Abstract
Patients with Autistic Spectrum Discorder are known to have deficits in interpreting others’ intentions from gaze-direction or other social attention. Here, we use electroencephalography data recorded in virtual reality experiments with patients to predict one out of eight objects that was focused on. Correct labels for these objects were known from parallel eye-tracking measurements. We extracted features from the time domain and from Pearson’s correlation and applied both statistical and neuro-inspired supervised machine learning algorithms. Using a multi-layer perceptron, we achieved 65.4% accuracy on the validation data set and 70.0% accuracy on the test data set.
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Acknowledgements
We would like to thank Prof. Dr. Martin Bogdan for discussing preprocessing steps for the given data set, and Marlo Kriegs for evaluating hyperparameter optimization techniques for support vector machines.
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Adama, V.S., Schindler, B., Schmid, T. (2020). Using Time Domain and Pearson’s Correlation to Predict Attention Focus in Autistic Spectrum Disorder from EEG P300 Components. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_230
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DOI: https://doi.org/10.1007/978-3-030-31635-8_230
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