Investigation on Identifying Implicit Learning Event from EEG Signal Using Multiscale Entropy and Artificial Bee Colony

Abstract

The way people learn will play an essential role in the sustainable development of the educational system for the future. Utilizing technology in the age of information and incorporating it into how people learn can produce better learners. Implicit learning is a type of learning of the underlying rules without consciously seeking or understanding the rules; it is commonly seen in small children while learning how to speak their native language without learning grammar. This research aims to introduce a processing system that can systematically identify the relationship between implicit learning events and their Encephalogram (EEG) signal characteristics. This study converted the EEG signal from participants while performing cognitive task experiments into Multiscale Entropy (MSE) data. Using MSE data from different frequency bands and channels as features, the system explored a wide range of classifiers and observed their performance to see how they classified the features related to participants’ performance. The Artificial Bee Colony (ABC) method was used for feature selection to improve the process to make the system more efficient. The results showed that the system could correctly identify the differences between participants’ performance using MSE data and the ABC method with 95% confidence.

Joint Reconfiguration after Failure for Performing Emblematic Gestures in Humanoid Receptionist Robot

“…To solve the problem of joint failure recovery for humanoid receptionist robots, the method should find the reconfigured joint angles of the remaining joints that allow the robots to perform emblematic gestures; the output gestures may not be completely the same as the reference gestures but still communicate the meanings of those gestures. Similar to the way that animals adapt themselves to walk after a leg injury, bio-inspired artificial intelligence methods simulate self-organized mechanisms occurring in nature and use them to solve complex optimization problems [26]. In this study, we proposed the use of bio-inspired artificial intelligence methods for the joint reconfiguration of humanoid receptionist robots when one reference joint angle set for each emblematic gesture is available to the robots.…”

Discriminable Multi-Label Attribute Selection for Pre-Course Student Performance Prediction

“…At present, a great number of student performance and transcript-related data are stored in the relevant information systems of educational institutions, which are often dormant in the data system, without being fully utilized and referenced. With the continuous advancement of artificial intelligence technology, various fields-such as medicine [7,8], manufacturing [9], engineering optimization [10], speech recognition [11], and image processing [12]-have adopted and applied the combination of big data analysis and artificial intelligence algorithms radiating novel, cause-driven vitality [13,14]. With the advancement of education informatization and the promotion of smart campuses, colleges and universities have gradually accumulated massive educational data resources [15].…”