Integration of Facial Thermography in EEG-based Classification of ASD

Haputhanthri, Dilantha; Brihadiswaran, Gunavaran; Gunathilaka, Sahan; Meedeniya, Dulani; Jayarathna, Sampath; Jaime, Mark; Harshaw, Christopher

doi:10.1007/s11633-020-1231-6

Cited by 34 publications

(12 citation statements)

References 51 publications

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“…ERP allows the EEG recording to reflect the modulations of brain activity moment-by-moment, which is critical in the paradigm of the study. To justify the methodology of this study, a summary table of current studies of mental health disorders and the methodology implemented is featured in Table 1 ERP results showed the Late Positive Potential reflects the clinical benefits of attention bias modification intervention [24] Classification of autism spectrum disorder (ASD)…”

Section: Research Methods 21 Related Workmentioning

confidence: 99%

Attentional bias during public speaking anxiety revealed using event-related potentials

Feroz

Salman

Ali

et al. 2021

IJEECS

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<p>Analysis of brain signals and their properties provides valuable information regarding the underlying neural deficiencies and enables the diagnosis of attention bias related to public speaking anxiety (PSA). Although 25% people around the world suffer from PSA, currently, there exists a lack of standard assessment in diagnosing the severity of attention bias in individuals with PSA. This study aims to distinguish behavioral and neural abnormalities related to attentional bias during PSA by comparing reaction time (RT) and event-related potential (ERP) correlates of high (H) PSA and low (L) PSA individuals. 12 individuals suffering from HPSA and 12 individuals with LPSA participated in the modified emotional Stroop experiment. Electroencephalography (EEG) was recorded with the low cost, 14-channel Emotiv Epoc+. RT showed slower responses, linked to attentional deficits in HPSA individuals. ERP results revealed the P200 emotional Stroop biomarker, found to be linked to attentional bias in HPSA, but not in LPSA individuals. These results revealed significant RT and P200 ERP abnormalities related to attentional bias in HPSA individuals using the low-cost Emotiv Epoc+.</p>

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Section: Research Methods 21 Related Workmentioning

confidence: 99%

Attentional bias during public speaking anxiety revealed using event-related potentials

Feroz

Salman

Ali

et al. 2021

IJEECS

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“…Thus, computer-aided solutions can be used to identify lung infection conditions by analyzing chest X-ray images as a support tool for an effective and efficient diagnosis process by reducing human error and effort. At present, computational methods play a significant role in decision making across several directions in the field of medical image analysis [8] , [9] , [10] , [11] , [12] . The recent advancement in data engineering approaches, particularly deep learning (DL) techniques have shown promising performance in identifying patterns and classifying medical images including chest X-ray images.…”

Section: Introductionmentioning

confidence: 99%

Chest X-ray analysis empowered with deep learning: A systematic review

Meedeniya

Kumarasinghe

Kolonne

et al. 2022

Applied Soft Computing

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“…The research implemented feature extraction and feature selection methods through correlation and random forest (RF) for seizure and non-seizure classification [12], [13]. The EEG signals are also helpful for detecting other brain disorders like autism, as illustrated in [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier

Panigrahi

Behera²,

Patra³

2022

IJEECS

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Epilepsy causes repeated seizures in an individual's life, which causes transient irregularities in the brain's electrical activity. It results in different physical symptoms that are abnormal. Various antiepileptic drugs fail to minimize repeated patient seizures. The electroencephalogram (EEG) signal recordings provide us with time-series data set for epileptic seizure detection and analysis. These signals are highly nonlinear and inconsistent, and they are recorded over time. Predicting the ictal period (seizure period at the time of epilepsy) is thus a challenging task in the naked eye for the medical practitioners. Various machine learning techniques are applied to identify the seizure's occurrence and its classification in multiple domains. A classification model based on extreme gradient boosting (SCLXGB) is proposed here for the classification of the EEG signals. The SCLXGB model implements binary seizure classification on the benchmark dataset. Compared with K-nearest neighbor, linear regression, and Decision treebased models, the proposed model achieves the best area under receiver operating curve (AUC) of 0.9462 and an accuracy of 96% which signifies accurate prediction of seizure and non seizure period. The proposed model SCLXGB was validated by taking different performance metrics to indicate the occurrence and non-occurrence of seizures in patients more appropriately.

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Integration of Facial Thermography in EEG-based Classification of ASD

Cited by 34 publications

References 51 publications

Attentional bias during public speaking anxiety revealed using event-related potentials

Attentional bias during public speaking anxiety revealed using event-related potentials

Chest X-ray analysis empowered with deep learning: A systematic review

Epileptic seizure classification of electroencephalogram signals using extreme gradient boosting classifier

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