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In this paper, a novel classification method among running, walking and standing by combining common spatial patterns(CSP) and the fastICA feature extraction method together and constructing a Small-world Neural Network(SWNN, for short) through the gait feature extracted is proposed. We conducted our experiments on a treadmill at 0km/h, 1.6km/h and 3km/h speed separately. The EEG data is accompanied...
Contraposing mental task recognition, a novel Small-World neural network (SWNN) algorithm is proposed for the EEG classification tasks trying to solving the target hitting problem with the low spatial dispersion degree. A BMIs experimental paradigm with a series of Cue Scenes which focus on three-dimensional (3D) object control is described. Bring in the Small-World neural network, which is transformed...
Focusing on mental task recognition, a novel Small-World neural network(SWNN) algorithm is proposed for the EEG classification tasks aiming at solving small training sets problem. For making some attempts to discover the agile experimental paradigms, two channel sets having different information-carrying capacities are built to filter the multi-channel EEG data, as two channel-filters. The band-pass...
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