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Predicting an EEG-Based hypnotic time estimation with non-linear kernels of support vector machine algorithm

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Abstract

Our ability to measure time is vital for daily life, technology use, and even mental health; however, separating pure time perception from other mental processes (like emotions) is a research challenge requiring precise tests to isolate and understand brain activity solely related to time estimation. To address this challenge, we designed an experiment utilizing hypnosis alongside electroencephalography (EEG) to assess differences in time estimation, namely underestimation and overestimation. Hypnotic induction is designed to reduce awareness and meta-awareness, facilitating a detachment from the immediate environment. This reduced information processing load minimizes the need for elaborate internal thought during hypnosis, further simplifying the cognitive landscape. To predict time perception based on brain activity during extended durations (5 min), we employed artificial intelligence techniques. Utilizing Support Vector Machines (SVMs) with both radial basis function (RBF) and polynomial kernels, we assessed their effectiveness in classifying time perception-related brain patterns. We evaluated various feature combinations and different algorithms to identify the most accurate configuration. Our analysis revealed an impressive 80.9% classification accuracy for time perception detection using the RBF kernel, demonstrating the potential of AI in decoding this complex cognitive function.

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Data availability

The data used to support the findings of this study are available from the corresponding authors upon request.

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Funding

The present study was extracted from the Hoda Taghilou doctoral dissertation. She has not received any funding for her project.

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Authors and Affiliations

  1. Department of Cognitive Neuroscience, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran

    Hoda Taghilou

  2. Department of Clinical Psychology, Beheshti Hospital, School of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran

    Mazaher Rezaei

  3. Department of Physics, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Iran

    Alireza Valizadeh

  4. Pasargad Institute for Advanced Innovative Solutions (PIAIS), Tehran, Iran

    Alireza Valizadeh

  5. Department of Psychology, Faculty of Education and Psychology, University of Tabriz, Tabriz, Iran

    Touraj Hashemi Nosratabad

  6. Department of Neuroscience, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran

    Mohammad Ali Nazari

  7. Department of Artificial Intelligence in Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran

    Mohammad Ali Nazari

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  1. Hoda Taghilou
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  2. Mazaher Rezaei
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  3. Alireza Valizadeh
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  4. Touraj Hashemi Nosratabad
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  5. Mohammad Ali Nazari
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Contributions

MAN conceptualized the study. HT, MAN, MR, and THN designed the study. HT recruited participants and recorded EEG. MR conducted all the hypnotic inductions and the assessments of hypnotic susceptibility. AV was the supervisor of machine learning. Hoda Taghilou wrote the first draft of the manuscript and analyzed the data in collaboration with MAN and AV. All authors commented on previous versions of the manuscript and approved the final manuscript.

Corresponding authors

Correspondence to Mazaher Rezaei or Mohammad Ali Nazari.

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Ethical Approval

This study received ethics approval from the ethics committee of Tabriz University with the code IR.TABRIZU.REC.1399.070.

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Taghilou, H., Rezaei, M., Valizadeh, A. et al. Predicting an EEG-Based hypnotic time estimation with non-linear kernels of support vector machine algorithm. Cogn Neurodyn (2024). https://doi.org/10.1007/s11571-024-10088-y

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