Classification of Attention Performance Post-Longitudinal tDCS via Functional Connectivity and Machine Learning Methods-Reference-Cited by-同舟云学术

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Classification of Attention Performance Post-Longitudinal tDCS via Functional Connectivity and Machine Learning Methods

Published:2024-04-05 Issue: Volume: Page:1-6
ISSN:
Container-title:2024 IEEE 9th International Conference for Convergence in Technology (I2CT)
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Author:

Rao Akash K¹,Menon Vishnu K¹,Bhavsar Arnav²,Chowdhury Shubhajit Roy²,Negi Ramsingh³,Dutt Varun¹

Affiliation:

1. Indian Institute of Technology Mandi,Applied Cognitive Science Laboratory,Mandi,India

2. Indian Institute of Technology Mandi,School of Computing and Electrical Engineering,Mandi,India

3. Institute of Nuclear Medicine and Allied Sciences,Cognitive Control and Machine Learning Group,Delhi,India

Funder

Research and Development

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10543249/10543223/10544190.pdf?arnumber=10544190

Reference17 articles.

1. Modulation of executive control in dual tasks with transcranial direct current stimulation (tDCS)

2. Transcranial direct current stimulation changes resting state functional connectivity: A large-scale brain network modeling study

3. A Comparison between Uni- and Bilateral tDCS Effects on Functional Connectivity of the Human Motor Cortex

4. EEG functional connectivity metrics wPLI and wSMI account for distinct types of brain functional interactions

5. Task-Independent Mental Workload Classification Based Upon Common Multiband EEG Cortical Connectivity

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