skip to main content
10.1145/3605423.3605452acmotherconferencesArticle/Chapter ViewAbstractPublication PagesicctaConference Proceedingsconference-collections
research-article

Phase Lag Index of Visual-Memory Processing EEG for Computer-Aided AUD Diagnosis

Published: 20 August 2023 Publication History

Abstract

Alcohol use disorder (AUD) causes systemic damage to the human body, including brain structure and function. Computer-aided AUD diagnosis offers fast and reliable detection of AUD to prevent further harm from alcohol consumption. Various machine learning models and feature engineering techniques have been proposed for high system accuracy, reliability, and interpretability. EEG features from brain connectivity are promising due to their interpretability properties. This paper aims to propose a novel feature from task-based EEG signal data for computer-aided AUD diagnosis. The functional connectivity was calculated using the phase lag index (PLI) by choosing eight EEG channels from brain areas processing visual stimuli. The resulting connectivity values were evaluated using discriminant analysis. The proposed feature has yielded a significant discriminant function that proved its differentiation properties. The highest differentiation properties resulted from the gamma band discriminant function with p-value < 0.001 and a canonical correlation of 0.823. The classification accuracy reached 91.7%, and the leave-one-out cross-validation accuracy of 89.6%, showing consistent generalization.

References

[1]
D. M. Khan, N. Kamel, M. Muzaimi, and T. Hill, “Effective Connectivity for Default Mode Network Analysis of Alcoholism,” Brain Connect., vol. 11, no. 1, pp. 12–29, 2021.
[2]
X. Jiang and H. Wang, “Analysing Effective Connectivity of the Math-gifted Brain with Nonlinear Granger Causality,” in 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC), Jun. 2021, vol. 2021, pp. 1932–1936.
[3]
T. S. Ligeza, I. Nowak, M. Maciejczyk, Z. Szygula, and M. Wyczesany, “Acute aerobic exercise enhances cortical connectivity between structures involved in shaping mood and improves self-reported mood: An EEG effective-connectivity study in young male adults,” Int. J. Psychophysiol., vol. 162, no. April 2020, pp. 22–33, 2021.
[4]
B. Akbarian and A. Erfanian, “A framework for seizure detection using effective connectivity, graph theory, and multi-level modular network,” Biomed. Signal Process. Control, vol. 59, p. 101878, 2020.
[5]
S. Furlong, J. R. Cohen, J. Hopfinger, J. Snyder, M. M. Robertson, and M. A. Sheridan, “Resting-state EEG Connectivity in Young Children with ADHD,” J. Clin. Child Adolesc. Psychol., vol. 00, no. 00, pp. 1–17, 2020.
[6]
J. Bosch-Bayard, K. Girini, R. J. Biscay, P. Valdes-Sosa, A. C. Evans, and G. A. Chiarenza, “Resting EEG effective connectivity at the sources in developmental dysphonetic dyslexia. Differences with non-specific reading delay,” Int. J. Psychophysiol., vol. 153, no. April, pp. 135–147, 2020.
[7]
A. B. Buriro, “Classification of alcoholic EEG signals using wavelet scattering transform-based features,” Comput. Biol. Med., vol. 139, no. August, p. 104969, 2021.
[8]
V. K. Mehla, A. Singhal, and P. Singh, “A novel approach for automated alcoholism detection using Fourier decomposition method,” J. Neurosci. Methods, vol. 346, no. June, p. 108945, Dec. 2020.
[9]
A. Anuragi and D. S. Sisodia, “Empirical wavelet transform based automated alcoholism detecting using EEG signal features,” Biomed. Signal Process. Control, vol. 57, p. 101777, Mar. 2020.
[10]
M. Sharma, D. Deb, and U. R. Acharya, “A novel three-band orthogonal wavelet filter bank method for an automated identification of alcoholic EEG signals,” Appl. Intell., vol. 48, no. 5, pp. 1368–1378, Aug. 2017.
[11]
S. Bavkar, B. Iyer, and S. Deosarkar, “Optimal EEG channels selection for alcoholism screening using EMD domain statistical features and harmony search algorithm,” Biocybern. Biomed. Eng., vol. 41, no. 1, pp. 83–96, Jan. 2021.
[12]
K. Buza, “ASTERICS: Projection-based Classification of EEG with Asymmetric Loss Linear Regression and Genetic Algorithm,” in 2020 IEEE 14th International Symposium on Applied Computational Intelligence and Informatics (SACI), May 2020, pp. 000035–000040.
[13]
S. Bavkar, B. Iyer, and S. Deosarkar, “Rapid Screening of Alcoholism: An EEG Based Optimal Channel Selection Approach,” IEEE Access, vol. 7, pp. 99670–99682, 2019.
