Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods

Akash K Rao; Shashank Uttrani; Vishnu K Menon; Darshil Shah; Arnav Bhavsar; Shubhajit Roy Chowdhury; Varun Dutt

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arXiv · 2401.17711

Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods

Akash K Rao·Shashank Uttrani·Vishnu K Menon·Darshil Shah·Arnav Bhavsar·Shubhajit Roy Chowdhury·Varun Dutt

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Abstract

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

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Akash K Rao, Shashank Uttrani, Vishnu K Menon, Darshil Shah, Arnav Bhavsar, Shubhajit Roy Chowdhury, Varun Dutt. 2024-01-31. Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods. https://arxiv.org/abs/2401.17711

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