SVM-Based System for Prediction of Epileptic Seizures From iEEG Signal
Our work emphasizes the understanding of clinical considerations and proper translation of these clinical considerations into data-analytic modeling assumptions. Several design choices during preprocessing and postprocessing are investigated for their effect on seizure prediction accuracy.
Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach
We present an electrophysiological connectome (eConnectome) approach to study underlying brain networks in a noninvasive manner. This approach was directly tested by estimating epileptic networks from EEG/MEG measurements in patients suffering from focal epilepsy. The results obtained from the proposed approach were consistent with invasive clinical findings, in these patients.
Combined single neuron unit activity and local field potential oscillations in a human visual recognition memory task
Current neurotechnologies typically probe a limited range of the vast scale of human brain electrophysiological activities, focusing either on the micro-electrode recordings of single neurons and their local assemblies or the macro-electrode field potential of large neural populations. This paper describes an analytical approach whereby high frequency oscillations and single neuron activity recorded from hybrid macro- and micro-electrodes during cognitive processing are both treated as point processes.