Jason Satel
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Papers by Jason Satel
range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges
that prevent full integration into clinical settings.
New method: To translate ERP capabilities from the lab to clinical applications, we have developed methods
like the Halifax Consciousness Scanner (HCS). HCS is essentially a rapid, automated ERP evaluation
of brain functional status. The present study describes the ERP components evoked from auditory tones
and speech stimuli. ERP results were obtained using a 5-min test in 100 healthy individuals. The HCS
sequence was designed to evoke the N100, the mismatch negativity (MMN), P300, the early negative
enhancement (ENE), and the N400. These components reflected sensation, perception, attention, memory,
and language perception, respectively. Component detection was examined at group and individual
levels, and evaluated across both statistical and classification approaches.
Results: All ERP components were robustly detected at the group level. At the individual level, nonparametric
statistical analyses showed reduced accuracy relative to support vector (SVM) machine
classification, particularly for speech-based ERPs. Optimized SVM results were MMN: 95.6%; P300: 99.0%;
ENE: 91.8%; and N400: 92.3%.
Conclusions: A spectrum of individual-level ERPs can be obtained in a very short time. Machine learning
classification improved detection accuracy across a large healthy control sample. Translating ERPs into
clinical applications is increasingly possible at the individual level.
range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges
that prevent full integration into clinical settings.
New method: To translate ERP capabilities from the lab to clinical applications, we have developed methods
like the Halifax Consciousness Scanner (HCS). HCS is essentially a rapid, automated ERP evaluation
of brain functional status. The present study describes the ERP components evoked from auditory tones
and speech stimuli. ERP results were obtained using a 5-min test in 100 healthy individuals. The HCS
sequence was designed to evoke the N100, the mismatch negativity (MMN), P300, the early negative
enhancement (ENE), and the N400. These components reflected sensation, perception, attention, memory,
and language perception, respectively. Component detection was examined at group and individual
levels, and evaluated across both statistical and classification approaches.
Results: All ERP components were robustly detected at the group level. At the individual level, nonparametric
statistical analyses showed reduced accuracy relative to support vector (SVM) machine
classification, particularly for speech-based ERPs. Optimized SVM results were MMN: 95.6%; P300: 99.0%;
ENE: 91.8%; and N400: 92.3%.
Conclusions: A spectrum of individual-level ERPs can be obtained in a very short time. Machine learning
classification improved detection accuracy across a large healthy control sample. Translating ERPs into
clinical applications is increasingly possible at the individual level.