Evolutionary optimization of classifiers and features for single-trial EEG Discrimination |
| |
Authors: | Malin CB Åberg Johan Wessberg |
| |
Institution: | 1.Department of Neuroscience and Physiology,G?teborg University,G?teborg,Sweden |
| |
Abstract: | Background State-of-the-art signal processing methods are known to detect information in single-trial event-related EEG data, a crucial
aspect in development of real-time applications such as brain computer interfaces. This paper investigates one such novel
approach, evaluating how individual classifier and feature subset tailoring affects classification of single-trial EEG finger
movements. The discrete wavelet transform was used to extract signal features that were classified using linear regression
and non-linear neural network models, which were trained and architecturally optimized with evolutionary algorithms. The input
feature subsets were also allowed to evolve, thus performing feature selection in a wrapper fashion. Filter approaches were
implemented as well by limiting the degree of optimization. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|