The process of integration of inputs from several sensory modalities in the human brain is referred to as multisensory integration. Age-related cognitive decline leads to a loss in the ability of the brain to conceive multisensory inputs. There has been considerable work done in the study of such cognitive changes for the old age groups. However, in the case of middle age groups, such analysis is limited. Motivated by this, in the current work, EEG-based functional connectivity during audiovisual temporal asynchrony integration task for middle-aged groups is explored. Investigation has been carried out during different tasks such as: unimodal audio, unimodal visual, and variations of audio-visual stimulus. A correlation-based functional connectivity analysis is done, and the changes among different age groups including: young (18-25 years), transition from young to middle age (25-33 years), and medium (33-41 years), are observed. Furthermore, features extracted from the connectivity graphs have been used to classify among the different age groups. Classification accuracies of $89.4\%$ and $88.4\%$ are obtained for the Audio and Audio-50-Visual stimuli cases with a Random Forest based classifier, thereby validating the efficacy of the proposed method.
翻译:随着年龄的增长,人脑能够接收多种感官模式的输入能力下降,这被称为多感官整合。对于老年人群,研究这些认知变化的工作已经有了相当多的进展。但是,在中年群体中,这种分析是有限的。受此启发,本文探讨了基于脑电图(EEG)的功能连接在中年群体中执行音频视觉时间不同步度整合任务时的表现。在不同的任务中进行了调查,包括:单模音频、单模视觉和不同的音视频刺激变化。进行了基于相关性的功能连接分析,并观察了不同年龄组之间的变化,包括:年轻组(18-25岁)、从年轻到中年的转型期(25-33岁)和中等年龄(33-41岁)。此外,从连接图中提取的特征被用于分类不同的年龄组。对于基于音频和基于音频-50-视觉刺激的情况,采用随机森林分类器,分类准确率分别达到了89.4%和88.4%。从而验证了该方法的有效性。