagery Using a Multivariate Mutual Information Based Phase Synchrony Measure

Baboukani, Payam Shahsavari; Mohammadi, Sara; Azemi, Ghasem

doi:10.1109/icbme.2017.8430223

Cited by 3 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rajan and Devassy [ 106 ] used a boosting approach that improved the classification by a combination of feature vectors. Baboukani et al [ 107 ] used an Ant Colony Optimization technique to select a subset of features for SVM based classification of MI-BCI. Wang et al [ 108 ] divided all of the electrodes in several sensor groups.…”

Section: Key Issues In MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

107

View full text Add to dashboard Cite

Motor imagery (MI) based brain–computer interface (BCI) aims to provide a means of communication through the utilization of neural activity generated due to kinesthetic imagination of limbs. Every year, a significant number of publications that are related to new improvements, challenges, and breakthrough in MI-BCI are made. This paper provides a comprehensive review of the electroencephalogram (EEG) based MI-BCI system. It describes the current state of the art in different stages of the MI-BCI (data acquisition, MI training, preprocessing, feature extraction, channel and feature selection, and classification) pipeline. Although MI-BCI research has been going for many years, this technology is mostly confined to controlled lab environments. We discuss recent developments and critical algorithmic issues in MI-based BCI for commercial deployment.

show abstract

Section: Key Issues In MI Based Bcimentioning

confidence: 99%

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

Singh

Hussain

Lal

et al. 2021

Sensors

107

View full text Add to dashboard Cite

show abstract

EEG Phase Synchrony Reflects SNR Levels During Continuous Speech-in-Noise Tasks

Baboukani

Graversen²,

Aličković

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

View full text Add to dashboard Cite

Speech to noise ratio improvement induces nonlinear parietal phase synchrony in hearing aid users

et al. 2022

Self Cite

View full text Add to dashboard Cite

ObjectivesComprehension of speech in adverse listening conditions is challenging for hearing-impaired (HI) individuals. Noise reduction (NR) schemes in hearing aids (HAs) have demonstrated the capability to help HI to overcome these challenges. The objective of this study was to investigate the effect of NR processing (inactive, where the NR feature was switched off, vs. active, where the NR feature was switched on) on correlates of listening effort across two different background noise levels [+3 dB signal-to-noise ratio (SNR) and +8 dB SNR] by using a phase synchrony analysis of electroencephalogram (EEG) signals.DesignThe EEG was recorded while 22 HI participants fitted with HAs performed a continuous speech in noise (SiN) task in the presence of background noise and a competing talker. The phase synchrony within eight regions of interest (ROIs) and four conventional EEG bands was computed by using a multivariate phase synchrony measure.ResultsThe results demonstrated that the activation of NR in HAs affects the EEG phase synchrony in the parietal ROI at low SNR differently than that at high SNR. The relationship between conditions of the listening task and phase synchrony in the parietal ROI was nonlinear.ConclusionWe showed that the activation of NR schemes in HAs can non-linearly reduce correlates of listening effort as estimated by EEG-based phase synchrony. We contend that investigation of the phase synchrony within ROIs can reflect the effects of HAs in HI individuals in ecological listening conditions.

show abstract

Classifying Single-Trial EEG During Motor Imagery Using a Multivariate Mutual Information Based Phase Synchrony Measure

Cited by 3 publications

References 17 publications

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface

EEG Phase Synchrony Reflects SNR Levels During Continuous Speech-in-Noise Tasks

Speech to noise ratio improvement induces nonlinear parietal phase synchrony in hearing aid users

Contact Info

Product

Resources

About