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This paper presents a hybrid collaborative brain-computer interface (cBCI) to improve group-based recognition of target faces in crowded scenes recorded from surveillance cameras. The cBCI uses a combination of neural features extracted from EEG and response times to estimate the decision confidence of the users. Group decisions are then obtained by weighing individual responses according to these...
Efficient and accurate classification of event related potentials is a core task in brain-computer interfaces (BCI). This is normally obtained by first extracting features from the voltage amplitudes recorded via EEG at different channels and then feeding them into a classifier. In this paper we evaluate the relative benefits of using the first order temporal derivatives of the EEG signals, not the...
In this paper we use genetic programming — an evolutionary program-induction technology — to evolve algorithms that accurately approximate the behaviour of two standard detectors of ocular movement based on Electro-oculogram (EOG). The prediction is based entirely on EEG signals, i.e., without using EOG, making it possible to detect eye movements even in data recorded without EOG or eye tracking....
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