Akaysha C. Tang, Matthew T. Sutherland, and Zhen Yang
- Published in print:
- 2011
- Published Online:
- September 2011
- ISBN:
- 9780195393798
- eISBN:
- 9780199897049
- Item type:
- chapter
- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780195393798.003.0009
- Subject:
- Neuroscience, Behavioral Neuroscience, Development
To understand cognition and emotion in the real world, it is critical to investigate the phenomena of interest within the rich context of moment-to-moment variations in the real world, which we ...
More
To understand cognition and emotion in the real world, it is critical to investigate the phenomena of interest within the rich context of moment-to-moment variations in the real world, which we assume is at least in part encoded in the high-dimensional state of the brain. Here the chapter reviews empirical evidence from a series of novel validation studies that demonstrate the technical capabilities of one blind source separation (BSS) algorithm—second-order blind identification (SOBI)—in enabling neuronscientists and clinicians to investigate human brain functions, cognition, and behavior using the electroencephalography (EEG). The chapter concludes that by shifting from an EEG-sensor-based to a neuronal-source-based characterization of brain states, one may better capture the rich context of moment-to-moment variations in the real world.Less
To understand cognition and emotion in the real world, it is critical to investigate the phenomena of interest within the rich context of moment-to-moment variations in the real world, which we assume is at least in part encoded in the high-dimensional state of the brain. Here the chapter reviews empirical evidence from a series of novel validation studies that demonstrate the technical capabilities of one blind source separation (BSS) algorithm—second-order blind identification (SOBI)—in enabling neuronscientists and clinicians to investigate human brain functions, cognition, and behavior using the electroencephalography (EEG). The chapter concludes that by shifting from an EEG-sensor-based to a neuronal-source-based characterization of brain states, one may better capture the rich context of moment-to-moment variations in the real world.
