Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods Article Swipe
Akash K Rao
,
Vishnu K Menon
,
Shashank Uttrani
,
Ayushman Dixit
,
Dipanshu Verma
,
Varun Dutt
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.17700
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.17700
Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in a goal-directed manner. Understanding and classifying the changes in executive functioning after longitudinal interventions (like transcranial direct current stimulation (tDCS)) has not been explored in the literature. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS. Fifty subjects were divided into experimental and placebo control groups. EEG data was collected while subjects performed an executive functioning task on Day 1. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS. On Day 10, subjects repeated the tasks specified on Day 1. Different functional connectivity metrics were extracted from EEG data and eventually used for classifying executive functioning performance using different machine learning algorithms. Results revealed that a novel combination of partial directed coherence and multi-layer perceptron (along with recursive feature elimination) resulted in a high classification accuracy of 95.44%. We discuss the implications of our results in developing real-time neurofeedback systems for assessing and enhancing executive functioning performance post-tDCS administration.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.17700
- https://arxiv.org/pdf/2401.17700
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391462779
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391462779Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2401.17700Digital Object Identifier
- Title
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Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methodsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
-
2024-01-31Full publication date if available
- Authors
-
Akash K Rao, Vishnu K Menon, Shashank Uttrani, Ayushman Dixit, Dipanshu Verma, Varun DuttList of authors in order
- Landing page
-
https://arxiv.org/abs/2401.17700Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2401.17700Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2401.17700Direct OA link when available
- Concepts
-
Psychology, Functional connectivity, Executive functions, Cognitive psychology, Computer science, Physical medicine and rehabilitation, Neuroscience, Cognition, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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