Human Mental Condition Detection by EEG Signal and Time-Frequency Domain Analysis for Behavior

Islam, Md. Ashfaqul; Wami, Md. Rubaiat Zaman; Hayder, Md. Rifat

doi:10.1109/icsct53883.2021.9642653

Cited by 1 publication

(2 citation statements)

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“…To extend the feature extraction, we also employed non-linear analysis by extracting the Poincare plot parameters, pSD1 and pSD2 [22]. The heart rate activity features were presented in equations ( 4) - (8).…”

Section: F Signal Processing and Features Extractionmentioning

confidence: 99%

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Analysis of Multimodal Biosignals during Surprise Conditions Correlates with Psychological Traits

Setiawan,

Miladiyah,

Nuryadi

et al. 2024

j.electron.electromedical.eng.med.inform

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Surprise can simultaneously represent bad or good, pleasant or unpleasant, with the same experiences since understanding how humans' physiological qualities link with their emotional or mental health is required. We conducted quantitative research to concisely correlate mental stress and emotional issues by measuring brain activity, breathing, and heart rate in real time while executing specialized audio-visual stimulation to elicit a surprise event. We evaluated the frequency and temporal domain characteristics to determine if physiological measurements matched biochemical metrics and subjective stress assessments during the elicit surprise condition experiment. We discovered that the brain is still preferable to most in recognizing a human's psychological changes over a short period of time. The temporal (T3) (r = 0.544, p = 0.005) and frontal (Fz) (r = 0.519, p = 0.008) regions were shown to correlate with salivary amylase activity. In comparison to other channels, there was a negative association between stress perception and the occipital site (O1, r = -0.618, p = 0.001). We also found that heart rate variability activity correlates with arousal perception. By looking at specific multimodal biosignals, it is possible to understand human psychological traits by recording specific physiological signals for daily mental health monitoring.

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Section: F Signal Processing and Features Extractionmentioning

confidence: 99%

“…Most previous studies have reported biosignals' effectiveness in classifying emotions and stress with various accuracies [6][7][8]. However, the main issues are still how we can deeply understand specific psychological characteristics rather than binary or classification problems.…”

Section: Introductionmentioning

confidence: 99%