OBJECTIVE: Little experience exists in the application of source reconstruction methods to recordings from subdural strip and grid electrodes. This study addressed the question, whether reliable and accurate 3D source localization is possible from the Electrocorticogram (ECoG). METHODS: The accuracy of source reconstruction was investigated by simulations and a case study. Simulated sources were used to compute potentials at the electrode positions derived from the MRI of a patient with subdural electrodes. Used procedures were the linear estimation (minimum norm) algorithm and the MUSIC (MUltiple SIgnal Classification) scan. RESULTS: Maxima of linear estimation were attracted to adjacent electrodes. Reliable localization with a localization error 15 mm was only achieved for about 35% of the original source positions. Maxima of the MUSIC metric were identical to original positions for simulations without noise. Noise reduced the percentage of reliable solutions down to a 79.0%. Electrode contacts distant to the source had small influence on localization accuracy. The case study supported simulation results. CONCLUSION: Reliable source reconstruction derived from ECoG can be achieved by the application of the MUSIC algorithm. Linear estimation needs additional compensation mechanisms. SIGNIFICANCE: MUSIC based 3D localization based on ECoG has the potential improving epilepsy diagnosis and cognitive research.
OBJECTIVE: Little experience exists in the application of source reconstruction methods to recordings from subdural strip and grid electrodes. This study addressed the question, whether reliable and accurate 3D source localization is possible from the Electrocorticogram (ECoG). METHODS: The accuracy of source reconstruction was investigated by simulations and a case study. Simulated sources were used to compute potentials at the electrode positions derived from the MRI of a patient with subdural electrodes. Used procedures were the linear estimation (minimum norm) algorithm and the MUSIC (MUltiple SIgnal Classification) scan. RESULTS: Maxima of linear estimation were attracted to adjacent electrodes. Reliable localization with a localization error 15 mm was only achieved for about 35% of the original source positions. Maxima of the MUSIC metric were identical to original positions for simulations without noise. Noise reduced the percentage of reliable solutions down to a 79.0%. Electrode contacts distant to the source had small influence on localization accuracy. The case study supported simulation results. CONCLUSION: Reliable source reconstruction derived from ECoG can be achieved by the application of the MUSIC algorithm. Linear estimation needs additional compensation mechanisms. SIGNIFICANCE: MUSIC based 3D localization based on ECoG has the potential improving epilepsy diagnosis and cognitive research.
Authors: Kitti Kaiboriboon; Hans O Lüders; Mehdi Hamaneh; John Turnbull; Samden D Lhatoo Journal: Nat Rev Neurol Date: 2012-08-07 Impact factor: 42.937
Authors: Eleonora Tamilia; Michel AlHilani; Naoaki Tanaka; Melissa Tsuboyama; Jurriaan M Peters; P Ellen Grant; Joseph R Madsen; Steven M Stufflebeam; Phillip L Pearl; Christos Papadelis Journal: Clin Neurophysiol Date: 2019-01-31 Impact factor: 3.708
Authors: Sana Hannan; Mayo Faulkner; Kirill Aristovich; James Avery; Matthew Walker; David Holder Journal: Neuroimage Clin Date: 2018-09-05 Impact factor: 4.881