[14]
S. Patidar, R. B. Pachori, A. Upadhyay, and U. Rajendra Acharya, “An integrated alcoholic index using tunable-Q wavelet transform based features extracted from EEG signals for diagnosis of alcoholism,” Appl. Soft Comput., vol. 50, pp. 71–78, Jan. 2017.
[15]
B. Zhang, H. Zhou, L. Wang, and C. Sung, “Classification based on neuroimaging data by tensor boosting,” in 2017 International Joint Conference on Neural Networks (IJCNN), May 2017, vol. 2017-May, pp. 1174–1179.
[16]
N. Ahmadi, Y. Pei, and M. Pechenizkiy, “Detection of Alcoholism Based on EEG Signals and Functional Brain Network Features Extraction,” in 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), Jun. 2017, vol. 2017-June, pp. 179–184.
[17]
A. Anuragi and D. S. Sisodia, “Alcoholism detection using support vector machines and centered correntropy features of brain EEG signals,” in 2017 International Conference on Inventive Computing and Informatics (ICICI), Nov. 2017, no. Icici, pp. 1021–1026.
[18]
D. M. Khan, N. Yahya, N. Kamel, and I. Faye, “Effective Connectivity in Default Mode Network for Alcoholism Diagnosis,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 29, pp. 796–808, 2021.
[19]
W. Mumtaz, M. N. b M. Saad, N. Kamel, S. S. A. Ali, and A. S. Malik, “An EEG-based functional connectivity measure for automatic detection of alcohol use disorder,” Artif. Intell. Med., vol. 84, no. July 2019, pp. 79–89, Jan. 2018.
[20]
Y. Bae, B. W. Yoo, J. C. Lee, and H. C. Kim, “Automated network analysis to measure brain effective connectivity estimated from EEG data of patients with alcoholism,” Physiol. Meas., vol. 38, no. 5, pp. 759–773, May 2017.
[21]
C. Kamarajan, “Random Forest Classification of Alcohol Use Disorder Using EEG Source Functional Connectivity, Neuropsychological Functioning, and Impulsivity Measures,” Behav. Sci. (Basel)., vol. 10, no. 3, p. 62, Mar. 2020.
[22]
H. Begleiter, “EEG Database Data Set,” UCI Machine Learning Repository, 1999. https://archive.ics.uci.edu/ml/datasets/EEG+Database.
[23]
R. Carter, The Human Brain Book: An Illustrated Guide to its Structure, Function, and Disorders. 2019.
[24]
M. Hardmeier, F. Hatz, H. Bousleiman, C. Schindler, C. J. Stam, and P. Fuhr, “Reproducibility of functional connectivity and graph measures based on the phase lag index (PLI) and weighted phase lag index (wPLI) derived from high resolution EEG,” PLoS One, vol. 9, no. 10, 2014.
[25]
M. X. Cohen, Analyzing Neural Time Series Data. The MIT Press, 2019.
[26]
A. Gramfort, “MEG and EEG data analysis with MNE-Python,” Front. Neurosci., vol. 7, pp. 1–13, 2013.
[27]
J. F. Hair Jr, W. C. Black, B. J. Babin, R. E. Anderson, W. C. Black, and R. E. Anderson, Multivariate Data Analysis, 8th ed. Hampshire: Cengage Learning EMEA, 2019.
[28]
C. Kamarajan, “Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures,” Behav. Sci. (Basel)., vol. 12, no. 5, p. 128, Apr. 2022.
[29]
Z. Song, J. Chen, Z. Wen, and L. Zhang, “Abnormal functional connectivity and effective connectivity between the default mode network and attention networks in patients with alcohol-use disorder,” Acta radiol., vol. 62, no. 2, pp. 251–259, 2021.

Cited By

View all

Index Terms

  1. Phase Lag Index of Visual-Memory Processing EEG for Computer-Aided AUD Diagnosis

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    ICCTA '23: Proceedings of the 2023 9th International Conference on Computer Technology Applications
    May 2023
    270 pages
    ISBN:9781450399579
    DOI:10.1145/3605423
    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 20 August 2023

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tags

    1. EEG
    2. Feature selection
    3. alcohol use disorder (AUD)
    4. brain connectivity
    5. phase lag index (PLI)

    Qualifiers

    • Research-article
    • Research
    • Refereed limited

    Funding Sources

    Conference

    ICCTA 2023

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • 0
      Total Citations
    • 30
      Total Downloads
    • Downloads (Last 12 months)13
    • Downloads (Last 6 weeks)1
    Reflects downloads up to 18 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    HTML Format

    View this article in HTML Format.

    HTML Format

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media