Analysis and prediction of the freezing of gait using EEG brain dynamics

A. M. Ardi Handojoseno, James M. Shine, Tuan N. Nguyen, Yvonne Tran, Simon J. G. Lewis, Hung T. Nguyen

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)


Freezing of Gait (FOG) is a common symptom in the advanced stages of Parkinson's disease (PD), which significantly affects patients' quality of life. Treatment options offer limited benefit and there are currently no mechanisms able to effectively detect FOG before it occurs, allowing time for a sufferer to avert a freezing episode. Electroencephalography (EEG) offers a novel technique that may be able to address this problem. In this paper, we investigated the univariate and multivariate EEG features determined by both Fourier and wavelet analysis in the confirmation and prediction of FOG. The EEG power measures and network properties from 16 patients with PD and FOG were extracted and analyzed. It was found that both power spectral density and wavelet energy could potentially act as biomarkers during FOG. Information in the frequency domain of the EEG was found to provide better discrimination of EEG signals during transition to freezing than information coded in the time domain. The performance of the FOG prediction systems improved when the information from both domains was used. This combination resulted in a sensitivity of 86.0%, specificity of 74.4%, and accuracy of 80.2% when predicting episodes of freezing, outperforming current accelerometry- based tools for the prediction of FOG.

Original languageEnglish
Article number6985641
Pages (from-to)887-896
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number5
Publication statusPublished - 1 Sep 2015
Externally publishedYes


  • Biomedical signal processing
  • Electroencephalogram
  • Freeing of gait (FOG)
  • Movement disorders
  • Parkinson's disease (PD).


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