Decoding the Confidence Level of Subjects in Answering Multiple Choice Questions Using EEG Induced Capsule Network - Bose, Shirsha; Ghosh, Sayantani; Konar, Amit; Nagar, Atulya K. (2021 IEEE Symposium Series on Computational Intelligence (SSCI), 2021)
The paper introduces an innovative methodology for the automatic discrimination of multiple choice answers chosen by merit and random guess by analyzing the confidence level of examinees using an Electroencephalographic system. The acquired brain signals of the subjects participating in the experiment are first examined using the eLORETA software which portrays the active participation of the middle frontal gyrus and precuneus when a subject is fully confident regarding the choice of the correct answer. In the next phase, the signals are pre-processed and converted to spectrogram plots using Short Time Fourier Transform (STFT) which reveal the enhanced activation of theta and lower alpha bands when a subject attempts an answer with his/her merit. On the other hand, the afore-said frequency bands portray reduced activation when a subject tries to choose an answer by a mere guess. The acquired spectrogram plots are transferred to a novel Capsule network model that aids in categorizing the two degrees of confidence level: High and Low. The novelty in the design of the Capsule based classifier lies in the introduction of a depthwise separable convolution layer, a squeeze and excitation attention mechanism and a Sigmoid-Weighted Linear Unit (SiLU) based dynamic routing algorithm. The proposed classifier demonstrates promising results in categorizing the two classes of confidence level and also outperforms its conventional counterparts. Thus, the proposed scheme can be utilized to improve the quality of assessment in multiple choice based examinations.