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. 2023 Jul 6:17:1219553.
doi: 10.3389/fnins.2023.1219553. eCollection 2023.

Recognizing emotions induced by wearable haptic vibration using noninvasive electroencephalogram

Xin Wang  1 , Baoguo Xu  1 , Wenbin Zhang  1 , Jiajin Wang  1 , Leying Deng  1 , Jingyu Ping  1 , Cong Hu  2 , Huijun Li  1
Affiliations

Recognizing emotions induced by wearable haptic vibration using noninvasive electroencephalogram

Xin Wang et al. Front Neurosci. .

Abstract

The integration of haptic technology into affective computing has led to a new field known as affective haptics. Nonetheless, the mechanism underlying the interaction between haptics and emotions remains unclear. In this paper, we proposed a novel haptic pattern with adaptive vibration intensity and rhythm according to the volume, and applied it into the emotional experiment paradigm. To verify its superiority, the proposed haptic pattern was compared with an existing haptic pattern by combining them with conventional visual-auditory stimuli to induce emotions (joy, sadness, fear, and neutral), and the subjects' EEG signals were collected simultaneously. The features of power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), and differential caudality (DCAU) were extracted, and the support vector machine (SVM) was utilized to recognize four target emotions. The results demonstrated that haptic stimuli enhanced the activity of the lateral temporal and prefrontal areas of the emotion-related brain regions. Moreover, the classification accuracy of the existing constant haptic pattern and the proposed adaptive haptic pattern increased by 7.71 and 8.60%, respectively. These findings indicate that flexible and varied haptic patterns can enhance immersion and fully stimulate target emotions, which are of great importance for wearable haptic interfaces and emotion communication through haptics.

Keywords: affective computing; affective haptics; electroencephalogram; emotion recognition; wearable haptic vibration.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The framework for emotion recognition that incorporates two haptic patterns with traditional visual–auditory stimuli.
Figure 2
Figure 2
Experimental setup and paradigm for emotion recognition. (A) An experimental platform for evoking subjects’ emotions through the visual–auditory-haptic fusion stimulation. (B) The EEG cap layout for 64 channels.
Figure 3
Figure 3
The haptic vest with a dual vibration motors matrix: (A) Vest. (B) Front view of the motors matrix.
Figure 4
Figure 4
Emotion experimental paradigm based on the visual–auditory-haptic stimuli. The visual–auditory stimuli include a previously selected film clip and the haptic stimuli chosen from either of the two haptic patterns.
Figure 5
Figure 5
Procedure of two visual–auditory-haptic fusion stimuli. The visual–auditory stimuli were presented as movie clips throughout the experiment, while the haptic stimuli were applied only in the second half of each clip. The first haptic vibration pattern employed a fixed vibration intensity and rhythm, and the second haptic pattern adapted the vibration intensity and rhythm to the video volume.
Figure 6
Figure 6
Mean time-frequency analysis based on 16 subjects with non-Haptic 1 or Haptic 1.
Figure 7
Figure 7
Mean time-frequency analysis based on 16 subjects with non-Haptic 2 or Haptic 2.
Figure 8
Figure 8
The average neural patterns in different emotional states for 16 subjects with non-Haptic 1 or Haptic 1.
Figure 9
Figure 9
The average neural patterns in different emotional states for 16 subjects with non-Haptic 2 or Haptic 2.
Figure 10
Figure 10
The average classification accuracy of DE feature by SVM in different frequency bands with non-haptic and haptic patterns.
See this image and copyright information in PMC

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