EEG Based Human Emotion Analysis

Garg, Aakanksha; Mankar, Krish; Manhas, Shauryavir Singh; Naik, Isha; Jain, Hrithik; Patil, Harshada K.; Pimpale, Pranjal; Kalathil, Divit; Rahise, Shravani; Poddar, Smiti; Deshmukh, Laukik; Hake, Akash; Talele, Kiran

doi:10.1109/tensymp54529.2022.9864507

Cited by 1 publication

(3 citation statements)

References 7 publications

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“…This waveform shows a high level of processing, for instance thinking, and concentrating. It helps to identify good memory which can process actively and efficiently and can prove if people face any learning disabilities [GMMN22].…”

Section: Eeg Frequency Bandsmentioning

confidence: 99%

“…It appears when individuals can feel creative (more like imagery, fantasy) or be distracted. So that it shows a noticeable presence during the time of mediation[GMMN22].…”

mentioning

confidence: 95%

“…For instance, normally the alpha waves are measured from the occipital region in an awake person when the eyes are closed, and the beta waves are measured over the parietal and frontal lobes.• Delta (δ):sleep. So that when we start to focus, delta activity starts to decrease[GMMN22].• Theta (θ): • Beta (β): It is classified as a fast activity. It is more visible in the frontal head region.…”

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confidence: 99%

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Emotion Recognition with Asymmetry Features of EEG Signals

Mouri

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Currently the study of affective computing (AC) includes a focus on researching emotion regulation and recognition. Recent studies in this field have utilized deep learning architectures to enhance emotion recognition from EEG signals. An alternative approach to deep learning is to use feature engineering to extract relevant features to train supervised machine learning models. Current theories in the neuroscience field can guide this feature engineering process. Neuroscientists have suggested various models to clarify how emotions are processed. One of these models suggests that positive emotions are processed in the left hemisphere, while negative emotions are processed in the right hemisphere. This emotional processing model has inspired previous studies to propose asymmetrical features to predict emotions. However, none of these studies have statistically evaluated whether the inclusion of asymmetrical features could yield benefits such as increased accuracy or reduced training time. To address that direction, this research presents both statistical evaluations for emotion regulation and a comparable model for emotion recognition. The outcomes show that brain hemispheres and frequency bands participate differently in processing emotions and observed the presence of the two asymmetry emotion processing models but in different frequency ranges. Also, the results from this study imply that by using asymmetry EEG, emotion recognition approaches can use fewer features without significantly compromising performance.

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Section: Eeg Frequency Bandsmentioning

confidence: 99%

“…It appears when individuals can feel creative (more like imagery, fantasy) or be distracted. So that it shows a noticeable presence during the time of mediation[GMMN22].…”

mentioning

confidence: 95%

mentioning

confidence: 99%

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Emotion Recognition with Asymmetry Features of EEG Signals

Mouri

View full text Add to dashboard Cite

show abstract

EEG Based Human Emotion Analysis

Cited by 1 publication

References 7 publications

Emotion Recognition with Asymmetry Features of EEG Signals

Emotion Recognition with Asymmetry Features of EEG Signals

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