Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts

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Decoding attentional states for neurofeedback : Mindfulness vs. wandering thoughts. / Zhigalov, A.; Heinilä, E.; Parviainen, T.; Parkkonen, L.; Hyvärinen, A.

julkaisussa: NeuroImage, Vuosikerta 185, 15.01.2019, s. 565-574.

Tutkimustuotos: Lehtiartikkelivertaisarvioitu

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Zhigalov, A. ; Heinilä, E. ; Parviainen, T. ; Parkkonen, L. ; Hyvärinen, A. / Decoding attentional states for neurofeedback : Mindfulness vs. wandering thoughts. Julkaisussa: NeuroImage. 2019 ; Vuosikerta 185. Sivut 565-574.

Bibtex - Lataa

@article{1b04df72d1254614864e145e7adb69c8,
title = "Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts",
abstract = "Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem. In this study, we used magnetoencephalography (MEG) to acquire brain activity during mindfulness meditation and thought-inducing tasks mimicking wandering thoughts. We used a novel real-time feature extraction to decode the mindfulness, i.e., to discriminate it from the thought-inducing tasks. The key methodological novelty of our approach is usage of MEG power spectra and functional connectivity of independent components as features underlying mindfulness states. Performance was measured as the classification accuracy on a separate session but within the same subject. We found that the spectral- and connectivity-based classification approaches allowed discriminating mindfulness and thought-inducing tasks with an accuracy around 60{\%} compared to the 50{\%} chance-level. Both classification approaches showed similar accuracy, although the connectivity approach slightly outperformed the spectral one in a few cases. Detailed analysis showed that the classification coefficients and the associated independent components were highly individual among subjects and a straightforward transfer of the coefficients over subjects provided near chance-level classification accuracy. Thus, discriminating between mindfulness and wandering thoughts seems to be possible, although with limited accuracy, by machine learning, especially on the subject-level. Our hope is that the developed spectral- and connectivity-based decoding methods can be utilized in real-time neurofeedback to decode mindfulness states from ongoing neuronal activity, and hence, provide a basis for improved, individualized mindfulness training.",
keywords = "Machine learning, Magnetoencephalography, Mindfulness, Neurofeedback, NETWORK, EEG, MIND, CLASSIFICATION, EXPERIENCE, MEG",
author = "A. Zhigalov and E. Heinil{\"a} and T. Parviainen and L. Parkkonen and A. Hyv{\"a}rinen",
year = "2019",
month = "1",
day = "15",
doi = "10.1016/j.neuroimage.2018.10.014",
language = "English",
volume = "185",
pages = "565--574",
journal = "NeuroImage",
issn = "1053-8119",

}

RIS - Lataa

TY - JOUR

T1 - Decoding attentional states for neurofeedback

T2 - Mindfulness vs. wandering thoughts

AU - Zhigalov, A.

AU - Heinilä, E.

AU - Parviainen, T.

AU - Parkkonen, L.

AU - Hyvärinen, A.

PY - 2019/1/15

Y1 - 2019/1/15

N2 - Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem. In this study, we used magnetoencephalography (MEG) to acquire brain activity during mindfulness meditation and thought-inducing tasks mimicking wandering thoughts. We used a novel real-time feature extraction to decode the mindfulness, i.e., to discriminate it from the thought-inducing tasks. The key methodological novelty of our approach is usage of MEG power spectra and functional connectivity of independent components as features underlying mindfulness states. Performance was measured as the classification accuracy on a separate session but within the same subject. We found that the spectral- and connectivity-based classification approaches allowed discriminating mindfulness and thought-inducing tasks with an accuracy around 60% compared to the 50% chance-level. Both classification approaches showed similar accuracy, although the connectivity approach slightly outperformed the spectral one in a few cases. Detailed analysis showed that the classification coefficients and the associated independent components were highly individual among subjects and a straightforward transfer of the coefficients over subjects provided near chance-level classification accuracy. Thus, discriminating between mindfulness and wandering thoughts seems to be possible, although with limited accuracy, by machine learning, especially on the subject-level. Our hope is that the developed spectral- and connectivity-based decoding methods can be utilized in real-time neurofeedback to decode mindfulness states from ongoing neuronal activity, and hence, provide a basis for improved, individualized mindfulness training.

AB - Neurofeedback requires a direct translation of neuronal brain activity to sensory input given to the user or subject. However, decoding certain states, e.g., mindfulness or wandering thoughts, from ongoing brain activity remains an unresolved problem. In this study, we used magnetoencephalography (MEG) to acquire brain activity during mindfulness meditation and thought-inducing tasks mimicking wandering thoughts. We used a novel real-time feature extraction to decode the mindfulness, i.e., to discriminate it from the thought-inducing tasks. The key methodological novelty of our approach is usage of MEG power spectra and functional connectivity of independent components as features underlying mindfulness states. Performance was measured as the classification accuracy on a separate session but within the same subject. We found that the spectral- and connectivity-based classification approaches allowed discriminating mindfulness and thought-inducing tasks with an accuracy around 60% compared to the 50% chance-level. Both classification approaches showed similar accuracy, although the connectivity approach slightly outperformed the spectral one in a few cases. Detailed analysis showed that the classification coefficients and the associated independent components were highly individual among subjects and a straightforward transfer of the coefficients over subjects provided near chance-level classification accuracy. Thus, discriminating between mindfulness and wandering thoughts seems to be possible, although with limited accuracy, by machine learning, especially on the subject-level. Our hope is that the developed spectral- and connectivity-based decoding methods can be utilized in real-time neurofeedback to decode mindfulness states from ongoing neuronal activity, and hence, provide a basis for improved, individualized mindfulness training.

KW - Machine learning

KW - Magnetoencephalography

KW - Mindfulness

KW - Neurofeedback

KW - NETWORK

KW - EEG

KW - MIND

KW - CLASSIFICATION

KW - EXPERIENCE

KW - MEG

UR - http://www.scopus.com/inward/record.url?scp=85055037319&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2018.10.014

DO - 10.1016/j.neuroimage.2018.10.014

M3 - Article

VL - 185

SP - 565

EP - 574

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -

ID: 29118382