Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification
Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive op...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-11-01
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| Series: | Frontiers in Systems Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnsys.2021.716897/full |
| _version_ | 1818843746560638976 |
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| author | Alexander Kuc Sergey Korchagin Vladimir A. Maksimenko Vladimir A. Maksimenko Vladimir A. Maksimenko Natalia Shusharina Alexander E. Hramov Alexander E. Hramov Alexander E. Hramov |
| author_facet | Alexander Kuc Sergey Korchagin Vladimir A. Maksimenko Vladimir A. Maksimenko Vladimir A. Maksimenko Natalia Shusharina Alexander E. Hramov Alexander E. Hramov Alexander E. Hramov |
| author_sort | Alexander Kuc |
| collection | DOAJ |
| description | Incorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group of subjects. As a result, the pre-trained classifier achieved 74% accuracy on the data of newly recruited subjects. Analysis of the literature suggested that a pre-trained classifier could help naive users to start using BCI bypassing training and further increased accuracy during the feedback session. Thus, our results contribute to using BCI during paralysis or limb amputation when there is no explicit user-generated kinematic output to properly train a decoder. In machine learning, our approach may facilitate the development of transfer learning (TL) methods for addressing the cross-subject problem. It allows extracting the interpretable feature subspace from the source data (the representative group of subjects) related to the target data (a naive user), preventing the negative transfer in the cross-subject tasks. |
| first_indexed | 2024-12-19T05:02:46Z |
| format | Article |
| id | doaj.art-5743091b38de46a7bf8fadbfca948492 |
| institution | Directory Open Access Journal |
| issn | 1662-5137 |
| language | English |
| last_indexed | 2024-12-19T05:02:46Z |
| publishDate | 2021-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Systems Neuroscience |
| spelling | doaj.art-5743091b38de46a7bf8fadbfca9484922022-12-21T20:35:01ZengFrontiers Media S.A.Frontiers in Systems Neuroscience1662-51372021-11-011510.3389/fnsys.2021.716897716897Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms ClassificationAlexander Kuc0Sergey Korchagin1Vladimir A. Maksimenko2Vladimir A. Maksimenko3Vladimir A. Maksimenko4Natalia Shusharina5Alexander E. Hramov6Alexander E. Hramov7Alexander E. Hramov8Center for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, RussiaDepartment of Data Analysis and Machine Learning, Financial University Under the Government of the Russian Federation, Moscow, RussiaCenter for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, RussiaInstitute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, RussiaNeuroscience and Cognitive Technology Laboratory, Innopolis University, Innopolis, RussiaCenter for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, RussiaCenter for Neurotechnology and Machine Learning, Immanuel Kant Baltic Federal University, Kaliningrad, RussiaInstitute of Information Technologies, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, RussiaNeuroscience and Cognitive Technology Laboratory, Innopolis University, Innopolis, RussiaIncorporating brain-computer interfaces (BCIs) into daily life requires reducing the reliance of decoding algorithms on the calibration or enabling calibration with the minimal burden on the user. A potential solution could be a pre-trained decoder demonstrating a reasonable accuracy on the naive operators. Addressing this issue, we considered ambiguous stimuli classification tasks and trained an artificial neural network to classify brain responses to the stimuli of low and high ambiguity. We built a pre-trained classifier utilizing time-frequency features corresponding to the fundamental neurophysiological processes shared between subjects. To extract these features, we statistically contrasted electroencephalographic (EEG) spectral power between the classes in the representative group of subjects. As a result, the pre-trained classifier achieved 74% accuracy on the data of newly recruited subjects. Analysis of the literature suggested that a pre-trained classifier could help naive users to start using BCI bypassing training and further increased accuracy during the feedback session. Thus, our results contribute to using BCI during paralysis or limb amputation when there is no explicit user-generated kinematic output to properly train a decoder. In machine learning, our approach may facilitate the development of transfer learning (TL) methods for addressing the cross-subject problem. It allows extracting the interpretable feature subspace from the source data (the representative group of subjects) related to the target data (a naive user), preventing the negative transfer in the cross-subject tasks.https://www.frontiersin.org/articles/10.3389/fnsys.2021.716897/fullEEG topogramsconvolutional neural networkCNNambiguous stimulipre-trained decoder |
| spellingShingle | Alexander Kuc Sergey Korchagin Vladimir A. Maksimenko Vladimir A. Maksimenko Vladimir A. Maksimenko Natalia Shusharina Alexander E. Hramov Alexander E. Hramov Alexander E. Hramov Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification Frontiers in Systems Neuroscience EEG topograms convolutional neural network CNN ambiguous stimuli pre-trained decoder |
| title | Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification |
| title_full | Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification |
| title_fullStr | Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification |
| title_full_unstemmed | Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification |
| title_short | Combining Statistical Analysis and Machine Learning for EEG Scalp Topograms Classification |
| title_sort | combining statistical analysis and machine learning for eeg scalp topograms classification |
| topic | EEG topograms convolutional neural network CNN ambiguous stimuli pre-trained decoder |
| url | https://www.frontiersin.org/articles/10.3389/fnsys.2021.716897/full |
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