Analyzing EEG Signals Using Decision Trees: A Study of Modulation of Amplitude.
Comput Intell Neurosci
; 2020: 3598416, 2020.
Article
em En
| MEDLINE
| ID: mdl-32695151
An electroencephalogram (EEG) is a test that records electrical activity of the brain using electrodes attached to the scalp, and it has recently been used in conjunction with BMI (Brain-Machine Interface). Currently, the analysis of the EEG is visual, using graphic tools such as topographic maps. However, this analysis can be very difficult, so in this work, we apply a methodology of EEG analysis through data mining to analyze two different band frequencies of the brain signals (full band and Beta band) during an experiment where visually impaired and sighted individuals recognize spatial objects through the sense of touch. In this paper, we present details of the proposed methodology and a case study using decision trees to analyze EEG signals from visually impaired and sighted individuals during the execution of a spatial ability activity. In our experiment, the hypothesis was that sighted individuals, even if they are blindfolded, use vision to identify objects and that visually impaired people use the sense of touch to identify the same objects.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Pessoas com Deficiência Visual
/
Percepção do Tato
Tipo de estudo:
Health_economic_evaluation
Limite:
Humans
Idioma:
En
Revista:
Comput Intell Neurosci
Assunto da revista:
INFORMATICA MEDICA
/
NEUROLOGIA
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Brasil
País de publicação:
Estados Unidos