A robust seizure prediction methodology would enable a ‘closed-loop’ system that would only activate as impending seizure activity is detected. Such a system would eliminate ongoing stimulation to the brain, thereby eliminating such side effects as coughing, hoarseness, voice alteration, and paresthesias (Murphy et al., 1998, Ben-Menachem, 2001), while preserving overall battery life of the system. The seizure prediction and detection algorithm uses Phase/Amplitude Lock Values (PLV/ALV) which calculate the difference of phase and amplitude between EEG electrodes local and remote to the epileptic event. PLV is used as the seizure prediction marker and signifies the emergence of abnormal neuronal activations through local neuron populations. PLV/ALVs are used as seizure detection markers to demarcate the seizure event, or when the local seizure event has propagated throughout the brain turning into a grand-mal event. We verify the performance of this methodology against the ‘CHB-MIT Scalp EEG Database’ which features seizure attributes for testing. Through this testing, we can demonstrate a high degree of sensivity and precision of our methodology between pre-ictal and ictal events.

Cortical spatiotemporal signal patterns based on object recognition can be discerned from visual stimulation. These are in the form of amplitude modulation (AM) and phase modulation (PM) patterns, which contain perceptual information gathered from sensory input. A high-density Electroencephalograph (EEG) device consisting of 48 electrodes with a spacing of 5 mm was utilized to measure frontal lobe activity in order to capture event-related potentials from visual stimuli. Four randomized stimuli representing different levels of salient responsiveness were measured to determine if mild stimuli can be discerned from more extreme stimuli. AM/PM response patterns were detected between mild and more salient stimuli across participants. AM patterns presented distinct signatures for each stimulus. AM patterns had the highest number of incidents detected in the middle of the frontal lobe. Through this work, we can expand our encyclopedia of neural signatures to object recognition, and provide a broader understanding of quantitative neural responses to external stimuli. The results provide a quantitative approach utilizing spatiotemporal patterns to analyze where distinct AM patterns can be linked to object perception.