Introduction
Background
Wave | State of consciousness | Frequency (Hz) | Psychological state |
---|---|---|---|
\(\delta\) | Unconscious level | 1–3 | \(\delta\) usually appear in the deep sleep state of adults and are also the most important brain waves in infants \(\delta\) are often used as a basis for sleep therapy, such as [12, 13], which detects the amount of energy released by the patient’s Delta wave and determines whether it has entered a deep sleep state \(\delta\) are needed to regain physical sleep |
\(\theta\) | Subconscious level | 4–7 | \(\theta\) usually appear in a shallow sleep state, also as a meditative state, referring to the brain waves that manifest when they first fall asleep When we perform memory, perceptual, and emotional-related behaviors, \(\theta\) are higher because the brain acts as a memory, and behavior is trained as an unconscious action |
\(\alpha\) | |||
Low \(\alpha\) | Consciousness and subconscious level | 8–9 | It is the main brain wave of normal relaxed adults Consciousness gradually moves towards ambiguity It’s also about relaxation and freedom The \(\upalpha\) is related to the active activity of the brain [14], and when the energy released by the Alpha Wave is strong, it represents the brain wave in the best state of learning and thinking |
High \(\alpha\) | 10–12 | ||
\(\beta\) | |||
Low \(\beta\) | Level of consciousness | 13–17 | \(\beta\) are associated with concentration, and when beta waves emit higher energy, they represent a positive increase in attention |
High \(\beta\) | 18–30 | ||
\(\gamma\) | |||
Low \(\gamma\) | Level of consciousness | 31–40 | \(\upgamma\) are associated with happiness When the energy released by the Gamma Wave is higher, it represents a higher sense of happiness. \(\upgamma\) are associated with reducing stress When the energy released by the \(\upgamma\) is higher, the more pressure is released |
High \(\gamma\) | 41–50 |
- Fear: The instinctive behavior of a common creature or person in the face of danger in life. Fear can cause changes in the heart rate, elevated blood pressure, night sweats, tremors and other physiological phenomena, and even the symptoms of cardiac arrest shock.
- Anger: Emotional agitation, being violated, disrespected, or wrongly treated, can lead to instinctive self-preparedness for its combat response. Emotional anger, micro-lukewarm, resentment, inequality, irritability, hostility, and, more extreme, hatred and violence.
- Sadness: It is usually the psychological frustration of failure, the mood is lower meaning. Emotions are sad, depressed, self-pity, loneliness, depression, despair, and morbid severe melancholy.
- Joy: Emotion is the psychological state of pleasure, with the meaning of joy, contentment, self-satisfaction, pride, and excitement in the senses.
- Surprise: By unexpected stimulation in the living environment, resulting in temporary action to stop.
- Disgust: Facing negative stimuli in the environment.
Evaluation metrics
Classifier of brain wave emotion classification
Linear classifier
Schemes | Preprocessing | Feature extraction | Feature smoothing | Classification | Emotion states | Accuracy |
---|---|---|---|---|---|---|
Method by Li et al. | FT | CSP | liner-SVM | Happiness and sadness | 93.5% | |
Method by Murugappan et al. | Surface Laplacian filtering Zero mean unit variance | Wavelet transform | KNN and LDA | Disgust, happy, surprise, fear and neutral | KNN: 77.68% LDA: 73.5% | |
Method by Wang et al. | Wavelet transform, PCA, LDA, CFS | LDS | liner-SVM | Negative and positive | 87.53% | |
Method by Petrantonakis et al. | Statistical values, wavelet transform and HOC | QDA, KNN, MD and SVMs | Happiness, surprise, anger, fear, disgust and sadness | QDA: 62.3% SVMs: 83.33% MD: 44.90% KNN: 34.60% | ||
Method by Duan et al. | DE, DASM, RASM and ES | LDS PCA and MRMR | liner-SVM and kNN | Negative and positive | liner-SVM: 74.10% kNN: 69.24% |
Method by Li et al. [15]
Method by Murugappan et al. [11]
Method by Wang et al. [18]
Method by Petrantonakis et al. [19]
Method by Duan et al. [22]
Nonlinear classifier
Schemes | Preprocessing | Feature extraction | Feature smoothing | Classification | Emotion states | Accuracy |
---|---|---|---|---|---|---|
Method by Liu et al. | FD | Sad, frustrated, fear, satisfied, pleasant and happy | ||||
Method by Liu et al. | ResNets, LFCC | KNN, SVM, LR, RF, NB, DT and FC | Anger, joy, sadness and pleasure | KNN: 89.72% | ||
Method by Zheng et al. | DE, DASM, RASM | DBN, SVM, LR and KNN | Positive, neutral and negative | DBN: 86.08% SVM: 83.99% LR: 82.70% KNN: 72.60% | ||
Method by Dan Nie et al. | FFT | LDS | SVM | Negative and positive | SVM: 87.53 | |
Method by Zheng et al. | PSD, DE, DASM, RASM, ASM and DCAU | MRMR | KNN, LR, SVM and GELM | Negative, positive and neutral | KNN: 70.43% LR: 84.08% SVM: 78.21 GELM: 91.07% |