The Academic Salon of the Department of Psychology and Cognitive Sciences is held every Thursday afternoon. Welcome to all students and faculty members from every department!
Time: March 13 (Thursday) afternoon 14:30
Location: Room 1110, 11th floor, Lv Dalong Building
Department of Psychology and Cognitive Sciences ZHANG Dan's Research Group Academic Salon
Report One
Effects of Transcranial Direct Current Stimulation on Temporal Working Memory
Presenter: Bingxin Lin
Content:
Time is one of the fundamental frameworks through which humans perceive and understand the world. The processing of temporal information in the millisecond to second range is closely related to various activities in human daily life—such as sports, music, and language. The core element of temporal information processing may be working memory, namely the short-term storage and online manipulation of temporal information. However, compared to non-temporal information such as spatial and verbal information, the processing mechanisms of temporal information in working memory have received little attention. Furthermore, as a non-invasive and non-intrusive brain stimulation technique, transcranial direct current stimulation (tDCS) has been proven to enhance various cognitive functions. Dr. Bingxin Lin will report on the effects of tDCS on temporal working memory, aiming to provide new perspectives for understanding the neural mechanisms of temporal working memory and to offer potential non-invasive intervention methods for related cognitive dysfunction.
Report Two
Intelligent Discrimination of Portable EEG in ADHD Diagnosis Under Multi-task Cognitive Paradigms
Presenter: Wenrui Cheng
Content:
Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental disorder in childhood, usually associated with abnormalities in attention control, impulse inhibition, and emotion regulation. Traditional ADHD diagnostic methods often rely on clinical observation and behavioral assessment, lacking objective and quantifiable neurophysiological indicators. Electroencephalography (EEG), as a non-invasive and easy-to-operate technique, has been widely applied in neurophysiological research of ADHD in recent years. Student Wenrui Cheng will report on her collection of EEG data from subjects during six cognitive tasks (memory, emotion recognition, spatial cognition, attention, reaction speed, and reasoning) through portable EEG devices, conducting comparative analysis between ADHD patients and healthy control groups through machine learning classification methods. Preliminary research results show that task-related EEG features can effectively improve the diagnostic accuracy of ADHD.
Report Three
Brain Concerto: Hyperscanning Study of Neural Coupling During Live Musical Performance
Presenter: Jiameng Liu, Zheng Liang
Content:
This study uses hyperscanning technology based on functional near-infrared spectroscopy (fNIRS) to investigate interpersonal neural activity between performers and audiences during live musical performance. In the experiment, three musicians performed for thirty audience members across five concerts. fNIRS devices simultaneously recorded brain activity in the frontal, central, and temporal regions of all participants. Results showed significant differences in inter-brain coupling patterns among different participant roles. Within the performer group, coupling mainly occurred in the prefrontal regions of the brain, possibly reflecting that joint musical performance requires higher levels of cognitive control coordination; performer-audience coupling mainly appeared between performers' sensorimotor regions and audiences' prefrontal regions, suggesting that audiences may achieve musical understanding by analyzing performers' body language; coupling within the audience group was mainly concentrated in temporal and central sensorimotor regions, possibly related to musical perception processing. This study enhances our understanding of the neural basis of interpersonal interaction in musical activities.
Report Four
Emotion State Recognition from Wearable Physiological Data in Daily Contexts Based on Transformer Models
Presenter: Fang Li
Content:
In recent years, the rapid development of wearable physiological measurement technology has gradually integrated affective computing into daily life scenarios. Recognizing emotional states in daily contexts brings important application potential to human-computer interaction and psychiatric medicine fields. Addressing the challenges of long-term, multimodal physiological data in daily environments, this study proposes an emotion state recognition algorithm based on Transformer architecture, which aims to fully exploit the temporal features of signals and the correlational characteristics between different modalities. Using the DAPPER dataset (including heart rate, skin conductance, and three-axis acceleration data collected from 88 subjects wearing wrist devices continuously for 5 days), our developed Transformer model achieved an average accuracy of 71.5% in self-reported "positive-negative" binary emotion classification from randomly sampled daily data, and achieved accuracies of 60.29% and 61.55% respectively in five-level classification tasks based on valence and arousal scores. Research results verify the feasibility of implementing emotion state recognition in daily scenarios based on wearable multimodal physiological signals.
