Name
Abstract | ||
|---|---|---|
The EEG recording of a person has been considered as one potential component within an overall wearable sensor system that predicts the onset of mental health problems. Such a smart EEG sensor should provide detailed sensory information, be easy to use, and to put on and take off and whilst being very ergonomic the design should aim at a very low final end user cost to ensure the widest possible take up by the e-Health community. The work reported here describes the design of such a sensor, the performance and its use during extensive clinical trials aimed to establish the rules that link physiology sensing to mental health prediction. |
Year | DOI | Venue |
|---|---|---|
2012 | null | HEALTHINF |
Keywords | Field | DocType |
Depression,EEG sensing,Mental health,Wearable smart sensor | Computer science,Computer security,Human factors and ergonomics,Mental illness,Knowledge management,Human–computer interaction,Electroencephalography | Conference |
Volume | Issue | Citations |
null | null | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dennis Majoe | 1 | 68 | 10.20 |
| Jürg Gutknecht | 2 | 137 | 32.23 |
| Hong Peng | 3 | 0 | 0.68 |