Back to Search
Start Over
Age-related differences in SSVEP-based BCI performance
- Source :
- Neurocomputing. 250:57-64
- Publication Year :
- 2017
- Publisher :
- Elsevier BV, 2017.
-
Abstract
- BrainComputer Interface (BCI) systems analyze brain signals to generate control commands for computer applications or external devices. Utilized as alternative communication channel, BCIs have the potential to assist people with severe motor disabilities to interact with their environment and to participate in daily life activities. Handicapped people from all age groups could benefit from such BCI technologies. Although some papers have previously reported slightly worse BCI performance by older subjects, in many studies BCI systems were tested with young subjects only.In the presented paper age-associated differences in BCI performance were investigated. We compared accuracy and speed of a steady-state visual evoked potential (SSVEP)-based BCI spelling application controlled by participants of two different equally sized age groups. Twenty subjects (eleven female and nine male) participated in this study; each age group consisted of ten subjects, ranging from 19 to 27 years and from 64 to 76 years. Our results confirm that elderly people may have a deteriorated information transfer rate (ITR). The mean (SD) ITR of the young age group was 27.36 (6.50) bit/min while the elderly people achieved a significantly lower ITR of 16.10 (5.90) bit/min. The average time window length associated with the signal classification was usually larger for the participants of advanced age. These findings show that the subject age must be taken into account during the development of SSVEP-based applications.
- Subjects :
- medicine.medical_specialty
Computer science
Cognitive Neuroscience
Speech recognition
0206 medical engineering
02 engineering and technology
Audiology
020601 biomedical engineering
Computer Science Applications
03 medical and health sciences
0302 clinical medicine
Artificial Intelligence
Age related
medicine
Evoked potential
030217 neurology & neurosurgery
Brain–computer interface
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 250
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....f693dbccdda365612edfff10f02c8418