An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm
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An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm. / Costa, Ana P.; Møller, Jakob S.; Iversen, Helle K.; Puthusserypady, Sadasivan.
In: Computers in Biology and Medicine, Vol. 103, 2018, p. 24-33.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - An adaptive CSP filter to investigate user independence in a 3-class MI-BCI paradigm
AU - Costa, Ana P.
AU - Møller, Jakob S.
AU - Iversen, Helle K.
AU - Puthusserypady, Sadasivan
PY - 2018
Y1 - 2018
N2 - This paper describes the implementation of a Brain Computer Interface (BCI) scheme using a common spatial patterns (CSP) filter in combination with a Recursive Least Squares (RLS) approach to iteratively update the coefficients of the CSP filter. The proposed adaptive CSP (ACSP) algorithm is made more robust by introducing regularization using Diagonal Loading (DL), and thus will be able to significantly reduce the length of training sessions when introducing new patients to the BCI system. The system is tested on a 4-class multi-limb motor imagery (MI) data set from the BCI competition IV (2a), and a more complex single limb 3-class MI dataset recorded in-house. The latter dataset is produced to mimic an upper limb rehabilitation session, e.g., after stroke. The findings indicate that when extensive calibration data is available, the ACSP performs comparably to the CSP (kappa value of 0.523 and 0.502, respectively, for the 4-class problem); for reduced calibration sessions, the ACSP significantly improved the performance of the system (up to 4-fold). The proposed paradigm proved feasible and the ACSP algorithm seems to enable a user or semi user independent scenario, where the need for long system calibration sessions without feedback is eliminated.
AB - This paper describes the implementation of a Brain Computer Interface (BCI) scheme using a common spatial patterns (CSP) filter in combination with a Recursive Least Squares (RLS) approach to iteratively update the coefficients of the CSP filter. The proposed adaptive CSP (ACSP) algorithm is made more robust by introducing regularization using Diagonal Loading (DL), and thus will be able to significantly reduce the length of training sessions when introducing new patients to the BCI system. The system is tested on a 4-class multi-limb motor imagery (MI) data set from the BCI competition IV (2a), and a more complex single limb 3-class MI dataset recorded in-house. The latter dataset is produced to mimic an upper limb rehabilitation session, e.g., after stroke. The findings indicate that when extensive calibration data is available, the ACSP performs comparably to the CSP (kappa value of 0.523 and 0.502, respectively, for the 4-class problem); for reduced calibration sessions, the ACSP significantly improved the performance of the system (up to 4-fold). The proposed paradigm proved feasible and the ACSP algorithm seems to enable a user or semi user independent scenario, where the need for long system calibration sessions without feedback is eliminated.
KW - Brain-computer interface (BCI)
KW - Common spatial patterns (CSP)
KW - Diagonal loading (DL) CSP (DLCSP)
KW - Electroencephalography(EEG)
KW - Motor imagery (MI)
KW - Recursive least squares (RLS)
KW - Stroke rehabilitation
U2 - 10.1016/j.compbiomed.2018.09.021
DO - 10.1016/j.compbiomed.2018.09.021
M3 - Journal article
AN - SCOPUS:85054756315
VL - 103
SP - 24
EP - 33
JO - Computers in Biology and Medicine
JF - Computers in Biology and Medicine
SN - 0010-4825
ER -
ID: 217390877