Bingxin Lin

Bingxin Lin


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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15 March 2024 in Sensors
Verified
Journal Article
Open access
PDF file
YHYuqi Hang
Yuqi Hang
BUBuyanzaya Unenbat
Buyanzaya Unenbat
STShiyun Tang
Shiyun Tang
FWFei Wang
Fei Wang
BLBingxin Lin
Bingxin Lin
Dan ZhangDan Zhang
Dan Zhang
Flow experience, characterized by deep immersion and complete engagement in a task, is highly recognized for its positive psychological impacts. However, previous studies have been restricted to using a single type of task, and the exploration of its neural correlates has been limited. This study aimed to explore the neural correlates of flow experience with the employment of multifaceted flow-induction tasks. Six tasks spanning mindfulness, artistic tasks, free recall, and varying levels of Tetris complexity (easy, flow, and hard conditions) were employed to have relatively complete coverage of the known flow-induction tasks for a better induction of individualized flow experience. Twenty-eight participants were recruited to perform these six tasks with a single-channel prefrontal EEG recording. Significant positive correlations were observed between the subjective flow scores of the individual’s best-flow-experience task and the EEG activities at the delta, gamma, and theta bands, peaking at latencies around 2 min after task onset. The outcomes of regression analysis yield a maximum R2 of 0.163. Our findings report the EEG correlates of flow experience in naturalistic settings and highlight the potential of portable and unobtrusive EEG technology for an objective measurement of flow experience.

15 September 2023 in IEEE Transactions on Affective Computing
Verified
Journal Article
Closed access
XSXinyu Shui
Xinyu Shui
RLRongzan Lin
Rongzan Lin
ZLZiyang Luo
Ziyang Luo
BLBingxin Lin
Bingxin Lin
XMXinxin Mao
Xinxin Mao
Dan ZhangDan Zhang
Dan Zhang
The view of embodied emotion believes that emotions are the emotions of the body. While emotion-specific patterns of self-reported bodily sensation have been previously reported, the physiological bodily representation across emotions remains to be addressed. The present study aimed to investigate the effectiveness of multi-site bodily electrodermal representations of emotions. A multi-channel electrodermal measurement device was designed to record electrodermal activities from nine body sites (neck, back, chest, bilateral abdomen, bilateral wrist, and bilateral ankle) from thirty-six college students (all male), while they were presented with a series of emotional pictures. Using the integral skin conductance response feature and a random forest classification method, the classification of high and low arousal levels achieved an average classification accuracy of 80.4±8.1%, and the classification of positive, neutral, and negative states reached an average classification accuracy of 76.4±10.2%. The classification models for arousal and valence were found to rely on distinct bodily representations. Meanwhile, the classification results of multi-site measurement were significantly better than single-site results. Our findings for the first time illustrate the bodily electrodermal representations of emotion and suggest the feasibility of affective computing using bodily electrodermal signals.

11 January 2024 in Preprints.org
Verified
Preprint
Open access
PDF file
YHYuqi Hang
Yuqi Hang
BUBuyanzaya Unenbat
Buyanzaya Unenbat
STShiyun Tang
Shiyun Tang
FWFei Wang
Fei Wang
BLBingxin Lin
Bingxin Lin
Dan ZhangDan Zhang
Dan Zhang
Flow experience, characterized by deep immersion and complete engagement in a task, is highly recognized for its positive psychological impacts. However, previous studies have been restricted to use a single type of task, and the exploration on its neural correlates has been limited. This study aimed to explore the neural correlates of flow experience with the employment of multifac-eted flow-induction tasks. Six tasks spanning mindfulness, artistic tasks, free recall, and varying levels of Tetris complexity (easy, flow, and hard conditions) were employed to have a relatively complete coverage of the known flow-induction tasks for a better induction of individualized flow experience. Twenty-eight participants were recruited to perform these six tasks, with a sin-gle-channel prefrontal electroencephalography (EEG) recording. Significant positive correlations were observed between the subjective flow scores of individual’s best-flow-experience task and the EEG activities at delta, gamma, and theta bands, peaking at latencies around 2 minutes after task onset. The outcomes of our multiple regression analysis yield a maximum coefficient of de-termination (R²) of 0.279. Our findings report the EEG correlates of flow experience in naturalistic settings and highlight the potential of portable and unobtrusive EEG technology for an objective measurement of flow experience.