We present a methodology for a hybrid brain-computer interface (BCI) system, with the recognition of motor imagery (MI) based\non EEG and blink EOG signals. We tested the BCI system in a 3D Tetris and an analogous 2D game playing environment. To\nenhance player�s BCI control ability, the study focused on feature extraction from EEG and control strategy supporting Game-BCI\nsystem operation. We compared the numerical differences between spatial features extracted with common spatial pattern (CSP)\nand the proposed multifeature extraction. To demonstrate the effectiveness of 3D game environment at enhancing player�s eventrelated\ndesynchronization (ERD) and event-related synchronization (ERS) production ability, we set the 2D Screen Game as the\ncomparison experiment. According to a series of statistical results, the group performingMI in the 3D Tetris environment showed\nmore significant improvements in generating MI-associated ERD/ERS. Analysis results of game-score indicated that the players�\nscores presented an obvious uptrend in 3D Tetris environment but did not show an obvious downward trend in 2D Screen Game.\nIt suggested that the immersive and rich-control environment for MI would improve the associated mental imagery and enhance\nMI-based BCI skills.
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