Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods

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.Comment: 7 pages, presented at the IEEE 20th India Council International Conference (INDICON 2023), Hyderabad, India, December 202

Collaborative efforts to investigate emissions from residential and municipal trash burning in India

Emissions from trash burning represent an important component of regional air quality, especially in countries such as India where the practice of roadside, residential, and municipal trash burning is highly prevalent. However, research on trash emissions is limited due to difficulties associated with measuring a source that varies widely in composition and burning characteristics. To investigate trash burning in India, a collaborative program was formed among RTI, Duke University, and the India Institute of Technology (IIT) in Gandhinagar, involving both senior researchers and students. In addition to researching emission measurement techniques, this program aimed to foster international partnerships and provide students with a hands-on educational experience, culminating in a pilot study in India. Before traveling, students from Duke and IIT met virtually to design experiments. IIT students were able to visit proposed sites and offer specified knowledge on burning practices prior to the pilot study, allowing potential experiments to be iteratively improved. The results demonstrated a proof of concept of using a low-cost sensor attached to a commercial drone to measure emissions from a municipal dump site. In addition, for small-scale residential and roadside trash burning, a combustor was designed to burn trash in a consistent way. Results suggested that thermocouples and low-cost sensors may offer an affordable way for combustor designers to assess particulate emissions during prototype iterations. More experiences like this should be made available so that future research can benefit from the unique insights that come from having veteran researchers work with students and from forming international partnerships.by H.Vreeland, C.Norris, L. Shum, J.Pokuri, E. Shannon, Anmol Raina, Ayushman Tripathi, Dinesh Borse, Ankit Patel, P. Dixit, Mike H. Bergin and B. R. Stone