Using scalp EEG and intracranial EEG signals for predicting epileptic seizures: Review of available methodologies

Syed Muhammad Usman, Shehzad Khalid, Rizwan Akhtar*, Zuner Bortolotto, Zafar Bashir, Haiyang Qiu

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

47 Citations (Scopus)
138 Downloads (Pure)


Patients suffering from epileptic seizures are usually treated with medication and/or surgical procedures. However, in more than 30% of cases, medication or surgery does not effectively control seizure activity. A method that predicts the onset of a seizure before it occurs may prove useful as patients might be alerted to make themselves safe or seizures could be prevented with therapeutic interventions just before they occur. Abnormal neuronal activity, the preictal state, starts a few minutes before the onset of a seizure. In recent years, different methods have been proposed to predict the start of the preictal state. These studies follow some common steps, including recording of EEG signals, preprocessing, feature extraction, classification, and postprocessing. However, online prediction of epileptic seizures remains a challenge as all these steps need further refinement to achieve high sensitivity and low false positive rate. In this paper, we present a comparison of state-of-the-art methods used to predict seizures using both scalp and intracranial EEG signals and suggest improvements to existing methods.

Original languageEnglish
Pages (from-to)258-269
Number of pages12
Early online date19 Aug 2019
Publication statusPublished - 1 Oct 2019


  • Epilepsy prediction
  • Intracranial EEG
  • Preictal state
  • Scalp EEG
  • Seizures prediction methods


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