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Related Concept Videos

Hearing01:31

Hearing

When we hear a sound, our nervous system is detecting sound waves—pressure waves of mechanical energy traveling through a medium. The frequency of the wave is perceived as pitch, while the amplitude is perceived as loudness.
Auditory Perception01:17

Auditory Perception

The auditory system is essential for sound perception, utilizing various critical structures. When sound waves enter the outer ear, they travel through the ear canal and cause the eardrum to vibrate. These vibrations are then transmitted to the middle ear, where three tiny bones – the malleus, incus, and stapes – amplify the sound. This amplification is crucial, as it ensures that the sound vibrations are strong enough to be conveyed to the inner ear. These vibrations then reach the cochlea, a...

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Decoding auditory-evoked response in affective states using wearable around-ear EEG system.

Jaehoon Choi1, Netiwit Kaongoen1, HyoSeon Choi2

  • 1School of Computing, KAIST, Daejeon, Republic of Korea.

Biomedical Physics & Engineering Express
|August 17, 2023
PubMed
Summary

Around-ear electroencephalography (EEG) shows comparable accuracy to traditional scalp-EEG for classifying emotional states. This ear-EEG system offers a more practical alternative for daily emotion monitoring applications.

Keywords:
affective computingear-eegelectroencephalogramemotion recognitionwearable device

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Area of Science:

  • Neuroscience
  • Affective Computing
  • Biomedical Engineering

Background:

  • Conventional scalp-based electroencephalography (EEG) is effective for emotion recognition but can be cumbersome for long-term monitoring.
  • Developing unobtrusive and wearable systems for continuous affective state monitoring is crucial for daily life applications.

Purpose of the Study:

  • To investigate the efficacy of an around-ear EEG system as an alternative to scalp-EEG for classifying human affective states.
  • To compare the performance of ear-EEG and scalp-EEG in recognizing arousal and valence dimensions of emotions evoked by auditory stimuli.

Main Methods:

  • A wearable, eight-dry-channel ear-EEG device was developed for data acquisition.
  • EEG data were collected from 21 subjects using both ear-EEG and conventional scalp-EEG (international 10-20 system) methods.
  • Features were extracted and asymmetry methods were applied for binary classification of arousal and valence states using multi-layer extreme learning machines.

Main Results:

  • Ear-EEG achieved average accuracies of 67.09% for arousal and 66.61% for valence in a subject-dependent context.
  • Scalp-EEG achieved average accuracies of 68.59% for arousal and 67.10% for valence in a subject-dependent context.
  • In a subject-independent context, ear-EEG yielded 63.74% (arousal) and 64.32% (valence), while scalp-EEG yielded 64.67% (arousal) and 64.86% (valence), with no significant differences observed.

Conclusions:

  • Around-ear EEG signals demonstrate comparable performance to scalp-EEG signals for classifying affective states.
  • This study is the first to explore ear-EEG for emotion monitoring, highlighting its potential for unobtrusive, daily affective life logging systems.