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P300, which is usually evoked by visual stimulus, is widely used in electroencephalography (EEG) based brain computer interface (BCI) studies. As an application-oriented BCI study, the P300 speller would inevitably be used in outdoor environments. However, the visual stimulation effect might be interfered by the reflections in outdoor environment. This paper attempted to improve the outdoor P300 speller...
Classification of EEG under wearable environment faces many challenges including motion artifact, electrode DC offset, noise and limited available energy source. This paper describes the design consideration of a multi-channel machine-learning based EEG classification and recording processors for wearable form-factor sensors. The goal is to optimize the detection performance while balancing the analog...
The main principle behind EEG-based brain computer interfaces (BCI) is the recording and accurate classification of EEG signals during imagination of different types of motor movements. The changes in the neural activity effected by motor imagery are a lot similar to those induced by actual movement. Common features, e.g., band power values, present in the single EEG trials are extracted by suitable...
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