Centre for Healthcare Technologies, Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Tamil Nadu, India.
Department of Pediatrics, Sri Ramachandra Institute of Higher Education and Research, Porur, Chennai, Tamil Nadu, India.
Proc Inst Mech Eng H. 2023 May;237(5):653-665. doi: 10.1177/09544119231170683. Epub 2023 Apr 25.
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by learning, attention, social, communication, and behavioral impairments. Each person with Autism has a different severity and level of brain functioning, ranging from high functioning (HF) to low functioning (LF), depending on their intellectual/developmental abilities. Identifying the level of functionality remains crucial in understanding the cognitive abilities of Autistic children. Assessment of EEG signals acquired during specific cognitive tasks is more appropriate in identifying brain functional and cognitive load variations. The spectral power of EEG sub-band frequency and parameters related to brain asymmetry has the potential to be employed as indices to characterize brain functioning. Thus, the objective of this work is to analyze the cognitive task-based electrophysiological variations in autistic and control groups, using EEG acquired during two well-defined protocols. Theta to Alpha ratio (TAR) and Theta to Beta ratio (TBR) of absolute powers of the respective sub-band frequencies have been estimated to quantify the cognitive load. The variations in interhemispheric cortical power measured by EEG were studied using the brain asymmetry index. For the arithmetic task, the TBR of the LF group was found to be considerably higher than the HF group. The findings reveal that the spectral powers of EEG sub-bands can be a key indicator in the assessment of high and low-functioning ASD to facilitate appropriate training strategies. Instead of depending solely on behavioral tests to diagnose autism, it could be a beneficial approach to use task-based EEG characteristics to differentiate between the LF and HF groups.
自闭症谱系障碍(ASD)是一种神经发育障碍,其特征为学习、注意力、社交、沟通和行为方面的障碍。每个自闭症患者的严重程度和大脑功能水平都不同,从高功能(HF)到低功能(LF)不等,这取决于他们的智力/发育能力。确定功能水平对于理解自闭症儿童的认知能力仍然至关重要。评估在特定认知任务期间获得的 EEG 信号更适合识别大脑功能和认知负荷的变化。EEG 子带频率的谱功率和与大脑不对称性相关的参数有可能被用作表征大脑功能的指标。因此,这项工作的目的是分析自闭症和对照组在基于认知任务的电生理变化,使用在两个明确定义的协议期间获得的 EEG。已估计各自子带频率的绝对功率的θ到α比(TAR)和θ到β比(TBR)来量化认知负荷。使用大脑不对称指数研究了 EEG 测量的大脑半球间皮质功率的变化。对于算术任务,发现 LF 组的 TBR 明显高于 HF 组。研究结果表明,EEG 子带的谱功率可以成为评估高功能和低功能 ASD 的关键指标,以促进适当的训练策略。而不是仅仅依靠行为测试来诊断自闭症,使用基于任务的 EEG 特征来区分 LF 和 HF 组可能是一种有益的方法。