Stress Prediction Using Machine-Learning Techniques on Physiological Signals

Mindfulness is an intervention that was used in many psychiatry treatments. In this study, the subject experienced two different states: (1) attention (i.e., listening to a podcast) and (2) mindfulness (i.e., meditation). Twenty-two students were included in a Mindfulness-Based Stress Reduction (MBSR) course with EEG recording sessions on week four and week six. Brain dynamics were investigated to elucidate the complexity and connectivity of the brain. The alpha PSD increased in all brain areas during mindfulness in both weeks. For complexity, Fractal Dimension (FD) increased significantly during meditation in the week six recording. When comparing the FD in the mindfulness state of week four and week six, we also witnessed a significant increase in the following week. The coherence of the interhemispheric frontal and temporal regions increased significantly in both weeks. In conclusion, the subject successfully transferred from attention to mindfulness, demonstrated by the alpha changes when going from podcast to meditating. An enhancement in brain complexity was found, suggesting an augmentation in cognitive function. Finally, the coherence exhibits strengthened connections in the frontal area. 

https://doi.org/10.1016/j.ibneur.2023.03.003

Intermediate effects of mindfulness practice on the brain activity of college students: An EEG study

Contamination of EEG by ocular artifacts (EOG) is the major artifact that reduces the accuracy of applications using Electroencephalogram (EEG) signal. To resolve this issue, Independent Component Analysis (ICA) is a common method to remove EOG artifacts from EEG recordings, by decomposing multichannel EEG signals into maximally Independent Components (ICs). ICs representing ocular activities can be identified visually, then be eliminated to reconstruct EOG-free EEG signals. However, this approach requires prior domain knowledge, and hence, undermine reliability and reproducibility. To address this, our study proposed a method to remove EOG contamination by applying machine learning techniques. We acquired an EEG database of 20 healthy subjects using Alice 5 Polysomnography system to record signals from 12 electrodes. Randomly selected 15-s data segments from EEG channels were used to run ICA, which resulted in 10 ICs. For each IC, we plotted its topography map and labelled whether this IC is “EOG” or “non-EOG”. A total of 612 labelled data points of ICs, topography maps and labels were collected. After applying several classifiers for model training and evaluation using cross-validation, the best classifier, Extremely Randomized Tree, achieved an average accuracy of 92%, precision of 83%, recall of 71%, and F1 score of 76%. In conclusion, the proposed method showed promising results in identifying EOG components and attenuating ocular activity on reconstructed EEG signals. Compared with existing automated solutions, our proposed method only used a small number of channels and had the potential to be applied in real-time applications due to its fast computation.

Automated EOG Removal from EEG Signal Using Independent Component Analysis and Machine Learning Algorithms

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Automated EOG Removal from EEG Signal Using Independent Component Analysis and Machine Learning Algorithms