High variability between individual subjects and recording sessions is a known fact about scalp recorded EEG signal. While some do, the majority of the EEG based machine learning studies do not attempt to assess performance of algorithms across recording sessions or across subjects, instead studies use the whole data-set available for training and testing, using an established k-fold cross validation technique and thus missing performance in a real-life setting on an unseen subject. This study primarily aimed to show how important is to have a leave-one-subject-out (LOSO) evaluation done for any scalp recorded EEG based machine learning. This study also demonstrates effectiveness of a Multilayer Perceptron (MLP) in getting good LOSO accuracy from balanced, clean EEG data, without any pre-processing in comparison with traditional machine learning algorithms. The study used data from participants diagnosed with schizophrenia, as well as a group of participants with no known neurological disorder. Classification was done using traditional methods and MLP to classify the participants as belonging to disease or control subjects. Results shows that 85% accuracy on unseen subject was achievable from a clean data-set. MLP is seen to be effective in finding features by which schizophrenia could be detected from clean EEG data. LOSO evaluation done with this proven MLP configuration using carefully and intentionally corrupted data clearly indicate that for disease diagnosis, the k-fold classification result is misleading. Therefore, evaluation of any scalp recorded EEG based disease classification method must use a LOSO style cross-validation.
|Title of host publication||Brain Informatics|
|Subtitle of host publication||14th International Conference, BI 2021 Virtual Event, September 17–19, 2021 Proceedings|
|Editors||Mufti Mahmud, M Shamim Kaiser, Stefano Vassanelli, Qionghai Dai, Ning Zhong|
|Place of Publication||Switzerland|
|Number of pages||10|
|Publication status||Published - 2021|
|Event||14th International Conference on Brain Informatics, BI 2021 - Virtual, Online|
Duration: 17 Sep 2021 → 19 Sep 2021
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||14th International Conference on Brain Informatics, BI 2021|
|Period||17/09/21 → 19/09/21|
Bibliographical noteFunding Information:
This work was supported by the National Health and Medical Research Council, the Flinders Medical Centre Foundation, the Clinician?s Special Purpose Fund of the Flinders Medical Centre, and an equipment grant from the Wellcome Trust, London, U.K.. Acknowledgements. We thank Prof Michael Baigent and Dr Randall Long (Department of Psychiatry and Flinders University and Medical Centre), Dr Cate Houen (Central Adelaide Local Health Network, SA Psychiatry Training Unit), Dr Emma Whitham and Prof John Willoughby (Department of Neurology, Flinders University and Medical), for their contributions in collecting and classifying the clinical material and providing the clinical background.
© 2021, Springer Nature Switzerland AG.
- Cross-subject classification
- Leave one subject out
- Machine learning