[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125866-en":3,"doc-seo-125866-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},125866,1099523885336,"Violet","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods","Predicting and understanding changes in cognitive performance after a longitudinal intervention is a core neuroscience challenge. This study examines whether EEG-based functional connectivity and machine learning can forecast post-intervention performance changes following transcranial direct current stimulation (tDCS). Forty subjects completed a multitasking task with 32-channel EEG on Day 1, received 2 mA anodal tDCS or sham from Day 2–Day 7, then repeated the task with EEG on Day 10. Phase lag index and directed transfer function were extracted and modeled to predict cognitive changes.","Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods  \nAkash K Rao 1[0000-0003-4025-1042], Shashank Uttrani1[0000-0003-2601-2125], Vishnu K Men  \non 1[0009-0007-9449-0934], Darshil Shah2[0009-0000-1591-6351], Arnav Bhavsar3 [0000-0003-2849-4375], Shubhajit Roy Chowdhury3 [0000-0003-1878-6657], Varun Dutt 1[0000-0002-2151-8314]  \n1 Applied Cognitive Science Laboratory, Indian Institute of Technology Mandi, Himachal Pradesh, India  \n2Ashoka Centre for Social and Behavior Change, Delhi, India  \n3 School of Computing and Electrical Engineering, Indian Institute of Technology Mandi, Himachal Pradesh, India  \n[akashrao.iitmandi@gmail.com](akashrao.iitmandi@gmail.com)  \nAbstract. 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 postintervention. 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 activecontrol 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.  \nResults 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.  \nKeywords: Functional connectivity, transcranial direct current stimulation,  \nmulti-layer perceptron, Electroencephalography, multitasking, cognitive per  \nformance.  \n1 Introduction  \nCognitive performance can be described as the ability to effectively process and integrate information, make decisions, tackle complex problems, and efficiently use the inherent information-processing propensity of the brain [1] . The information processing capabilities become unequivocally important in complex cognitive processes like higher-order decision-making, multitasking, situational awareness, etc., where many cognitive processes are required to act in unison [1,2] . Effectively balancing several cognitive processes, such as attention, working memory, and task switching, is what multitasking entails. The propensity to distribute and shift attention between tasks is essential for successful multitasking [1] . However, the human attentional system is limited, and attempting to handle multiple stimuli simultaneously might result in attentional dispersion and poor overall performance [2] .  \nOver the years, several behavioral interventions like neurofeedback, music, meditation, and extended reality have been used to enhance cognitive performance overtime. However, non-invasive brain stimulation techniques like transcranial direct current stimulation (tDCS) have gained considerable traction recently [3] . tDCS is usually adm","cbCaiumWio4enHA9","https://ap.wps.com/l/cbCaiumWio4enHA9","pdf",394380,7,1,15,"English","en",105,"# Abstract\n# Introduction\n## Cognitive performance and multitasking\n## Longitudinal tDCS and neuroplasticity\n## EEG, functional connectivity, and machine learning","[{\"question\":\"What intervention was used to influence cognitive performance over time?\",\"answer\":\"The study used longitudinal transcranial direct current stimulation (tDCS), comparing 2 mA anodal stimulation during task training with a sham active-control condition.\"},{\"question\":\"How was brain activity recorded during the multitasking task?\",\"answer\":\"All subjects performed the multitasking task with simultaneous 32-channel EEG acquisition on Day 1 and again on Day 10.\"},{\"question\":\"Which EEG-based functional connectivity metrics were extracted for prediction?\",\"answer\":\"Source-level functional connectivity metrics including phase lag index and directed transfer function were extracted from EEG data on Day 1 and Day 10.\"}]","Prediction of multitasking performance post-longitudinal tDCS via EEG-based functional connectivity and machine learning methods | 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intervention was used to influence cognitive performance over time?","Question",{"text":77,"@type":78},"The study used longitudinal transcranial direct current stimulation (tDCS), comparing 2 mA anodal stimulation during task training with a sham active-control condition.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"How was brain activity recorded during the multitasking task?",{"text":82,"@type":78},"All subjects performed the multitasking task with simultaneous 32-channel EEG acquisition on Day 1 and again on Day 10.",{"name":84,"@type":75,"acceptedAnswer":85},"Which EEG-based functional connectivity metrics were extracted for prediction?",{"text":86,"@type":78},"Source-level functional connectivity metrics including phase lag index and directed transfer function were extracted from EEG data on Day 1 and Day 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