An Integrated Neuroscience and Engineering Approach to Classifying Human Brain-States
Abstract
Harnessing the capability to read and classify brainwaves into the myriad of possible human cognitive states (referred to as brain-states) has been a long-standing engineering challenge. Brain signals are generally captured non-invasively by electroencephalography (EEG) a cheapand portable brain imaging tool with time resolution fine enough to track the dynamic changes of different brain-states. While the scientific quest to map human brain function has exploded in the last two decades, the ability to link patterns in EEG signals to specific cognitive states remains elusive, owing perhaps to limited crosstalk between the fields of neuroscience and engineering. Here, we report a framework we developed that leverages the latest neuroscience knowledge to transform the current engineering approach to brain-state classification. We used inverse imaging techniques and surface-based spatial normalization algorithms to interpret brain signals across a large pool of subjects and cross-validated our findings with simulated and actual brain data. We concluded that decoding brain signals in the brain (a.k.a. source-space approach) confers two major benefits compared to classifying brain signals directly on the EEG sensor readings (a.k.a. sensor-space approach): i) it provides a principled method to transfer data from one subject to another, thereby reducing BCI calibration time; and ii) it increases classification accuracy regardless of which dimensionality-reduction techniques were used to preprocess the data. Overall, this innovative approach establishes a formal integrated neuroengineering framework that allows us to capitalize on the similarity in brain function across subjects (a traditional neuroscience approach) and optimally incorporate a priori information to maximize classification algorithm performance at an individual level (a traditional engineering goal) that ultimately improves our ability to classify human brain-states.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 22, 2015
- Accession Number
- AD1001845
Entities
People
- Adrian K. Lee
- Mark Wronkiewicz
Organizations
- University of Washington