Autism Spectrum Disorder (ASD) is a disturbing neuro-developmental disorder affecting the behavioural ability and communication skills of an individual. Early detection of such prevalent abnormality through EEG pattern analysis with the help of computer-aided diagnostic tools; provide significant relief to the health care professionals for better management and treatment of such patients. This work has been proposed as an investigation of the sensory responsiveness of the children affected by ASD. This ASD is analyzed by extracting the non-linear features of the EEG signal. A total of 73 EEG signals are collected from different patients out of which 41 are affected with ASD and 32 with normal neural activity. These signals are then pre-processed and filtered using a low pass filter and then separated using Independent Component Analysis (ICA) into additive subcomponents. Significant features are extracted, which can be further investigated to identify event-related potential and Non-biological Artifacts. These features are then classified using Support Vector Machine (SVM) with an accuracy of 90.41%. As a result, appropriate sensory profiles can be obtained that may help proper diagnosis and treatment at an early stage.

自闭症谱系障碍 (ASD) 是一种令人不安的神经发育障碍，影响个体的行为能力和沟通技巧。借助计算机辅助诊断工具，通过脑电图模式分析及早发现此类普遍异常；为医疗保健专业人员提供显着的缓解，以更好地管理和治疗此类患者。这项工作被提议作为对受 ASD 影响的儿童的感官反应的调查。通过提取 EEG 信号的非线性特征来分析此 ASD。从不同患者收集了总共 73 个 EEG 信号，其中 41 个患有 ASD，32 个具有正常神经活动。然后使用低通滤波器对这些信号进行预处理和过滤，然后使用独立分量分析 (ICA) 将其分离为附加子分量。提取重要特征，可以进一步研究这些特征以识别与事件相关的潜力和非生物人工制品。然后使用支持向量机 (SVM) 对这些特征进行分类，准确率为 90.41%。因此，可以获得适当的感官概况，这可能有助于在早期阶段进行正确的诊断和治疗。