Ptolemaios G Sarrigiannis1, Yifan Zhao2, Hua-Liang Wei2, Stephen A Billings2, Jayne Fotheringham3, Marios Hadjivassiliou4. 1. Department of Clinical Neurophysiology, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, United Kingdom. Electronic address: ptolemaios.sarrigiannis@sth.nhs.uk. 2. Department of Automatic Control and Systems Engineering, University of Sheffield, Sheffield, United Kingdom. 3. Department of Clinical Neurophysiology, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, United Kingdom. 4. Department of Neurology, Sheffield Teaching Hospitals NHS Foundation Trust, Royal Hallamshire Hospital, Sheffield, United Kingdom.
Abstract
OBJECTIVE: To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures. METHODS: A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence. The methods were then applied to real EEG data. RESULTS: Analysis of both simulated and real EEG data demonstrates that ERR-causality successfully detects dynamically evolving changes between two signals, with very high time resolution, dependent on the sampling rate of the data. Our method can properly detect both linear and non-linear effects, encountered during analysis of focal and generalised seizures. CONCLUSIONS: We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisation in the linear and non-linear domains. It computes directionality of information flow with corresponding time lags. SIGNIFICANCE: This novel dynamic real time EEG signal analysis unveils hidden neural network interactions with a very high time resolution. These interactions cannot be adequately resolved by the traditional methods of coherence and cross-correlation, which provide limited results in the presence of non-linear effects and lack fidelity for changes appearing over small periods of time.
OBJECTIVE: To introduce a new method of quantitative EEG analysis in the time domain, the error reduction ratio (ERR)-causality test. To compare performance against cross-correlation and coherence with phase measures. METHODS: A simulation example was used as a gold standard to assess the performance of ERR-causality, against cross-correlation and coherence. The methods were then applied to real EEG data. RESULTS: Analysis of both simulated and real EEG data demonstrates that ERR-causality successfully detects dynamically evolving changes between two signals, with very high time resolution, dependent on the sampling rate of the data. Our method can properly detect both linear and non-linear effects, encountered during analysis of focal and generalised seizures. CONCLUSIONS: We introduce a new quantitative EEG method of analysis. It detects real time levels of synchronisation in the linear and non-linear domains. It computes directionality of information flow with corresponding time lags. SIGNIFICANCE: This novel dynamic real time EEG signal analysis unveils hidden neural network interactions with a very high time resolution. These interactions cannot be adequately resolved by the traditional methods of coherence and cross-correlation, which provide limited results in the presence of non-linear effects and lack fidelity for changes appearing over small periods of time.
Authors: Daniel J Blackburn; Yifan Zhao; Matteo De Marco; Simon M Bell; Fei He; Hua-Liang Wei; Sarah Lawrence; Zoe C Unwin; Michelle Blyth; Jenna Angel; Kathleen Baster; Thomas F D Farrow; Iain D Wilkinson; Stephen A Billings; Annalena Venneri; Ptolemaios G Sarrigiannis Journal: Brain Sci Date: 2018-07-17