TY - GEN
T1 - A Wearable Brain Machine Interface Architecture for Regulation of Energy in Hypercortisolism
AU - Azgomi, Hamid Fekri
AU - Faghih, Rose T.
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - Hypercortisolism is associated with tiredness and fatigue during the day and disturbed sleep at night. Our goal is to employ a wearable brain machine interface architecture to regulate one's energy in hypercortisolism. First, we present a state-space model to infer a hidden cognitive energy-related state from one's Cortisol secretion patterns. Particularly, we consider circadian upper and lower bound envelope curves on Cortisol levels, and timings of hypothalamic pulsatile activity underlying Cortisol secretion as observations. We then use Bayesian filtering to estimate the hidden cognitive energy-related state. Finally, we close the loop using a knowledge-based control approach. In a simulation study based on experimental data, we illustrate the feasibility of designing a wearable brain machine interface architecture for energy regulation in hypercortisolism. In this architecture, we infer one's cognitive energy-related state seamlessly rather than monitoring the brain activity directly and close the loop using fuzzy control. This simulation study is a first step towards the ultimate goal of managing hypercortisolism in real-world situations.
AB - Hypercortisolism is associated with tiredness and fatigue during the day and disturbed sleep at night. Our goal is to employ a wearable brain machine interface architecture to regulate one's energy in hypercortisolism. First, we present a state-space model to infer a hidden cognitive energy-related state from one's Cortisol secretion patterns. Particularly, we consider circadian upper and lower bound envelope curves on Cortisol levels, and timings of hypothalamic pulsatile activity underlying Cortisol secretion as observations. We then use Bayesian filtering to estimate the hidden cognitive energy-related state. Finally, we close the loop using a knowledge-based control approach. In a simulation study based on experimental data, we illustrate the feasibility of designing a wearable brain machine interface architecture for energy regulation in hypercortisolism. In this architecture, we infer one's cognitive energy-related state seamlessly rather than monitoring the brain activity directly and close the loop using fuzzy control. This simulation study is a first step towards the ultimate goal of managing hypercortisolism in real-world situations.
UR - http://www.scopus.com/inward/record.url?scp=85077975180&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077975180&partnerID=8YFLogxK
U2 - 10.1109/IEEECONF44664.2019.9049057
DO - 10.1109/IEEECONF44664.2019.9049057
M3 - Conference contribution
AN - SCOPUS:85077975180
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 254
EP - 258
BT - Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
A2 - Matthews, Michael B.
PB - IEEE Computer Society
T2 - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Y2 - 3 November 2019 through 6 November 2019
ER -