Authors:
Alejandro A. A. Torres-García
;
Luis Alfredo Moctezuma
and
Marta Molinas
Affiliation:
Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU), Gløshaugen, O. S. Bragstads plass 2, Trondheim, Norway
Keyword(s):
EEG Signals, Brain-Computer Interfaces (BCI), Classification, Color Exposure, Idle States, SVM, Random Forest, Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD).
Abstract:
Self-paced Brain-Computer Interfaces (BCIs) are desirable for allowing the BCI’s user to control a BCI without a cue to indicate him/her when to send a command or message. As a first step towards a self-paced color-based BCI, we assessed if a machine learning algorithm can learn to distinguish between primary color exposure and idle state. In this paper, we record and analyze the EEG signals from 18 subjects for assessing the feasibility of distinguishing between color exposure and idle states. Specifically, we compare separately the performances obtained in the classification of two different types of idle states (one relaxation-related and another attention-related) and color exposure. We characterize the signals using two different ways based on discrete wavelet transform and Empirical Mode Decomposition (EMD), respectively. We trained and tested two different classifiers, support vector machine (SVM) and random forest. The outcomes provide experimental evidence that a machine lea
rning algorithm can distinguish between the two classes (exposure to primary colors and idle states), regardless of the kind of idle state analyzed. The more consistent outcomes were obtained using EMD-based features with accuracies of 92.3% and 91.6% (considering a break and an attention-related task as the idle states). Also, when we discard the epochs’ onset the performances were 91.8% and 94.6%, respectively.
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