| Full text | |
| Author(s): |
Pinto, Adam H. M.
;
Nardari, Guilherme V.
;
Romero, Roseli A. F.
;
Mijan, Marco A. M.
;
DoNascimento, TP
;
Colombini, EL
;
DeBrito, AV
;
Garcia, LTD
;
Sa, STD
;
Goncalves, LMG
Total Authors: 10
|
| Document type: | Journal article |
| Source: | 15TH LATIN AMERICAN ROBOTICS SYMPOSIUM 6TH BRAZILIAN ROBOTICS SYMPOSIUM 9TH WORKSHOP ON ROBOTICS IN EDUCATION (LARS/SBR/WRE 2018); v. N/A, p. 7-pg., 2018-01-01. |
| Abstract | |
Brain Computer Interface systems allow communication between the user and the computer using information provided from brain waves. This control is possible through the interpretation of electrical records and allows the system to analyze the conditions of human attention and engagement in a task. One of the problems in attention during learning is the moment when the student makes a mistake, which is possible to catch through a specific signal of the brain using those systems. However, this data is quite complicated and noisy, and low accuracy was found so far. In this paper, it was compared methods to extract features and classify this signal to enhance the accuracy in error detection. Both wavelets and Fourier transform are used to feature extraction and a MultiLayer Perceptron as compared to Deep Learning Neural Network in classification. The results shown that wavelet extraction are better to extract the error and Deep Learning approach are better classifier. (AU) | |
| FAPESP's process: | 14/50851-0 - INCT 2014: National Institute of Science and Technology for Cooperative Autonomous Systems Applied in Security and Environment |
| Grantee: | Marco Henrique Terra |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 17/17444-0 - Plantation monitoring using heterogeneous robots |
| Grantee: | Guilherme Vicentim Nardari |
| Support Opportunities: | Scholarships in Brazil - Doctorate (Direct) |