Design of Healthcare Assistant Using EEG Signals for Stress

Author(s):  
Nilima Salankar

Stress is a matter of concern for every age group. In this chapter, the author has focused on the age group 17-24 (five males and five females), who have just completed senior secondary and entered into an undergraduate course, who are vulnerable to cope up with the situation of transition. The focus of the chapter is on the design of the study protocol to identify stress levels by using induced stress tests like Stroop color test and arithmetic three-level (easy, medium, and hard) test. Data is divided into bands delta theta alpha beta and gamma by using four levels of discrete wavelet transform, and features extracted are Hjorth parameters activity, mobility, and complexity from the combination of channels and band levels. The accuracy achieved for stress and no-stress for male vs. male, female vs. female, and male vs. female is in the range 93-98% for the combination of channels at the frontal position and at band delta theta alpha beta and gamma whereas less involvement has been observed for other lobes parietal, temporal, and occipital.

Informatica ◽  
2013 ◽  
Vol 24 (4) ◽  
pp. 657-675
Author(s):  
Jonas Valantinas ◽  
Deividas Kančelkis ◽  
Rokas Valantinas ◽  
Gintarė Viščiūtė

2020 ◽  
Vol 64 (3) ◽  
pp. 30401-1-30401-14 ◽  
Author(s):  
Chih-Hsien Hsia ◽  
Ting-Yu Lin ◽  
Jen-Shiun Chiang

Abstract In recent years, the preservation of handwritten historical documents and scripts archived by digitized images has been gradually emphasized. However, the selection of different thicknesses of the paper for printing or writing is likely to make the content of the back page seep into the front page. In order to solve this, a cost-efficient document image system is proposed. In this system, the authors use Adaptive Directional Lifting-Based Discrete Wavelet Transform to transform image data from spatial domain to frequency domain and perform on high and low frequencies, respectively. For low frequencies, the authors use local threshold to remove most background information. For high frequencies, they use modified Least Mean Square training algorithm to produce a unique weighted mask and perform convolution on original frequency, respectively. Afterward, Inverse Adaptive Directional Lifting-Based Discrete Wavelet Transform is performed to reconstruct the four subband images to a resulting image with original size. Finally, a global binarization method, Otsu’s method, is applied to transform a gray scale image to a binary image as the output result. The results show that the difference in operation time of this work between a personal computer (PC) and Raspberry Pi is little. Therefore, the proposed cost-efficient document image system which performed on Raspberry Pi embedded platform has the same performance and obtains the same results as those performed on a PC.


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