Polich 2016 updating p300

A high efficient time-shift correlation algorithm was proposed to deal with the peak time uncertainty of P300 evoked potential for a P300-based brain-computer interface (BCI).The time-shift correlation series data were collected as the input nodes of an artificial neural network (ANN), and the classification of four LED visual stimuli was selected as the output node.However, EEG is currently the most common method to obtain brain activity.It has the advantages of low cost, acceptable temporal resolution, high mobility, and higher acceptance by subjects.

polich 2016 updating p300-86polich 2016 updating p300-87

used an autonomous navigation system to drive a wheelchair in an indoor environment [4].However, noisy signals and low signal amplitude and spatial resolution present challenges in signal processing.Compared to noninvasive approaches, invasive methods require the insertion of microelectrode arrays into subjects’ skulls.Steady-state visually evoked potential (SSVEP) was elicited through a number of flickering stimuli.According to the dominant response frequency of EEG, the humanoid robot was capable of performing defined tasks.

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