Predicting Perceptual Decision-Making Errors Using EEG and Machine Learning

Alisa Batmanova, Alexander Kuc, Vladimir Maksimenko, Andrey Savosenkov, Nikita Grigorev, Susanna Gordleeva, Victor Kazantsev, Sergey Korchagin and Alexander E. Hramov ()
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Alisa Batmanova: Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 125993 Moscow, Russia
Alexander Kuc: Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
Vladimir Maksimenko: Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
Andrey Savosenkov: Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
Nikita Grigorev: Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
Susanna Gordleeva: Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
Victor Kazantsev: Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia
Sergey Korchagin: Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, 125993 Moscow, Russia
Alexander E. Hramov: Baltic Center for Artificial Intelligence and Neurotechnology, Immanuel Kant Baltic Federal University, 236016 Kaliningrad, Russia

Mathematics, 2022, vol. 10, issue 17, 1-12

Abstract: We trained an artificial neural network (ANN) to distinguish between correct and erroneous responses in the perceptual decision-making task using 32 EEG channels. The ANN input took the form of a 2D matrix where the vertical dimension reflected the number of EEG channels and the horizontal one—to the number of time samples. We focused on distinguishing the responses before their behavioural manifestation; therefore, we utilized EEG segments preceding the behavioural response. To deal with the 2D input data, ANN included a convolutional procedure transforming a 2D matrix into the 1D feature vector. We introduced three types of convolution, including 1D convolutions along the x - and y -axes and a 2D convolution along both axes. As a result, the F 1 -score for erroneous responses was above 88%, which confirmed the model’s ability to predict perceptual decision-making errors using EEG. Finally, we discussed the limitations of our approach and its potential use in the brain-computer interfaces to predict and prevent human errors in critical situations.

Keywords: perceptual decision-making; ambiguous stimuli; electroencephalograms; perceptual error; machine learning (ML) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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