Are time series of single EEG channels correlated to short-term subjective well-being?
Poster No:
1334
Submission Type:
Abstract Submission
Authors:
Betty Wutzl1, Kenji Leibnitz2, Yuichi Ohsita1, Masayuki Murata1
Institutions:
1Osaka University, Suita, Osaka, 2National Institute of Information and Communications Technology, and Osaka University, Suita, Osaka
First Author:
Co-Author(s):
Kenji Leibnitz
National Institute of Information and Communications Technology, and Osaka University
Suita, Osaka
National Institute of Information and Communications Technology, and Osaka University
Suita, Osaka
Introduction:
In recent years, much attention has been paid to the well-being of individuals. Many studies perform psychological or psychiatric interventions to improve Subjective Well-Being (SWB), and a correlation between electroencephalography (EEG) frontal alpha asymmetry (FAA) and SWB has been reported, e.g., (Urry et al., 2004; Xu et al., 2018). We focused on short time scales (60 or 30 seconds) of SWB and tried to influence it by environmental conditions. In our previous work (Wutzl et al., 2023), we concluded that the correlation between FAA and SWB also holds for short-term changes. Moreover, we investigated different sensor locations and frequency bands and found that while FAA has the strongest correlation with SWB, other frequency bands and asymmetries of different sensors may also provide insights into short-term SWB (Wutzl et al., 2024). We now focus on the EEG time series itself. Instead of calculating an asymmetry value, we analyze if the time series of a single sensor already provides insights into short-term SWB.
Methods:
The experiments were conducted in 2022. We recorded EEG with an Emotiv EPOC X headset (EMOTIV, San Francisco, USA) and the participants orally reported their SWB every 30 seconds on a scale from 1 (worst) to 10 (best). We performed six sessions per participant and changed the temperature and humidity settings of the room in each session to influence the participant's SWB. We followed HAPPE (Gabard-Durnam et al., 2018), using EEGLAB (Delorme & Makeig, 2004) and MARA (Winkler et al., 2011, 2014) for the EEG data preprocessing. The data were filtered into EEG frequency bands, namely, delta (0.5–3 Hz), theta (4–7 Hz), alpha (8–13 Hz), beta (14-30 Hz), or gamma (31-55 Hz), and the 10 s time series before was labeled with the corresponding SWB value. This interval was chosen because, in our previous work, we found it to show the highest correlation to short-term SWB. Since the SWB values in the dataset were imbalanced, we performed a k-nearest neighbor (kNN) algorithm with k=1 for classification. This was performed for each participant and each channel individually. Thus, for each subject we kept all 10 s time series of a single channel except for one with their SWB labels as input and classified the remaining time series with the kNN approach. This was repeated so that each time series was left out once. Then, the mean squared error (MSE) between the SWB label of the classified time series to the original label was calculated. After doing this for all channels, we ranked the channels for each subject according to their MSE. We used MSE as measure because misclassifying an SWB value as one close to its actual value (e.g., SWB of 3 misclassified as 4) is less severe than misclassifying it as a distant value (e.g., SWB of 3 misclassified as 9).
Results:
We recorded data from 30 participants (16 male, 2 left-handed, age: 22.3 ± 4.2 years). The data from 2 participants had to be excluded from the analysis because they reported 2 or less different SWB values. The following heatmap figure provides an overview of the results calculated in the alpha band. Similar heatmaps were also produced for the other EEG frequency bands.
The results show that for the delta and theta bands, channel T7 is the one most often chosen as the one which gives the best results. For the alpha, beta, and gamma bands, the sensors FC6, AF4, and F4 were chosen, respectively.
The results show that for the delta and theta bands, channel T7 is the one most often chosen as the one which gives the best results. For the alpha, beta, and gamma bands, the sensors FC6, AF4, and F4 were chosen, respectively.
·Figure
Conclusions:
When it comes to measuring short-term SWB, it may be possible to use a single sensor. However, our results indicate that the best sensor varies a lot between subjects and different frequency bands. Considering higher frequency bands frontal sensors resulted in the most significant sensors while for lower frequency bands a sensor over the temporal region showed importance. This result is also consistent with our previous work (Wutzl et al., 2024), where we conclude that focusing only on frontal areas and the alpha band may not reveal the full picture of short-time SWB processes in the brain.
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other
Modeling and Analysis Methods:
Classification and Predictive Modeling 2
EEG/MEG Modeling and Analysis 1
Task-Independent and Resting-State Analysis
Keywords:
Data analysis
Electroencephaolography (EEG)
Modeling
Other - Subjective Well Being
1|2Indicates the priority used for review
Abstract Information
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2. Gabard-Durnam, L. J., Mendez Leal, A. S., Wilkinson, C. L., & Levin, A. R. (2018). The Harvard Automated Processing Pipeline for Electroencephalography (HAPPE): Standardized Processing Software for Developmental and High-Artifact Data. Frontiers in Neuroscience, 12. https://doi.org/10.3389/fnins.2018.00097
3. Urry, H. L., Nitschke, J. B., Dolski, I., Jackson, D. C., Dalton, K. M., Mueller, C. J., Rosenkranz, M. A., Ryff, C. D., Singer, B. H., & Davidson, R. J. (2004). Making a life worth living: Neural correlates of well-being. Psychological Science, 15(6), 367–372. https://doi.org/10.1111/j.0956-7976.2004.00686.x
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6. Wutzl, B., Leibnitz, K., Kominami, D., Ohsita, Y., Kaihotsu, M., & Murata, M. (2023). Analysis of the Correlation between Frontal Alpha Asymmetry of Electroencephalography and Short-Term Subjective Well-Being Changes. Sensors (Basel, Switzerland), 23(15), 7006. https://doi.org/10.3390/s23157006
7. Wutzl, B., Leibnitz, K., & Murata, M. (2024). An Analysis of the Correlation between the Asymmetry of Different EEG-Sensor Locations in Diverse Frequency Bands and Short-Term Subjective Well-Being Changes. Brain Sciences, 14(3), Article 3. https://doi.org/10.3390/brainsci14030267
8. Xu, Y.-Y., Feng, Z.-Q., Xie, Y.-J., Zhang, J., Peng, S.-H., Yu, Y.-J., & Li, M. (2018). Frontal Alpha EEG Asymmetry Before and After Positive Psychological Interventions for Medical Students. Frontiers in Psychiatry, 9. https://doi.org/10.3389/fpsyt.2018.00432