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http://dx.doi.org/10.9708/jksci.2022.27.08.001

Applying a Novel Neuroscience Mining (NSM) Method to fNIRS Dataset for Predicting the Business Problem Solving Creativity: Emphasis on Combining CNN, BiLSTM, and Attention Network  

Kim, Kyu Sung (SKK Business School, Sungkyunkwan University)
Kim, Min Gyeong (SKK Business School, Sungkyunkwan University)
Lee, Kun Chang (SKK Business School/SAIHST (Samsung Advanced Institute of Health Sciences & Technology), Sungkyunkwan University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.8, 2022 , pp. 1-7 More about this Journal
Abstract
With the development of artificial intelligence, efforts to incorporate neuroscience mining with AI have increased. Neuroscience mining, also known as NSM, expands on this concept by combining computational neuroscience and business analytics. Using fNIRS (functional near-infrared spectroscopy)-based experiment dataset, we have investigated the potential of NSM in the context of the BPSC (business problem-solving creativity) prediction. Although BPSC is regarded as an essential business differentiator and a difficult cognitive resource to imitate, measuring it is a challenging task. In the context of NSM, appropriate methods for assessing and predicting BPSC are still in their infancy. In this sense, we propose a novel NSM method that systematically combines CNN, BiLSTM, and attention network for the sake of enhancing the BPSC prediction performance significantly. We utilized a dataset containing over 150 thousand fNIRS-measured data points to evaluate the validity of our proposed NSM method. Empirical evidence demonstrates that the proposed NSM method reveals the most robust performance when compared to benchmarking methods.
Keywords
Neuroscience mining (NSM); Business problem-solving creativity (BPSC); fNIRS; CNN; BiLSTM; Attention mechanism;
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