An Endogenic Attentional Visuomotor Regulator

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Abstract Submission 

Yiqing Hu¹, Hao Zhang², Xiaoli Li³, Yan Song³, Zaixu Cui¹, Chenguang Zhao⁴

¹Chinese Institute for Brain Research, Beijing, China, ²School of Systems Science, Beijing Normal University, Beijing, China, ³State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China, ⁴Chinese Institute for Brain Research, Beijing, Beijing

Yiqing Hu  
Chinese Institute for Brain Research
Beijing, China

Hao Zhang  
School of Systems Science, Beijing Normal University
Beijing, China

Xiaoli Li  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Yan Song  
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China

Zaixu Cui  
Chinese Institute for Brain Research
Beijing, China

Chenguang Zhao  
Chinese Institute for Brain Research
Beijing, Beijing

Attention is not constant but fluctuates from moment to moment(Esterman & Rothlein, 2019). Although a lot of studies have found alpha (8-12 Hz) synchrony appears to influence sensory processing during visual attention tasks(Esterman & Rothlein, 2019; Foxe et al., 1998; Fu et al., 2001; Herrmann & Knight, 2001), most of them only show correlation but not causality(Peylo et al., 2021). Here, we established a cognitive brain-machine interface (cBMI)(Chinchani et al., 2022), which was designed to monitor real-time alpha oscillations and was proved to regulate endogenous attention in visuomotor tasks successfully.

2 electroencephalograph (EEG) experiments with 75 young adults aged 18-28 years old were included in our study. A cartoon visual search paradigm with 5 green planes (non-target) and one yellow plane (target) distributed in a clockwise manner (Fig. 1A). Participants were asked to use the computer joystick, an efficient and cost-effective response device for recording continuous movements(Szul et al., 2020), to maneuver to the target position while maintaining their gaze on the central fixation. In this case, we can precisely measure participants' response time and belief time (Fig. 1B). Experiment 1 (N = 50) was designed to find the neural biomarker related to different attention levels in which the target search array appeared in 10 Hz frequency between 2-10 s intertrial interval after response end (Fig. 1C). Importantly, in experiment 2 (N = 25), we monitored the positive or negative biomarker contained from experiment 1 every second to determine the target appeared time which demonstrated our system can casually regulate the endogenic attentional visuomotor in real-time (Fig. 1D). The EEG recording, preprocessing processes, and EEG analyses methods, including N2pc (time-locked to target onset), alpha power, and channel tuning functions (CTFs) were based on the former attention studies(Jensen & Mazaheri, 2010; Zhao et al., 2023).

In experiment 1, we found different cognitive protocols connected to attention fluctuates that the alpha power decreased in good performance trials but increased in bad performance trials (Fig. 2A). In experiment 2, the belief time became shorter (t = -2.378, p < 0.05) when using the positive protocol (when alpha power decreased in the test segment, the target search array will appear) than using the negative protocol (target search array only appeared while alpha power increased) (Fig. 2B). N2pc component which appears to reflect target selection(Eimer, 1996) was larger in the negative protocol condition than in the positive protocol condition (t = -2.248, p < 0.05), which indicated that in the positive attention condition, benefit in the spatial representation for target selection during the search array is no longer needed (Fig. 2C). CTFs which reflect the spatial distribution of alpha power measured by scalp after the target search array for positive and negative protocol condition (Fig. 2D). The time-resolved slope of CTFs results suggest that alpha power tracked the target location earlier in positive condition (T = 175 ms) than in the negative protocol condition (T = 412 ms) (Fig. 2E).

Taken together, we monitored real-time alpha oscillations attached to attention fluctuations and successfully regulated endogenous attention to motivate better behavior performance. This work may be helpful in understanding the mechanism of attention and can be applied to improve cognition in diverse psychiatric disorders, including ADHD and schizophrenia.

Non-Invasive Stimulation Methods Other ¹

Executive Function, Cognitive Control and Decision Making

EEG/MEG Modeling and Analysis

EEG

Attention: Visual ²

Cognition

Motor

Vision

Other - EEG; Attention; cBMI