Investigating EEG Signals of Autistic Individuals Using Detrended Fluctuation Analysis

Radhakrishnan, Menaka; Ramamurthy, Karthik; Kothandaraman, Avantika; Madaan, Gauri; Machavaram, Harini

doi:10.18280/ts.380528

Cited by 4 publications

(2 citation statements)

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“…The coherence between EEG channels has assessed using the NeuCube architecture [8]. The analysis focused on the strength of self-similarity in the signals, utilizing Hurst exponents derived from Detrended Fluctuation Analysis (DFA) outputs [9].…”

Section: Introductionmentioning

confidence: 99%

Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients

Shanmugam,

Radhakrishnan

2024

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by restricted, repetitive behaviors and impaired social interaction. Currently, the identification of individuals with ASD largely relies on subjective assessments, presenting a challenge for researchers to distinguish between Typically Developing (TD) children and those with ASD. This study analyzes EEG data from 10 children with ASD and 10 TD children in response to an audio-video stimulus. Two separate analyses were performed on EEG frequency bands within the range of 0-70 Hz and specific frequency bands of 8-30 Hz, aiming to identify the brain lobe region that yields the most significant discrimination between ASD and TD. Parameters such as Linear Frequency Cepstral Coefficients (LFCC), Cepstral energy, signal energy, delta, and delta-delta derivatives were utilized for the analysis. The study deployed classification techniques including K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Decision Tree, Support Vector Machine (SVM), Bagging KNN, and Random Forest (RF). The results indicated that KNN surpassed all other classification models for frequency bands within the range of 0-70Hz, achieving a discriminating accuracy of 98.3% for ASD and TD in the central lobe region (C3, C4, Cz). However, KNN did not yield a significant level of accuracy when applied to a specific frequency band; it was improved by employing Bagging KNN, reaching 93.8% in the central lobe region (C3, C4, Cz). The electrode combination in the central lobe (C3, C4, and Cz) demonstrated superior discrimination between TD and ASD compared to other brain lobes.

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Section: Introductionmentioning

confidence: 99%

Autism Classification and Identification of Significant Brain Lobe Using Cepstral Coefficients

Shanmugam,

Radhakrishnan

2024

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“…Network variability may be seen through time series estimations using methods for analysing complex and intricate networks such as the human brain. As a result, a collection of invariant measures derived from EEG data must represent the neuronal prospects in the brain that generates the signals [2][3][4][5]. EEGs are established as observations of electrical activity in the brain acquired from the surface of the scalp.…”

Section: Introductionmentioning

confidence: 99%

EEG-Based Anamoly Detection for Autistic Kids – A Pilot Study

Radhakrishnan¹,

Boruah²,

Ramamurthy³

2022

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An electroencephalogram (EEG) test can be utilized to capture the electrical impulses in the human brain. EEG signal analysis is crucial in the detection and treatment of brain diseases. Autism is one of the neurological disorders that needs to be diagnosed in the early stages of life. Autistic behavior is difficult to differentiate and it can even lead to adverse effects in the daily routine of a kid. Recent advances in Artificial Intelligence have proven to be an effective way of diagnosing ASD. This research employs PyCaret framework to analyze the anomalies present in the EEG signal data in the context of differentiating Autistic children from Typically developing children. The different anomaly detection modules have been used to detect anomalies, compute their anomaly scores and visualize it. The goal of this study is to determine if PyCaret's anomaly detection module can aid the detection of ASD.

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