Kim, Keun-Tae Lee, Jaehyung Kim, Hyungmin Kim, Choong Hyun Lee, Song Joo 2024-01-19T18:00:44Z 2024-01-19T18:00:44Z 2021-09-05 2020-04 2169-3536 https://pubs.kist.re.kr/handle/201004/118801 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 0.35 +/- 0.17. Furthermore, based on theANOVAwith 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. English Institute of Electrical and Electronics Engineers Inc. Classification of Selective Attention Within Steady-State Somatosensory Evoked Potentials From Dry Electrodes Using Mutual Information-Based Spatio-Spectral Feature Selection Article 10.1109/ACCESS.2020.2990384 1 IEEE Access, v.8, pp.85464 - 85472 IEEE Access 8 85464 85472 Y scie scopus 000549852000001 2-s2.0-85085187398 Computer Science, Information Systems Engineering, Electrical & Electronic Telecommunications Computer Science Engineering Telecommunications Article BRAIN-COMPUTER INTERFACES COMMUNICATION SIGNALS BCI Steady-state somatosensory evoked potential selective attention brain-computer interface dry electrode