High-density surface microelectrodes for electrocorticography (ECoG) have become more common in recent years for recording electrical signals from the cortex. With an acceptable invasiveness/ signal fidelity trade-off and high spatial resolution, micro-ECoG is a promising tool to resolve fine task-related spatial-temporal dynamics. However, volume conduction -not a negligible phenomenon-is likely to frustrate efforts to obtain reliable and resolved signals from a sub-millimeter electrode array. To address this issue, we performed an independent component analysis (ICA) on micro-ECoG recordings of somatosensory-evoked potentials (SEPs) elicited by median nerve stimulation in three human patients undergoing brain surgery for tumor resection. Using well-described cortical responses in SEPs, we were able to validate our results showing that the array could segregate different functional units possessing unique, highly localized spatial distributions. The representation of signals through the root-mean-square (rms) maps and the signal-to-noise ratio (SNR) analysis emphasizes the advantages of adopting a source analysis approach on micro-ECoG recordings in order to obtain a clear picture of cortical activity. The implications are twofold: while on one side ICA may be used as a spatial-temporal filter extracting micro-signal components relevant to tasks for brain-computer interface (BCI) applications, it could also be adopted to accurately identify the sites of nonfunctional regions for clinical purposes.

Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro- Electrocorticography / Rembado, I; Castagnola, E; Turella, L; Ius, T; Budai, R; Ansaldo, A; Angotzi, Gn; Debertoldi, F; Ricci, D; Skrap, M; Fadiga, L. - In: INTERNATIONAL JOURNAL OF NEURAL SYSTEMS. - ISSN 0129-0657. - 27:4(2017), pp. 1-13. [10.1142/S0129065716500520]

Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro- Electrocorticography

Ius T;
2017

Abstract

High-density surface microelectrodes for electrocorticography (ECoG) have become more common in recent years for recording electrical signals from the cortex. With an acceptable invasiveness/ signal fidelity trade-off and high spatial resolution, micro-ECoG is a promising tool to resolve fine task-related spatial-temporal dynamics. However, volume conduction -not a negligible phenomenon-is likely to frustrate efforts to obtain reliable and resolved signals from a sub-millimeter electrode array. To address this issue, we performed an independent component analysis (ICA) on micro-ECoG recordings of somatosensory-evoked potentials (SEPs) elicited by median nerve stimulation in three human patients undergoing brain surgery for tumor resection. Using well-described cortical responses in SEPs, we were able to validate our results showing that the array could segregate different functional units possessing unique, highly localized spatial distributions. The representation of signals through the root-mean-square (rms) maps and the signal-to-noise ratio (SNR) analysis emphasizes the advantages of adopting a source analysis approach on micro-ECoG recordings in order to obtain a clear picture of cortical activity. The implications are twofold: while on one side ICA may be used as a spatial-temporal filter extracting micro-signal components relevant to tasks for brain-computer interface (BCI) applications, it could also be adopted to accurately identify the sites of nonfunctional regions for clinical purposes.
2017
bci; ica; micro-ecog; sep; adult; brain neoplasms; electric stimulation; electrocorticography; equipment design; glioma; humans; male; median nerve; microelectrodes; middle aged; motor cortex; somatosensory cortex; touch perception; evoked potentials; somatosensory; signal processing; computer-assisted; computer networks and communications
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Independent Component Decomposition of Human Somatosensory Evoked Potentials Recorded by Micro- Electrocorticography / Rembado, I; Castagnola, E; Turella, L; Ius, T; Budai, R; Ansaldo, A; Angotzi, Gn; Debertoldi, F; Ricci, D; Skrap, M; Fadiga, L. - In: INTERNATIONAL JOURNAL OF NEURAL SYSTEMS. - ISSN 0129-0657. - 27:4(2017), pp. 1-13. [10.1142/S0129065716500520]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1652916
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