Human stress classification using EEG signals in response to music tracks

Asif, Anum; Majid, Muhammad; Anwar, Syed Muhammad

doi:10.1016/j.compbiomed.2019.02.015

Cited by 157 publications

(105 citation statements)

References 57 publications

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“…The human body’s response to stress can vary, and symptoms can be considered dangerous if their duration is chronic. Persistent stress affects the body both physically and mentally [ 3 , 7 ]. One disease that stress contributes to is the cardiovascular disease, which aggravates by increasing blood pressure which is caused by a high level of adrenaline.…”

Section: Stress Under the Influence Of Musicmentioning

confidence: 99%

“…Brain–computer interface technology can be a helpful tool in the fight against stress. Hans Hugon Selye, who introduced the concept of stress, defines it as, “...a non-specific response of the body to a challenging situation.” Stress has been recognized as a state in which a person feels burdened with the requirements or a situation he/she finds themselves in [ 3 ]. One of the most common bases for this state, the so-called stressors, are moments which require acting under pressure and are observed primarily in interpersonal contacts, the workplace and situations associated with finances [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study

2020

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Everyone experiences stress at certain times in their lives. This feeling can motivate, however, if it persists for a prolonged period, it leads to negative changes in the human body. Stress is characterized, among other things, by increased blood pressure, increased pulse and decreased alpha-frequency brainwave activity. An overview of the literature indicates that music therapy can be an effective and inexpensive method of improving these factors. The objective of this study was to analyze the impact of various types of music on stress level in subjects. The conducted experiment involved nine females, aged 22. All participants were healthy and did not have any neurological or psychiatric disorders. The test included four types of audio stimuli: silence (control sample), rap, relaxing music and music triggering an autonomous sensory meridian response (ASMR) phenomenon. The impact of individual sound types was assessed using data obtained from four sources: a fourteen-channel electroencephalograph, a blood pressure monitor, a pulsometer and participant’s subjective stress perception. The conclusions from the conducted study indicate that rap music negatively affects the reduction of stress level compared to the control group (p < 0.05), whereas relaxing music and ASMR calms subjects much faster than silence (p < 0.05).

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Section: Stress Under the Influence Of Musicmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study

2020

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“…The Welch method was used to extract power spectral densities with a window length of 128 samples with 50 percent overlap. For feature extraction, power spectral densities of different neural oscillations namely, delta (1-3 Hz), theta (4-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), gamma (25-43 Hz), slow (4-13 Hz), and low beta (13)(14)(15)(16)(17) were computed from each channel. Relative gamma waves were computed by taking the ratio of slow and gamma waves.…”

Section: Feature Extraction and Selectionmentioning

confidence: 99%

“…Recently, wearable systems were developed that can record electro-physiological signals (such as EEG and heart rate variability) to detect acute stress [12]. EEG is one of the most common source of information for studying brain function [13][14][15][16][17]. The oscillations generated by the variation of electric potential in the brain are recorded using low resistance electrodes placed on the human scalp [18].…”

Section: Introductionmentioning

confidence: 99%

EEG Based Classification of Long-Term Stress Using Psychological Labeling

Saeed

Anwar

Khalid

et al. 2020

Sensors

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Stress research is a rapidly emerging area in the field of electroencephalography (EEG) signal processing. The use of EEG as an objective measure for cost effective and personalized stress management becomes important in situations like the nonavailability of mental health facilities. In this study, long-term stress was classified with machine learning algorithms using resting state EEG signal recordings. The labeling for the stress and control groups was performed using two currently accepted clinical practices: (i) the perceived stress scale score and (ii) expert evaluation. The frequency domain features were extracted from five-channel EEG recordings in addition to the frontal and temporal alpha and beta asymmetries. The alpha asymmetry was computed from four channels and used as a feature. Feature selection was also performed to identify statistically significant features for both stress and control groups (via t-test). We found that support vector machine was best suited to classify long-term human stress when used with alpha asymmetry as a feature. It was observed that the expert evaluation-based labeling method had improved the classification accuracy by up to 85.20%. Based on these results, it is concluded that alpha asymmetry may be used as a potential bio-marker for stress classification, when labels are assigned using expert evaluation.

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“…Physical measure for detecting stress could include facial expressions [6] and eye blink rate [7]. Whereas, common physiological measures include electroencephalography (EEG) [8], heart rate (HR), heart rate variability (HRV) [9], galvanic skin response (GSR) [10], and photoplethysmography (PPG) [11]. In literature, different types of physiological human stress measurement methods have been developed.…”

Section: Introductionmentioning

confidence: 99%

Classification of Perceived Human Stress using Physiological Signals

Arsalan

Majid

Anwar

et al. 2019

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of 28 participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used.

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Human stress classification using EEG signals in response to music tracks

Cited by 157 publications

References 57 publications

The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study

The Impact of Different Sounds on Stress Level in the Context of EEG, Cardiac Measures and Subjective Stress Level: A Pilot Study

EEG Based Classification of Long-Term Stress Using Psychological Labeling

Classification of Perceived Human Stress using Physiological Signals

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