Classification of Selective Attention within Steady-State Somatosensory Evoked Potentials from Dry Electrodes using Mutual Information-based Spatio-Spectral Feature Selection

Title
Classification of Selective Attention within Steady-State Somatosensory Evoked Potentials from Dry Electrodes using Mutual Information-based Spatio-Spectral Feature Selection
Authors
김충현김형민이송주김근태이재형
Keywords
Steady-State Somatosensory Evoked Potential; Selective Attention; Brain-Computer Interface; Dry Electrode
Issue Date
2020-04
Publisher
IEEE Access
Citation
VOL 8-85472
Abstract
Nowadays, the steady-state somatosensory evoked potential (SSSEP)-based brain-computer interfaces (BCIs) has been developed for improving the quality of daily life for people with physical disabilities. However, due to its poor performance of recognizing selective attention tasks and inattention(rest)-state, the SSSEP-based BCI has not been widely used for practical interfaces. In this paper, we propose a mutual information-based spatio-spectral feature selection method for recognizing selective attention tasks and inattention(rest)-state using dry electrodes considering a real-life application, when vibration stimuli were applied to both index fingers. In our methods, the filter-bank common spatial pattern (FBCSP) was used for extracting spatio-spectral features of the SSSEP. Then, discriminative features were selected using a mutual information-based best individual feature (MIBIF) algorithm. The regularized linear discriminant analysis (RLDA) used as the classifier. The feasibility of the proposed method was demonstrated through eight healthy subjects using the vibration stimuli induced SSSEP with spatially clear and distinguishable patterns for SSSEP-based BCI. From our study, the proposed method showed the best classification accuracy with a kappa value of 0.350.17. Furthermore, based on the ANOVA with posthoc tests, the proposed method showed significantly higher accuracy as 57.9% in decoding three classes (p-value < 0.01) compared to the fast Fourier transform (FFT) and common spatial pattern (CSP)-based previous feature extraction methods. Consequently, the proposed FBCSP and MIBIF-based methods and findings can further help to improve decoding performance and develop the SSSEP-based BCI systems for real-world applications.
URI
http://pubs.kist.re.kr/handle/201004/71356
ISSN
2169-3536
Appears in Collections:
KIST Publication > Article
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE