ENHANCED EEG SIGNAL CLASSIFICATION FOR AUTISM SPECTRUM DISORDER USING RIEMANNIAN GEOMETRY AND ENSEMBLE LEARNING TECHNIQUES

Hajir Alzubaydi; Zaid Alyasseri

doi:10.30572/2018/KJE/160312

Authors

Hajir Alzubaydi https://orcid.org/0000-0002-3010-8931
Zaid Alyasseri ECE dept. Faculty of Engineering, University of Kufa, Iraq.

DOI:

https://doi.org/10.30572/2018/KJE/160312

Keywords:

Autism, ASD, BCI, Electroencephalography, EEG, Ensemble Learning, Feature Extraction, Machine Learning, P300, Riemannian Geometry, Signal Processing

Abstract

Autism spectrum disorder (ASD) poses a severe challenge to effective communication and social interaction abilities for a large number of individuals worldwide. The P300 signal is difficult to detect in individuals with ASD due to noise, low amplitude, and increased latency compared to others. We enhance in this work the P300 classification in electroencephalogram (EEG) signals for autism disease using Riemannian geometry along with various conventional classifiers and ensemble learning approaches like bagging and boosting techniques (AdaBoostM1, GentleBoost, and LogitBoost), and developing sophisticated pre-processing methods for feature extraction. Using the BCIAUT-P300 dataset, our work achieved 96.37% accuracy, 97.51% sensitivity, and 91.11% specificity compared to existing processes, ranging from 67.2% to 92.3% accuracy. This work highlights the potential of our technique in assisting in diagnosis and supportive ASD technologies

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References

Adama, V.S., Schindler, B. and Schmid, T. (2020) ‘Using Time Domain and Pearson’s Correlation to Predict Attention Focus in Autistic Spectrum Disorder from EEG P300 Components’, in, pp. 1890–1893. Available at: https://doi.org/10.1007/978-3-030-31635-8_230.

de Arancibia, L. et al. (2020) ‘Linear vs Nonlinear Classification of Social Joint Attention in Autism Using VR P300-Based Brain Computer Interfaces’, in, pp. 1869–1874. Available at: https://doi.org/10.1007/978-3-030-31635-8_227.

Avendaño-Valencia, L.D. and Fassois, S.D. (2015) ‘Natural vibration response based damage detection for an operating wind turbine via Random Coefficient Linear Parameter Varying AR modelling’, Journal of Physics: Conference Series, 628(1), pp. 273–297. Available at: https://doi.org/10.1088/1742-6596/628/1/012073.

Bittencourt-Villalpando, M. and Maurits, N.M. (2020) ‘Linear SVM Algorithm Optimization for an EEG-Based Brain-Computer Interface Used by High Functioning Autism Spectrum Disorder Participants’, in, pp. 1875–1884. Available at: https://doi.org/10.1007/978-3-030-31635-8_228.

Borra, D., Fantozzi, S. and Magosso, E. (2020) ‘Interpretable and lightweight convolutional neural network for EEG decoding: Application to movement execution and imagination’, Neural Networks, 129, pp. 55–74. Available at: https://doi.org/10.1016/j.neunet.2020.05.032.

Breiman, L. (1996) ‘Bagging predictions’, Machine Learning, 24(2), pp. 123–140.

Chatterjee, B., Palaniappan, R. and Gupta, C.N. (2020a) ‘Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset’, IFMBE Proceedings, 76, pp. 1894–1898. Available at: https://doi.org/10.1007/978-3-030-31635-8_231.

Chatterjee, B., Palaniappan, R. and Gupta, C.N. (2020b) ‘Performance Evaluation of Manifold Algorithms on a P300 Paradigm Based Online BCI Dataset’, in, pp. 1894–1898. Available at: https://doi.org/10.1007/978-3-030-31635-8_231.

Christopher J.C. Burges (1998) ‘A Tutorial on Support Vector Machines for Pattern Recognition’, Data Mining and Knowledge Discovery, 2, pp. 121–167.

Daskalov, S. et al. (2020) Anthropomorphic Physical Breast Phantom Based on Patient Breast CT Data: Preliminary Results, IFMBE Proceedings. Available at: https://doi.org/10.1007/978-3-030-31635-8_44.

