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4,028 results
  • P300 Speller Performance Predictor Based on RSVP Multi-feature. [Journal Article]
    Front Hum Neurosci 2019; 13:261Won K, Kwon M, … Jun SC
  • Brain-computer interface (BCI) systems were developed so that people can control computers or machines through their brain activity without moving their limbs. The P300 speller is one of the BCI applications used most commonly, as is very simple and reliable and can achieve satisfactory performance. However, like other BCIs, the P300 speller still has room for improvements in terms of its practic…
  • EEG Signals Feature Extraction Based on DWT and EMD Combined with Approximate Entropy. [Journal Article]
    Brain Sci 2019; 9(8)Ji N, Ma L, … Zhang X
  • The classification recognition rate of motor imagery is a key factor to improve the performance of brain-computer interface (BCI). Thus, we propose a feature extraction method based on discrete wavelet transform (DWT), empirical mode decomposition (EMD), and approximate entropy. Firstly, the electroencephalogram (EEG) signal is decomposed into a series of narrow band signals with DWT, then the su…
  • Analysis of Prefrontal Single-Channel EEG Data for Portable Auditory ERP-Based Brain-Computer Interfaces. [Journal Article]
    Front Hum Neurosci 2019; 13:250Ogino M, Kanoga S, … Mitsukura Y
  • An electroencephalogram (EEG)-based brain-computer interface (BCI) is a tool to non-invasively control computers by translating the electrical activity of the brain. This technology has the potential to provide patients who have severe generalized myopathy, such as those suffering from amyotrophic lateral sclerosis (ALS), with the ability to communicate. Recently, auditory oddball paradigms have …
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