No abstract

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 electroencephalogram (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.

Co-adapted learning involves complex, dynamically unfolding interactions between human and artificial pedagogical agents (PAs) during learning with intelligent systems. In general, these interactions lead to effective learning when (1) learners correctly monitor and regulate their cognitive and metacognitive processes in response to internal (e.g., accurate metacognitive judgments followed by the selection of effective learning strategies) and external (e.g., response to agents' prompting and feedback) conditions, and (2) pedagogical agents can adequately and correctly detect, track, model, and foster learners' self-regulatory processes. In this study, we tested the effectiveness of PAs' prompting and feedback on learners' self-regulated learning about the human circulatory system with MetaTutor, an adaptive, multi-agent learning environment. Sixty-nine (N=69) undergraduates learned about the topic with MetaTutor, during a 2-hour session under one of three conditions: prompt and feedback (PF), prompt-only (PO), and no prompt (NP) condition. The PF condition received timely prompts from several pedagogical agents to deploy various SRL processes and received immediate directive feedback concerning the deployment of the processes. The PO condition received the same timely prompts, without feedback. Finally, the NP condition learned without assistance from the agents. Results indicate that those in the PF condition had significantly higher learning efficiency scores than those in both the PO and control conditions. In addition, log-file data provided evidence of the effectiveness of the PA's timely scaffolding and feedback in facilitating learners' (in the PF condition) metacognitive monitoring and regulation during learning.

No abstract

No abstract