Toward brain state decoding and real-time tracking: Modeling nonstationarity in human electroencephalography (EEG)
Guest Speaker: Shawn Hsu
As the human brain performs cognitive functions or generates spontaneous mental processes within ever-changing, real-world environments, states of the brain are inevitably nonstationary. This calls for innovative approaches to obtain objective and quantitative insights into hidden cognitive and mental states and study the dynamics of brain states that give rise to behaviors and mental disorders. Despite electroencephalography (EEG) offering a noninvasive, portable, real-time measurement of brain activity, an urgent need remains for computational tools to effectively decode brain states from continuous, unlabeled EEG data, to quantitatively assess state changes, and to provide neuroscientific insights.
In this talk, I will present three computational approaches for quantitative assessment of brain-state dynamics by modeling multichannel, nonstationary EEG data at the level of functional brain sources. These include a hypothesis-driven approach which uses independent component analysis (ICA) to model distinct source activities under different brain states, a data-driven approach (Adaptive Mixture ICA) for exploring nonstationary dynamics of continuous and unlabeled data, and the Online Recursive ICA approach for adaptive tracking of the nonstationary sources that underlie continuous state changes. I will present the results of applying these approaches to characterizing EEG dynamics during sleep for automatic staging, assessing transitions between alert and drowsy states in a simulated driving experiment, and exploring mental state changes during a guided-imagery hypnotherapy. Finally, I will discuss the challenges toward building a real-time brain monitoring system and some of the strategies and ongoing efforts to address those problems.