Abstract
Brain wave signals allow communication between user and computer in a system called Brain-Computer Interface. Signal processing can detect attention, engagement, and errors in a task. Error-Related Potentials (ErrP) can be extracted from brain signals with noise, however, it is quite complicated to be recognized and accurate. This paper presents a new database, using gaming and a humanoid robot to induce the occurrence of user errors and methods to extract signal features. A Haar wavelet was used to feature extraction, and a MultiLayer Perceptron (MLP) and a Convolutional Neural Network (CNN) to signal classification. Several experiments are presented demonstrating that wavelet extraction outperformed Fourier transform to extract the error and MLP performed a consistent accuracy.
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Notes
- 1.
Ethical Committee approval CAAE: 79649717.0.0000.5292.
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Pinto, A., Nardari, G., Mijam, M., Morya, E., Romero, R. (2019). A Serious Game to Build a Database for ErrP Signal Recognition. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_16
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