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.

Agentic AI Systems represent a significant ad-vancement in artificial intelligence by enabling systems to autonomously perceive, decide, and act in complex environments. This review explores the definition, scope, advantages, challenges, opportunities, and trustworthiness of agentic AI in organizational and societal contexts. Agentic AI offers higher efficiency, scalability, and improved decision making which enables the organizations to streamline operations and enhance their productivity. Despite these advancements Agentic AI has its own challenges such as; safety concerns, accountability, reliability issues and potential misuse remain critical areas for consideration. This paper provides a comprehensive discussion of agentic AI's impact in emphasizing both its transformative potential and the need for continuous oversight and refinement.

Task-evoked pupillary responses reveal the relationship between working memory capacity and its effect on cognitive states. Understanding the effects of cognitive load requires robust analysis of pupillary responses. In this paper, we introduced a Hilbert transform analytic phase based method to compute temporal patterns from pupillary responses. Analysis reveals that sharp change in incorrect task response may be attributed to the cognitive overload. It was also observed that a sharp change and continuation of the ramp in Hilbert unwrapped phase relates to the cognitive dissonance.