| Title: |
Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods |
| Authors: |
Rao, Akash K, Uttrani, Shashank, Menon, Vishnu K, Shah, Darshil, Bhavsar, Arnav, Chowdhury, Shubhajit Roy, Dutt, Varun |
| Publication Year: |
2024 |
| Collection: |
Computer Science |
| Subject Terms: |
Computer Science - Human-Computer Interaction, Computer Science - Artificial Intelligence |
| More Details: |
Predicting and understanding the changes in cognitive performance, especially after a longitudinal intervention, is a fundamental goal in neuroscience. Longitudinal brain stimulation-based interventions like transcranial direct current stimulation (tDCS) induce short-term changes in the resting membrane potential and influence cognitive processes. However, very little research has been conducted on predicting these changes in cognitive performance post-intervention. In this research, we intend to address this gap in the literature by employing different EEG-based functional connectivity analyses and machine learning algorithms to predict changes in cognitive performance in a complex multitasking task. Forty subjects were divided into experimental and active-control conditions. On Day 1, all subjects executed a multitasking task with simultaneous 32-channel EEG being acquired. From Day 2 to Day 7, subjects in the experimental condition undertook 15 minutes of 2mA anodal tDCS stimulation during task training. Subjects in the active-control condition undertook 15 minutes of sham stimulation during task training. On Day 10, all subjects again executed the multitasking task with EEG acquisition. Source-level functional connectivity metrics, namely phase lag index and directed transfer function, were extracted from the EEG data on Day 1 and Day 10. Various machine learning models were employed to predict changes in cognitive performance. Results revealed that the multi-layer perceptron and directed transfer function recorded a cross-validation training RMSE of 5.11% and a test RMSE of 4.97%. We discuss the implications of our results in developing real-time cognitive state assessors for accurately predicting cognitive performance in dynamic and complex tasks post-tDCS intervention
Comment: 16 pages, presented at the 30th International Conference on Neural Information Processing (ICONIP2023), Changsha, China, November 2023 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2401.17711 |
| Accession Number: |
edsarx.2401.17711 |
| Database: |
arXiv |
