Khazaei Saman, Parshi Srinidhi, Alam Samiul, Amin Md Rafiul, Faghih Rose T
Department of Biomedical Engineering, New York University, New York, NY, United States.
Department of Electrical and Computer Engineering, University of Houston, Houston, TX, United States.
Front Neurosci. 2024 Jun 19;18:1406814. doi: 10.3389/fnins.2024.1406814. eCollection 2024.
Decoding an individual's hidden brain states in responses to musical stimuli under various cognitive loads can unleash the potential of developing a non-invasive closed-loop brain-machine interface (CLBMI). To perform a pilot study and investigate the brain response in the context of CLBMI, we collect multimodal physiological signals and behavioral data within the working memory experiment in the presence of personalized musical stimuli.
Participants perform a working memory experiment called the -back task in the presence of calming music and exciting music. Utilizing the skin conductance signal and behavioral data, we decode the brain's cognitive arousal and performance states, respectively. We determine the association of oxygenated hemoglobin (HbO) data with performance state. Furthermore, we evaluate the total hemoglobin (HbT) signal energy over each music session.
A relatively low arousal variation was observed with respect to task difficulty, while the arousal baseline changes considerably with respect to the type of music. Overall, the performance index is enhanced within the exciting session. The highest positive correlation between the HbO concentration and performance was observed within the higher cognitive loads (3-back task) for all of the participants. Also, the HbT signal energy peak occurs within the exciting session.
Findings may underline the potential of using music as an intervention to regulate the brain cognitive states. Additionally, the experiment provides a diverse array of data encompassing multiple physiological signals that can be used in the brain state decoder paradigm to shed light on the human-in-the-loop experiments and understand the network-level mechanisms of auditory stimulation.
解码个体在各种认知负荷下对音乐刺激做出反应时隐藏的脑状态，能够释放开发非侵入性闭环脑机接口（CLBMI）的潜力。为了开展一项初步研究并探究CLBMI背景下的大脑反应，我们在个性化音乐刺激存在的情况下，于工作记忆实验中收集多模态生理信号和行为数据。
参与者在舒缓音乐和激昂音乐存在的情况下执行一项名为n-back任务的工作记忆实验。利用皮肤电导信号和行为数据，我们分别解码大脑的认知唤醒和表现状态。我们确定氧化血红蛋白（HbO）数据与表现状态之间的关联。此外，我们评估每个音乐时段的总血红蛋白（HbT）信号能量。
相对于任务难度，观察到唤醒变化相对较低，而唤醒基线相对于音乐类型有相当大的变化。总体而言，在激昂音乐时段内表现指数有所提高。在所有参与者的较高认知负荷（3-back任务）下，观察到HbO浓度与表现之间的最高正相关。此外，HbT信号能量峰值出现在激昂音乐时段内。
研究结果可能凸显了将音乐用作调节大脑认知状态干预手段的潜力。此外，该实验提供了包含多种生理信号的多样数据阵列，可用于脑状态解码器范式，以阐明人在回路实验并理解听觉刺激的网络层面机制。
