Authors:
Carlos Carreiras
1
;
André Lourenço
2
;
Helena Aidos
1
;
Hugo Plácido da Silva
1
and
Ana Fred
1
Affiliations:
1
Instituto de Telecomunicações, Portugal
;
2
Instituto Superior de Engenharia de Lisboa and Instituto de Telecomunicações, Portugal
Keyword(s):
Physiological Computing, Attention, ECG, EEG, Unsupervised Learning, Cluster Validation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Supervised and Unsupervised Learning
;
Theory and Methods
Abstract:
The electroencephalogram (EEG) signal, acquired on the scalp, has been extensively used to understand cognitive function, and in particular attention. However, this type of signal has several drawbacks in a context of Physiological Computing, being susceptible to noise and requiring the use of impractical head-mounted apparatuses, which impacts normal human-computer interaction. For these reasons, the electrocardiogram (ECG) has been proposed as an alternative source to assess emotion, which is also continuously available, and related with the psychophysiological state of the subject. In this paper we present a study focused on the morphological analysis of the ECG signal acquired from subjects performing a task demanding high levels of attention. The analysis is made using various unsupervised learning techniques, which are validated against evidence found in a previous study by our team, where EEG signals collected for the same task exhibit distinct patterns as the subjects progres
s in the task.
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