Paper Info
Title
Automatic user customization for improving the performance of a self-paced brain interface system.
Abstract
Customizing the parameter values of brain interface (BI) systems by a human expert has the advantage of being fast and computationally efficient. However, as the number of users and EEG channels grows, this process becomes increasingly time consuming and exhausting. Manual customization also introduces inaccuracies in the estimation of the parameter values. In this paper, the performance of a self-paced BI system whose design parameter values were automatically user customized using a genetic algorithm (GA) is studied. The GA automatically estimates the shapes of movement-related potentials (MRPs), whose features are then extracted to drive the BI. Offline analysis of the data of eight subjects revealed that automatic user customization improved the true positive (TP) rate of the system by an average of 6.68% over that whose customization was carried out by a human expert, i.e., by visually inspecting the MRP templates. On average, the best improvement in the TP rate (an average of 9.82%) was achieved for four individuals with spinal cord injury. In this case, the visual estimation of the parameter values of the MRP templates was very difficult because of the highly noisy nature of the EEG signals. For four able-bodied subjects, for which the MRP templates were less noisy, the automatic user customization led to an average improvement of 3.58% in the TP rate. The results also show that the inter-subject variability of the TP rate is also reduced compared to the case when user customization is carried out by a human expert. These findings provide some primary evidence that automatic user customization leads to beneficial results in the design of a self-paced BI for individuals with spinal cord injury.
Year
DOI
Venue
2006
10.1007/s11517-006-0125-2
Med. Biol. Engineering and Computing
Keywords
Field
DocType
visual inspection,genetic algorithm,genetics
Visual estimation,Computer vision,Pattern recognition,Genetic algorithm optimization,Communication channel,Offline analysis,Artificial intelligence,Genetic algorithm,Machine learning,Mathematics,Personalization
Journal
Volume
Issue
ISSN
44
12
0140-0118
Citations 
PageRank 
References 
2
0.43
11
Authors
4
Name
Order
Citations
PageRank
Mehrdad Fatourechi116911.96
Ali Bashashati2515.17
Gary E. Birch38211.36
Rabab K Ward41440135.88