Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929230208565248 |
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| author | Rao, Akash K Menon, Vishnu K Uttrani, Shashank Dixit, Ayushman Verma, Dipanshu Dutt, Varun |
| author_facet | Rao, Akash K Menon, Vishnu K Uttrani, Shashank Dixit, Ayushman Verma, Dipanshu Dutt, Varun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_17700 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods Rao, Akash K Menon, Vishnu K Uttrani, Shashank Dixit, Ayushman Verma, Dipanshu Dutt, Varun Human-Computer Interaction Artificial Intelligence 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. |
| title | Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2401.17700 |