• Palak Vyas
• Kirti Jain

Stress is major concern these days. According to WHO, stress is a mental health problem affecting the life of one in four citizens. According to neuroscience, human brain is the main target of mental stress, because only human brain can determine a situation which is threatening and stressful. Using wearable sensors and bio signal processing, several technologies are developed for human stress detection. Human bio signals such as electroencephalography (EEG), electromyography (EMG), electrocardiography (ECG), galvanic skin response (GSR), blood volume pulses (BVP), blood pressure (BP), skin temperature (ST) and respiration are used to detect stress. In proposed method, electroencephalography (EEG) is used to detect confusion among students. We collected EEG signal data from 10 college students while they watched MOOC video clips. We extracted online education videos that are assumed not to be confusing for college students, such as videos of the introduction of basic algebra or geometry. We also prepare videos that are expected to confuse a typical college student if a student is not familiar with the video topics like Quantum Mechanics, and Stem Cell Research. In this study we use the Python environment for processing the EEG signals. KNN algorithm is applied for classifying whether the student is least confused or most confused. Obtained classification report, shows 83% accuracy performance for this data using KNN.

Electroencephalography (EEG), K Nearest Neighbor (KNN), Machine learning, Mental stress

"PREDICTING CONFUSION USING K NEAREST NEIGHBOR ALGORITHM FOR EEG DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.407-411, May-2019, Available :http://www.jetir.org/papers/JETIR1905E60.pdf

"PREDICTING CONFUSION USING K NEAREST NEIGHBOR ALGORITHM FOR EEG DATA", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp407-411, May-2019, Available at : http://www.jetir.org/papers/JETIR1905E60.pdf