7月4日,古城苏州,明基中国营销总部充满品味的星巴克区,一场Q-desk媒体发布会向外界宣布了半年来新品牌经营的成果以及下半年品牌经营的策略。 Q-desk是BenQ on desk的缩写,Q还代表Quality(生活品质)和Cute(快乐)的意思,是明基新品牌享...7月4日,古城苏州,明基中国营销总部充满品味的星巴克区,一场Q-desk媒体发布会向外界宣布了半年来新品牌经营的成果以及下半年品牌经营的策略。 Q-desk是BenQ on desk的缩写,Q还代表Quality(生活品质)和Cute(快乐)的意思,是明基新品牌享受快乐科技内涵的具体体现。展开更多
In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in...In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.展开更多
文摘In electroencephalogram (EEG) modeling techniques, data segment selection is the first and still an important step. The influence of a set of data-segment-related parameters on feature extraction and classification in an EEG-based brain-computer interface (BCI) was studied. An auto search algorithm was developed to study four datasegment-related parameters in each trial of 12 subjects’ EEG. The length of data segment (LDS), the start position of data (SPD) segment, AR order, and number of trials (NT) were used to build the model. The study showed that, compared with the classification ratio (CR) without parameter selection, the CR was increased by 20% to 30% with proper selection of these data-segment-related parameters, and the optimum parameter values were subject-dependent. This suggests that the data-segment-related parameters should be individualized when building models for BCI.
