title: Optimum spatio-spectral filtering network for brain-computer interface.
abstract: This paper proposes a feature extraction method for motor imagery brain-computer interface (BCI) using electroencephalogram. We consider the primary neurophysiologic phenomenon of motor imagery, termed event-related desynchronization, and formulate the learning task for feature extraction as maximizing the mutual information between the spatio-spectral filtering parameters and the class labels. After introducing a nonparametric estimate of mutual information, a gradient-based learning algorithm is devised to efficiently optimize the spatial filters in conjunction with a band-pass filter. The proposed method is compared with two existing methods on real data: a BCI Competition IV dataset as well as our data collected from seven human subjects. The results indicate the superior performance of the method for motor imagery classification, as it produced higher classification accuracy with statistical significance ( ≥ 95% confidence level) in most cases.
authors.affiliation: Institute for Infocomm Research, Agency for Science, Technology and Research, 138632, Singapore. hhzhang@i2r.a-star.edu.sg
authors.forename: Haihong
authors.initials: H
authors.lastname: Zhang
authors.forename: Zheng Yang
authors.initials: ZY
authors.lastname: Chin
authors.forename: Kai Keng
authors.initials: KK
authors.lastname: Ang
authors.forename: Cuntai
authors.initials: C
authors.lastname: Guan
authors.forename: Chuanchu
authors.initials: C
authors.lastname: Wang
mesh_terms: D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D019054:Evoked Potentials, Motor / Q000502:physiology*; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000592:standards*; D014584:User-Computer Interface