Falih, S.M. (2017) ‘a New Chaotic Map for Generating Chaotic Binary Sequence’, Kufa Journal of Engineering, 8(1), pp. 16–25. Available at: https://doi.org/10.30572/2018/kje/811192.

Friedman, J., Hastie, T. and Tibshirani, R. (2000) ‘Additive logistic regression: A statistical view of boosting’, Annals of Statistics, 28(2), pp. 337–407. Available at: https://doi.org/10.1214/aos/1016218223.

Henriques, J. and Neves, N. (2019) ‘Volume 76 XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019’, 76.

Jang, Y. et al. (2024) ‘Structural connectome alterations between individuals with autism and neurotypical controls using feature representation learning’, Behavioral and Brain Functions, 20(1), pp. 1–10. Available at: https://doi.org/10.1186/s12993-024-00228-z.

Jin, Z. et al. (2020) ‘RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis’, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12343 LNCS, pp. 503–515. Available at: https://doi.org/10.1007/978-3-030-62008-0_35.

Kheder, H.A. (2023) ‘Human-Computer Interaction: Enhancing User Experience in Interactive Systems’, Kufa Journal of Engineering, 14(4), pp. 23–41. Available at: https://doi.org/10.30572/2018/KJE/140403.

Krzemi, D. et al. (2020) ‘Classification of P300 Component Using a Riemannian Ensemble Approach.’, 1, pp. 1515–1525. Available at: https://doi.org/10.1007/978-3-030-31635-8.

Li, P. (2010) ‘Robust logitboost and adaptive base class (ABC) logitboost’, Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010, (2), pp. 302–311.

Miladinović, A. et al. (2020) ‘Slow Cortical Potential BCI Classification Using Sparse Variational Bayesian Logistic Regression with Automatic Relevance Determination’, IFMBE Proceedings, 76, pp. 1853–1860. Available at: https://doi.org/10.1007/978-3-030-31635-8_225.

Oberman, L.M. et al. (2005) ‘EEG evidence for mirror neuron dysfunction in autism spectrum disorders’, Cognitive Brain Research, 24(2), pp. 190–198. Available at: https://doi.org/10.1016/j.cogbrainres.2005.01.014.

Patel, M. et al. (2023) ‘CNN-FEBAC: A framework for attention measurement of autistic individuals’, Biomedical Signal Processing and Control, (March), p. 105018. Available at: https://doi.org/10.1016/j.bspc.2023.105018.

Peketi, S. and Dhok, S.B. (2023) ‘Machine Learning Enabled P300 Classifier for Autism Spectrum Disorder Using Adaptive Signal Decomposition’, Brain Sciences, 13(2). Available at: https://doi.org/10.3390/brainsci13020315.

Santamaría-Vázquez, E. et al. (2020) ‘Deep Learning Architecture Based on the Combination of Convolutional and Recurrent Layers for ERP-Based Brain-Computer Interfaces’, in, pp. 1844–1852. Available at: https://doi.org/10.1007/978-3-030-31635-8_224.

Simões, M. et al. (2020) ‘BCIAUT-P300: A Multi-Session and Multi-Subject Benchmark Dataset on Autism for P300-Based Brain-Computer-Interfaces’, Frontiers in Neuroscience, 14(September). Available at: https://doi.org/10.3389/fnins.2020.568104.

Talib, M. et al. (2021) ‘Twin Fetus Ecg Signal Extraction Based on Temporal Predictability’, Kufa Journal of Engineering, 11(1), pp. 35–51. Available at: https://doi.org/10.30572/2018/kje/110103.

Tanha, J. et al. (2020) ‘Boosting methods for multi-class imbalanced data classification: an experimental review’, Journal of Big Data, 7(1). Available at: https://doi.org/10.1186/s40537-020-00349-y.

Zhao, H. et al. (2020a) ‘A Feasible Classification Algorithm for Event-Related Potential (ERP) Based Brain-Computer-Interface (BCI) from IFMBE Scientific Challenge Dataset’, IFMBE Proceedings, 76, pp. 1861–1868. Available at: https://doi.org/10.1007/978-3-030-31635-8_226.

Zhao, H. et al. (2020b) ‘A Feasible Classification Algorithm for Event-Related Potential (ERP) Based Brain-Computer-Interface (BCI) from IFMBE Scientific Challenge Dataset’, in, pp. 1861–1868. Available at: https://doi.org/10.1007/978-3-030-31635-8_226.