Sreenivasan Meyyappan

This study investigates the neural mechanisms of willed attention control using fMRI and EEG data, finding distinct activation patterns and decoding capabilities in the frontoparietal decision network.

This study uses fMRI and multivariate analysis to show how spatial attention templates guide stimulus selection through neural connectivity and decoding patterns.

This study investigates the role of the Ventral Attention Network (VAN) in attentional reorienting during cross-modal expectancy violations using EEG and fMRI.

The paper uses EEG to show that visual attention is influenced by object boundaries, supporting object-based attentional prioritization.

This paper investigates the hierarchical organization of visual feature attention control using EEG and multivariate decoding, finding that broad feature dimensions are biased before specific attributes.

The paper analyzes attention-related baseline activity using fMRI and two analytic approaches, finding that multivariate pattern analysis reveals top-down attentional biases across the visual hierarchy, which better predict behavioral performance than univariate activation.

This study compares SVM and CNN performance on fMRI data, finding CNNs achieve higher decoding accuracy and use distinct brain activity patterns.

The paper uses deep learning methods to identify brain regions associated with trigeminal neuralgia pain from fMRI data.

The paper investigates how attentional control modulates a left visual field bias in cued visual spatial attention using pupillometry and fMRI.

This study investigates the role of the inferior frontal junction (IFJ) in feature versus spatial attention using fMRI, finding that right IFJ is involved in feature attention but not spatial attention.

This study examines how theta tACS affects verbal working memory, finding load- and subject-dependent improvements in performance and frontoparietal theta synchrony.

This study uses EEG and fMRI with machine learning to show that affective pictures evoke valence-specific neural representations in retinotopic visual cortex, influenced by limbic and frontal connectivity.

The paper uses multivariate pattern classification on multichannel ERPs to investigate attention control and selection, finding earlier and stronger attentional effects under instructional cueing and linking decoding accuracy to behavioral performance.