TY - JOUR
T1 - Localization of Epileptic Brain Responses to Single-Pulse Electrical Stimulation by Developing an Adaptive Iterative Linearly Constrained Minimum Variance Beamformer
AU - Shirani, Sepehr
AU - Valentin, Antonio
AU - Abdi-Sargezeh, Bahman
AU - Alarcon, Gonzalo
AU - Sanei, Saeid
PY - 2023/10
Y1 - 2023/10
N2 - Delayed responses (DRs) to single pulse electrical stimulation (SPES) in patients with severe refractory epilepsy, from their intracranial recordings, can help to identify regions associated with epileptogenicity. Automatic DR localization is a large step in speeding up the identification of epileptogenic focus. Here, for the first time, an adaptive iterative linearly constrained minimum variance beamformer (AI-LCMV) is developed and employed to localize the DR sources from intracranial electroencephalogram (EEG) recorded using subdural electrodes. The prime objective here is to accurately localize the regions for the corresponding DRs using an adaptive localization method that exploits the morphology of DRs as the desired sources. The traditional closed-form linearly constrained minimum variance (CF-LCMV) solution is meant for tracking the sources with dominating power. Here, by incorporating the morphology of DRs, as a constraint, to an iterative linearly constrained minimum variance (LCMV) solution, the array of subdural electrodes is used to localize the low-power DRs, some not even visible in any of the electrode signals. The results from the cases included in this study also indicate more distinctive locations compared to those achievable by conventional beamformers. Most importantly, the proposed AI-LCMV is able to localize the DRs invisible over other electrodes.
AB - Delayed responses (DRs) to single pulse electrical stimulation (SPES) in patients with severe refractory epilepsy, from their intracranial recordings, can help to identify regions associated with epileptogenicity. Automatic DR localization is a large step in speeding up the identification of epileptogenic focus. Here, for the first time, an adaptive iterative linearly constrained minimum variance beamformer (AI-LCMV) is developed and employed to localize the DR sources from intracranial electroencephalogram (EEG) recorded using subdural electrodes. The prime objective here is to accurately localize the regions for the corresponding DRs using an adaptive localization method that exploits the morphology of DRs as the desired sources. The traditional closed-form linearly constrained minimum variance (CF-LCMV) solution is meant for tracking the sources with dominating power. Here, by incorporating the morphology of DRs, as a constraint, to an iterative linearly constrained minimum variance (LCMV) solution, the array of subdural electrodes is used to localize the low-power DRs, some not even visible in any of the electrode signals. The results from the cases included in this study also indicate more distinctive locations compared to those achievable by conventional beamformers. Most importantly, the proposed AI-LCMV is able to localize the DRs invisible over other electrodes.
KW - Humans
KW - Brain/physiology
KW - Electroencephalography/methods
KW - Epilepsy
KW - Drug Resistant Epilepsy/therapy
KW - Brain Mapping/methods
KW - Electric Stimulation/methods
U2 - 10.1142/S0129065723500508
DO - 10.1142/S0129065723500508
M3 - Article
C2 - 37567860
SN - 0129-0657
VL - 33
SP - 2350050
JO - International Journal of Neural Systems
JF - International Journal of Neural Systems
IS - 10
ER -