title: Learning a common dictionary for subject-transfer decoding with resting calibration.
abstract: Brain signals measured over a series of experiments have inherent variability because of different physical and mental conditions among multiple subjects and sessions. Such variability complicates the analysis of data from multiple subjects and sessions in a consistent way, and degrades the performance of subject-transfer decoding in a brain-machine interface (BMI). To accommodate the variability in brain signals, we propose 1) a method for extracting spatial bases (or a dictionary) shared by multiple subjects, by employing a signal-processing technique of dictionary learning modified to compensate for variations between subjects and sessions, and 2) an approach to subject-transfer decoding that uses the resting-state activity of a previously unseen target subject as calibration data for compensating for variations, eliminating the need for a standard calibration based on task sessions. Applying our methodology to a dataset of electroencephalography (EEG) recordings during a selective visual-spatial attention task from multiple subjects and sessions, where the variability compensation was essential for reducing the redundancy of the dictionary, we found that the extracted common brain activities were reasonable in the light of neuroscience knowledge. The applicability to subject-transfer decoding was confirmed by improved performance over existing decoding methods. These results suggest that analyzing multisubject brain activities on common bases by the proposed method enables information sharing across subjects with low-burden resting calibration, and is effective for practical use of BMI in variable environments.
authors: ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan; Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.
authors: Hiroshi
authors: H
authors: Morioka
authors: National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8568, Japan.
authors: Atsunori
authors: A
authors: Kanemura
authors: ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan.
authors: Jun-ichiro
authors: J
authors: Hirayama
authors: ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan.
authors: Manabu
authors: M
authors: Shikauchi
authors: ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan.
authors: Takeshi
authors: T
authors: Ogawa
authors: ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan.
authors: Shigeyuki
authors: S
authors: Ikeda
authors: ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan.
authors: Motoaki
authors: M
authors: Kawanabe
authors: ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan; Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan. Electronic address: ishii@i.kyoto-u.ac.jp.
authors: Shin
authors: S
authors: Ishii
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D062207:Brain-Computer Interfaces; D002138:Calibration; D004569:Electroencephalography / Q000379:methods*; D059907:Functional Neuroimaging / Q000379:methods*; D006801:Humans; D012815:Signal Processing, Computer-Assisted
keywords: Brain–machine interface (BMI); Dictionary learning and sparse coding; Electroencephalography (EEG); Multi-subject–session analysis; Spatial attention; Subject-transfer decoding