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Cited by 80EEG and Brain-Computer InterfacesEmotion and Mood RecognitionArtificial Intelligence in Healthcare

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

The Necessity of Leave One Subject Out (LOSO) Cross Validation for EEG Disease Diagnosis

Kunjan, Sajeev, Grummett, T. S., Pope, K. J., Powers, D. M. W., Fitzgibbon, S. P., Bastiampillai, T., Battersby, M., Lewis, T. W.·2021·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-86993-9_50
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Registry

Crossref · 10.1007/978-3-030-86993-9_50

Cites

2 of 2 references verified0 citation errors · 0 not checkable

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  • All models were evaluated using LOSO cross-validation: each subject served as the test set exactly once, with all remaining subjects forming the training set.

    “All models were evaluated using LOSO cross-validation [36]: each subject served as the test set exactly once, with all remaining subjects forming the training set.”

    From ERP-XTTN: interpretable prototype-guided cross-attention for cross-subject ERP classification (2026) · cites this paper as [36]

Citations

Cites

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Research works printed but not linked: 20

  1. Arribas, J.I., Calhoun, V.D., Adali, T.: Automatic Bayesian classification of healthy controls, bipolar disorder, and schizophrenia using intrinsic connectivity maps from fMRI data. IEEE Trans. Biomed. Eng. 57(12), 2850–2860 (2010) · DOI
  2. Başar, E., Güntekin, B.: A review of brain oscillations in cognitive disorders and the role of neurotransmitters. Brain Res. 1235, 172–193 (2008) · DOI
  3. Boostani, R., Sadatnezhad, K., Sabeti, M.: An efficient classifier to diagnose of schizophrenia based on the EEG signals. Expert Syst. Appl. 36(3), 6492–6499 (2009) · DOI
  4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001). https://doi.org/10.1023/A:1010933404324 · DOI · Paper page
  5. Challis, E., Hurley, P., Serra, L., Bozzali, M., Oliver, S., Cercignani, M.: Gaussian process classification of Alzheimer’s disease and mild cognitive impairment from resting-state fMRI. Neuroimage 112, 232–243 (2015) · DOI
  6. Delorme, A., et al.: EEGLAB, SIFT, NFT, BCILAB, and ERICA: new tools for advanced EEG processing. Comput. Intell. Neurosci. 2011, 5–6 (2011) · DOI
  7. Fazli, S., Popescu, F., Danóczy, M., Blankertz, B., Müller, K.R., Grozea, C.: Subject-independent mental state classification in single trials. Neural Netw. 22(9), 1305–1312 (2009) · DOI
  8. Goodfellow, I., Bengio, Y., Courville, A., Bengio, Y.: Deep Learning, vol. 1. MIT Press, Cambridge (2016)
  9. Grummett, T.: Analytical complexity required for diagnosing bipolar disorder, schizophrenia, and dementia. Ph.D. thesis, College of Science and Engineering, Flinders University, South Australia, December 2018
  10. Jamal, W., Das, S., Oprescu, I.A., Maharatna, K., Apicella, F., Sicca, F.: Classification of autism spectrum disorder using supervised learning of brain connectivity measures extracted from synchrostates. J. Neural Eng. 11(4), 046019 (2014) · DOI · Paper page
  11. Karimi, S., Mijani, A., Talebian, M., Mirzakuchaki, S.: Comparison of the P300 detection accuracy related to the BCI speller and image recognition scenarios. arXiv preprint arXiv:1912.11371 (2019) · arXiv
  12. Khan, K.A., Shanir, P., Khan, Y.U., Farooq, O.: A hybrid local binary pattern and wavelets based approach for EEG classification for diagnosing epilepsy. Expert Syst. Appl. 140, 112895 (2020) · DOI
  13. Mullen, T., Delorme, A., Kothe, C., Makeig, S.: An electrophysiological information flow toolbox for EEGLAB. Biol. Cybern. 83, 35–45 (2010)
  14. Peters, H., et al.: More consistently altered connectivity patterns for cerebellum and medial temporal lobes than for amygdala and striatum in schizophrenia. Front. Hum. Neurosci. 10, 55 (2016)
  15. Rashid, B., et al.: Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage 134, 645–657 (2016) · DOI
  16. Ravi, K., Palaniappan, R.: Leave-one-out authentication of persons using 40 Hz EEG oscillations. In: EUROCON 2005-The International Conference on “Computer as a Tool”, vol. 2, pp. 1386–1389. IEEE (2005) · DOI
  17. Sabeti, M., Katebi, S., Boostani, R.: Entropy and complexity measures for EEG signal classification of schizophrenic and control participants. Artif. Intell. Med. 47(3), 263–274 (2009) · DOI
  18. Watanabe, T., Kessler, D., Scott, C., Angstadt, M., Sripada, C.: Disease prediction based on functional connectomes using a scalable and spatially-informed support vector machine. Neuroimage 96, 183–202 (2014) · DOI
  19. Welch, P.: The use of fast Fourier transform for the estimation of power spectra: a method based on time averaging over short, modified periodograms. IEEE Trans. Audio Electroacoust. 15(2), 70–73 (1967) · DOI
  20. Zhu, M., Jie, N., Jiang, T.: Automatic classification of schizophrenia using resting-state functional language network via an adaptive learning algorithm. In: Medical Imaging 2014: Computer-Aided Diagnosis, vol. 9035, p. 903522. International Society for Optics and Photonics (2014) · DOI

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Metadata

Authors
Kunjan, S., Grummett, T. S., Pope, K. J., Powers, D. M. W., Fitzgibbon, S. P., Bastiampillai, T., Battersby, M., & Lewis, T. W.
Affiliations
  • Flinders University (AU)
  • University of Oxford (GB)
Corresponding author
Kunjan, Sajeev
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2021
Pages
558-567
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-86993-9_50
OpenAlex
W3200600885
Cited by
80
References
2
Citations by year
2026: 20 · 2025: 29 · 2024: 15 · 2023: 12 · 2022: 3 · 2021: 1
FWCI
12.43
Type
conference paper
Language
en
Open access
closed
Licence
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Physical Sciences › Computer Science › Artificial Intelligence › Emotion and Mood Recognition
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
leave-one-subject-out cross-validation, schizophrenia detection, multilayer perceptron, interindividual variability, EEG feature extraction

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