Alasfour Abdulwahab, Gilja Vikash
Department of Electrical Engineering, College of Engineering and Petroleum, Kuwait University, Kuwait City, Kuwait.
Department of Electrical and Computer Engineering, University of California, San Diego, CA, United States.
Front Hum Neurosci. 2024 May 30;18:1388267. doi: 10.3389/fnhum.2024.1388267. eCollection 2024.
Understanding the neural correlates of naturalistic behavior is critical for extending and confirming the results obtained from trial-based experiments and designing generalizable brain-computer interfaces that can operate outside laboratory environments. In this study, we aimed to pinpoint consistent spectro-spatial features of neural activity in humans that can discriminate between naturalistic behavioral states.
We analyzed data from five participants using electrocorticography (ECoG) with broad spatial coverage. Spontaneous and naturalistic behaviors such as "Talking" and "Watching TV" were labeled from manually annotated videos. Linear discriminant analysis (LDA) was used to classify the two behavioral states. The parameters learned from the LDA were then used to determine whether the neural signatures driving classification performance are consistent across the participants.
Spectro-spatial feature values were consistently discriminative between the two labeled behavioral states across participants. Mainly, , , and low and high in the postcentral gyrus, precentral gyrus, and temporal lobe showed significant classification performance and feature consistency across participants. Subject-specific performance exceeded 70%. Combining neural activity from multiple cortical regions generally does not improve decoding performance, suggesting that information regarding the behavioral state is non-additive as a function of the cortical region.
To the best of our knowledge, this is the first attempt to identify specific spectro-spatial neural correlates that consistently decode naturalistic and active behavioral states. The aim of this work is to serve as an initial starting point for developing brain-computer interfaces that can be generalized in a realistic setting and to further our understanding of the neural correlates of naturalistic behavior in humans.
理解自然行为的神经关联对于扩展和确认基于试验的实验结果，以及设计能够在实验室环境之外运行的通用脑机接口至关重要。在本研究中，我们旨在确定人类神经活动中能够区分自然行为状态的一致的频谱空间特征。
我们使用具有广泛空间覆盖范围的皮层脑电图（ECoG）分析了五名参与者的数据。从人工标注的视频中标记出“交谈”和“看电视”等自发自然行为。使用线性判别分析（LDA）对这两种行为状态进行分类。然后，从LDA中学习到的参数被用于确定驱动分类性能的神经特征在参与者之间是否一致。
频谱空间特征值在参与者之间的两种标记行为状态之间始终具有判别性。主要地，中央后回、中央前回和颞叶中的 、 以及低频和高频显示出跨参与者的显著分类性能和特征一致性。个体特异性性能超过70%。组合来自多个皮层区域的神经活动通常不会提高解码性能，这表明关于行为状态的信息作为皮层区域的函数不是相加的。
据我们所知，这是首次尝试识别能够一致地解码自然和主动行为状态的特定频谱空间神经关联。这项工作的目的是作为开发能够在现实环境中通用的脑机接口的初步起点，并进一步加深我们对人类自然行为的神经关联的理解。
