Abstract
Comparing machine learning approaches for motor-activity-related brain computer interfaces
Frontiers in human neuroscience, v 12
2018
Abstract
INTRODUCTION: A brain-computer interface (BCI) is a system that detects consistent spatiotemporal patterns in human brain activity that are related to select mental tasks, such as performing motor imagery, or cognitive workload (Bashashati, Ward, Birch, & Bashashati, 2015; Wolpaw, Birbaumer, McFarland, Pfurtscheller, & Vaughan, 2002). One of the main goal of an active BCI is to provide a new channel of output for the brain that requires voluntary adaptive control by the user, mainly used as a neurorehabilitation tool to improve motor or cognitive performance for people with motor disorders, such as spinal cord injury, amyotrophic lateral sclerosis (ALS), or people in the persistent locked-in state (LIS) (Coyle, Ward, Markham, & McDarby, 2004; Naseer & Hong, 2015; Vallabhaneni, Wang, & He, 2005).
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Details
- Title
- Comparing machine learning approaches for motor-activity-related brain computer interfaces
- Creators
- Lei Wang - Drexel UniversityHasan Ayaz - Drexel University
- Publication Details
- Frontiers in human neuroscience, v 12
- Conference
- 2nd International Neuroergonomics Conference, 2nd (Philadelphia, Pennsylvania, United States, 27 Jun 2018–29 Jun 2018)
- Publisher
- Frontiers
- Resource Type
- Abstract
- Language
- English
- Academic Unit
- Information Science; Drexel Solutions Institute; School of Biomedical Engineering, Science, and Health Systems
- Other Identifier
- 991019186530604721