Electroencephalogram with Machine Learning for Estimation of Mental Confusion Level
Harsh Kumar1, Mayank Sethia2, Himanshu Thakur3, Ishita Agrawal4, Swarnalatha P5

1Harsh Kumar,  B. Tech Computer Science Student from the School of Computer Science and Engineering at VIT Vellore.
2Mayank Sethia, B. Tech Computer Science Student from the School of Computer Science and Engineering at VIT Vellore.
3Himanshu Thakur, B. Tech Computer Science Student from the School of Computer Science and Engineering at VIT Vellore.
4Ishita Agrawal, B. Tech Computer Science Student from the School of Computer Science and Engineering at VIT Vellore.
5Swarnalatha P,  Associate Professor at the School of Computer Science and Engineering at VIT Vellore.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 761-765 | Volume-9 Issue-2, December, 2019. | Retrieval Number:  B2943129219/2020©BEIESP | DOI: 10.35940/ijeat.B2943.129219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: Estimating the mental state of an individual is crucial to many applications. A quantitative measure of the confusion one faces while doing a task can be useful in determining which subtask is the most difficult. This paper thus aims to develop an algorithm to estimate the confusion score using EEG signals collected using a Neurosky Mindwave Headset. Also, a full contextual audio based confusion score is generated to improve the system’s resilience. In this paper, the final algorithm is used to propose an EEG based system to enable the UI/UX testing which can help in confusion estimation and thus provide a qualitative means to measure the attention and concentration level of people which can be extended to various applications. The raw EEG data collected from the device was used to calculate the confusion score using various Machine Learning algorithms. This brain computer interface (BCI) system can be extended for calculating the confusion score of a person which can be used for various applications such as teaching, child health monitoring, suicide prevention, mental health analysis etc. The brain computer interface thus calculates the confusion score and based on the threshold value of the attention and concentration level it performs certain actions such as sending messages and alerts to emergency contacts. This is further extended to solve the problem of Usability testing in Human Computer Interaction.
Keywords: Brain Computer Interface, Confusion score, Attention level, Concentration level, Mental health, Human computer interaction.