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EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection

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Abstract

Epilepsy is a common non-communicable, group of neurological disorders affecting more than 50 million individuals worldwide. Researchers are working to automatically detect epileptic activities through Electroencephalography (EEG) signal analysis, and artificial intelligence techniques. Good quality, open-source, and free EEG data acts as a catalyst in the on-going research to early diagnose this disease. The present review focuses on reporting EEG datasets for automatic epilepsy diagnosis and seizure detection for the past three decades. Upon a thorough search analysis, 28 publicly available, and private EEG datasets have been compiled and compared for adult and paediatric human populations suffering from epilepsy. The discussion of the advantages and limitations of the present datasets has been done based on several parameters. Their associated, recent experimental pipelines have also been discussed. CHB-MIT and Bonn remain the benchmark datasets in this field. EEG meta-data has been released to tackle large EEG datasets like CHB-MIT and Siena Scalp. In conclusion, an increasing trend in the release of open-source EEG datasets has been observed with varied clinical setups and demographics. A standardized data development protocol is needed to develop generalized artificial intelligence techniques for the early detection of epileptic events.

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Availability of data and material

Links to all the datasets have been provided in appropriate sections.

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  1. Department of ECE, DTU, Delhi, India

    Palak Handa

  2. Department of ECE, IGDTUW, Delhi, India

    Monika Mathur & Nidhi Goel

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  1. Palak Handa
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Correspondence to Nidhi Goel.

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Handa, P., Mathur, M. & Goel, N. EEG Datasets in Machine Learning Applications of Epilepsy Diagnosis and Seizure Detection. SN COMPUT. SCI. 4, 437 (2023). https://doi.org/10.1007/s42979-023-01958-z

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